[ { "canonical_title": "Deep Residual Learning for Image Recognition", "components": [ { "confidence": "high", "contribution": 0.2, "metric": "citation_count", "normalized": 1.0, "provenance_url": "https://openalex.org/W2194775991", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 222154.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2194775991", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 1, "scenario": "broad_influence", "score": 0.4, "work_id": "paper-0079", "work_type": "paper" }, { "canonical_title": "Random Forests", "components": [ { "confidence": "high", "contribution": 0.1137, "metric": "citation_count", "normalized": 0.568502, "provenance_url": "https://openalex.org/W2911964244", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 126295.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2911964244", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 2, "scenario": "broad_influence", "score": 0.3137, "work_id": "paper-0043", "work_type": "paper" }, { "canonical_title": "Long Short-Term Memory", "components": [ { "confidence": "high", "contribution": 0.088368, "metric": "citation_count", "normalized": 0.441838, "provenance_url": "https://openalex.org/W2064675550", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 98156.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2064675550", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 3, "scenario": "broad_influence", "score": 0.288368, "work_id": "paper-0038", "work_type": "paper" }, { "canonical_title": "Fuzzy Sets", "components": [ { "confidence": "high", "contribution": 0.059555, "metric": "citation_count", "normalized": 0.297775, "provenance_url": "https://openalex.org/W4211007335", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 66152.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W4211007335", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 4, "scenario": "broad_influence", "score": 0.259555, "work_id": "paper-0012", "work_type": "paper" }, { "canonical_title": "Gradient-Based Learning Applied to Document Recognition", "components": [ { "confidence": "high", "contribution": 0.052441, "metric": "citation_count", "normalized": 0.262205, "provenance_url": "https://openalex.org/W2112796928", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 58250.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2112796928", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 5, "scenario": "broad_influence", "score": 0.252441, "work_id": "paper-0041", "work_type": "paper" }, { "canonical_title": "Adam: A Method for Stochastic Optimization", "components": [ { "confidence": "medium", "contribution": 0.076329, "metric": "citation_count", "normalized": 0.381645, "provenance_url": "https://openalex.org/W1522301498", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 84784.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "low", "contribution": 0.371429, "metric": "readership_persistence", "normalized": 0.928571, "provenance_url": "https://openalex.org/W1522301498", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 14.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 6, "scenario": "broad_influence", "score": 0.247758, "work_id": "paper-0070", "work_type": "paper" }, { "canonical_title": "Regression Shrinkage and Selection via the Lasso", "components": [ { "confidence": "high", "contribution": 0.046645, "metric": "citation_count", "normalized": 0.233226, "provenance_url": "https://openalex.org/W2135046866", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 51812.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2135046866", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 7, "scenario": "broad_influence", "score": 0.246645, "work_id": "paper-0037", "work_type": "paper" }, { "canonical_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting", "components": [ { "confidence": "high", "contribution": 0.030861, "metric": "citation_count", "normalized": 0.154303, "provenance_url": "https://openalex.org/W2095705004", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 34279.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2095705004", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 8, "scenario": "broad_influence", "score": 0.230861, "work_id": "paper-0061", "work_type": "paper" }, { "canonical_title": "GloVe: Global Vectors for Word Representation", "components": [ { "confidence": "high", "contribution": 0.030406, "metric": "citation_count", "normalized": 0.15203, "provenance_url": "https://openalex.org/W2250539671", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 33774.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2250539671", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 9, "scenario": "broad_influence", "score": 0.230406, "work_id": "paper-0065", "work_type": "paper" }, { "canonical_title": "Support-Vector Networks", "components": [ { "confidence": "high", "contribution": 0.02976, "metric": "citation_count", "normalized": 0.148798, "provenance_url": "https://openalex.org/W2119821739", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 33056.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2119821739", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 10, "scenario": "broad_influence", "score": 0.22976, "work_id": "paper-0035", "work_type": "paper" }, { "canonical_title": "Learning Representations by Back-Propagating Errors", "components": [ { "confidence": "high", "contribution": 0.027826, "metric": "citation_count", "normalized": 0.139129, "provenance_url": "https://openalex.org/W1498436455", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 30908.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W1498436455", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 11, "scenario": "broad_influence", "score": 0.227826, "work_id": "paper-0024", "work_type": "paper" }, { "canonical_title": "Greedy Function Approximation: A Gradient Boosting Machine", "components": [ { "confidence": "medium", "contribution": 0.026108, "metric": "citation_count", "normalized": 0.13054, "provenance_url": "https://openalex.org/W1678356000", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 29000.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "low", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W1678356000", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 12, "scenario": "broad_influence", "score": 0.226108, "work_id": "paper-0045", "work_type": "paper" }, { "canonical_title": "Latent Dirichlet Allocation", "components": [ { "confidence": "high", "contribution": 0.024353, "metric": "citation_count", "normalized": 0.121767, "provenance_url": "https://openalex.org/W1880262756", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 27051.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W1880262756", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 13, "scenario": "broad_influence", "score": 0.224353, "work_id": "paper-0047", "work_type": "paper" }, { "canonical_title": "Reducing the Dimensionality of Data with Neural Networks", "components": [ { "confidence": "high", "contribution": 0.018875, "metric": "citation_count", "normalized": 0.094376, "provenance_url": "https://openalex.org/W2100495367", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 20966.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2100495367", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 14, "scenario": "broad_influence", "score": 0.218875, "work_id": "paper-0050", "work_type": "paper" }, { "canonical_title": "A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting", "components": [ { "confidence": "high", "contribution": 0.0183, "metric": "citation_count", "normalized": 0.0915, "provenance_url": "https://openalex.org/W1988790447", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 20327.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W1988790447", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 15, "scenario": "broad_influence", "score": 0.2183, "work_id": "paper-0039", "work_type": "paper" }, { "canonical_title": "A Logical Calculus of the Ideas Immanent in Nervous Activity", "components": [ { "confidence": "high", "contribution": 0.016203, "metric": "citation_count", "normalized": 0.081016, "provenance_url": "https://openalex.org/W1995341919", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 17998.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W1995341919", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 16, "scenario": "broad_influence", "score": 0.216203, "work_id": "paper-0001", "work_type": "paper" }, { "canonical_title": "A Fast Learning Algorithm for Deep Belief Nets", "components": [ { "confidence": "high", "contribution": 0.014771, "metric": "citation_count", "normalized": 0.073854, "provenance_url": "https://openalex.org/W2136922672", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 16407.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2136922672", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 17, "scenario": "broad_influence", "score": 0.214771, "work_id": "paper-0049", "work_type": "paper" }, { "canonical_title": "Auto-Encoding Variational Bayes", "components": [ { "confidence": "medium", "contribution": 0.01407, "metric": "citation_count", "normalized": 0.070348, "provenance_url": "https://openalex.org/W1959608418", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15628.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "low", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W1959608418", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 18, "scenario": "broad_influence", "score": 0.21407, "work_id": "paper-0063", "work_type": "paper" }, { "canonical_title": "Going Deeper with Convolutions", "components": [ { "confidence": "high", "contribution": 0.042164, "metric": "citation_count", "normalized": 0.210822, "provenance_url": "https://openalex.org/W2097117768", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 46835.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.371429, "metric": "readership_persistence", "normalized": 0.928571, "provenance_url": "https://openalex.org/W2097117768", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 14.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 19, "scenario": "broad_influence", "score": 0.213593, "work_id": "paper-0068", "work_type": "paper" }, { "canonical_title": "No Free Lunch Theorems for Optimization", "components": [ { "confidence": "high", "contribution": 0.01255, "metric": "citation_count", "normalized": 0.062749, "provenance_url": "https://openalex.org/W2151554678", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 13940.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2151554678", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 20, "scenario": "broad_influence", "score": 0.21255, "work_id": "paper-0040", "work_type": "paper" }, { "canonical_title": "The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain", "components": [ { "confidence": "medium", "contribution": 0.01059, "metric": "citation_count", "normalized": 0.05295, "provenance_url": "https://openalex.org/W2040870580", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 11763.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "low", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2040870580", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 21, "scenario": "broad_influence", "score": 0.21059, "work_id": "paper-0006", "work_type": "paper" }, { "canonical_title": "Matrix Factorization Techniques for Recommender Systems", "components": [ { "confidence": "high", "contribution": 0.010518, "metric": "citation_count", "normalized": 0.05259, "provenance_url": "https://openalex.org/W2054141820", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 11683.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2054141820", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 22, "scenario": "broad_influence", "score": 0.210518, "work_id": "paper-0053", "work_type": "paper" }, { "canonical_title": "Self-Organized Formation of Topologically Correct Feature Maps", "components": [ { "confidence": "high", "contribution": 0.008571, "metric": "citation_count", "normalized": 0.042853, "provenance_url": "https://openalex.org/W65738273", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 9520.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W65738273", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 23, "scenario": "broad_influence", "score": 0.208571, "work_id": "paper-0021", "work_type": "paper" }, { "canonical_title": "Multilayer Feedforward Networks Are Universal Approximators", "components": [ { "confidence": "high", "contribution": 0.008428, "metric": "citation_count", "normalized": 0.042142, "provenance_url": "https://openalex.org/W3146803896", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 9362.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W3146803896", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 24, "scenario": "broad_influence", "score": 0.208428, "work_id": "paper-0027", "work_type": "paper" }, { "canonical_title": "Q-Learning", "components": [ { "confidence": "high", "contribution": 0.008078, "metric": "citation_count", "normalized": 0.040391, "provenance_url": "https://openalex.org/W32403112", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 8973.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W32403112", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 25, "scenario": "broad_influence", "score": 0.208078, "work_id": "paper-0032", "work_type": "paper" }, { "canonical_title": "A Robust Layered Control System for a Mobile Robot", "components": [ { "confidence": "high", "contribution": 0.006978, "metric": "citation_count", "normalized": 0.03489, "provenance_url": "https://openalex.org/W2097856935", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 7751.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2097856935", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 26, "scenario": "broad_influence", "score": 0.206978, "work_id": "paper-0023", "work_type": "paper" }, { "canonical_title": "Minds, Brains, and Programs", "components": [ { "confidence": "high", "contribution": 0.005839, "metric": "citation_count", "normalized": 0.029196, "provenance_url": "https://openalex.org/W2251410821", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 6486.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2251410821", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 27, "scenario": "broad_influence", "score": 0.205839, "work_id": "paper-0018", "work_type": "paper" }, { "canonical_title": "Intelligence Without Representation", "components": [ { "confidence": "high", "contribution": 0.00424, "metric": "citation_count", "normalized": 0.021202, "provenance_url": "https://openalex.org/W2397253692", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 4710.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2397253692", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 28, "scenario": "broad_influence", "score": 0.20424, "work_id": "paper-0031", "work_type": "paper" }, { "canonical_title": "Some Studies in Machine Learning Using the Game of Checkers", "components": [ { "confidence": "high", "contribution": 0.003928, "metric": "citation_count", "normalized": 0.01964, "provenance_url": "https://openalex.org/W3198350258", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 4363.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W3198350258", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 29, "scenario": "broad_influence", "score": 0.203928, "work_id": "paper-0007", "work_type": "paper" }, { "canonical_title": "Fairness Through Awareness", "components": [ { "confidence": "high", "contribution": 0.003042, "metric": "citation_count", "normalized": 0.01521, "provenance_url": "https://openalex.org/W2100960835", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 3379.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2100960835", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 30, "scenario": "broad_influence", "score": 0.203042, "work_id": "paper-0058", "work_type": "paper" }, { "canonical_title": "Some Philosophical Problems from the Standpoint of Artificial Intelligence", "components": [ { "confidence": "medium", "contribution": 0.002975, "metric": "citation_count", "normalized": 0.014877, "provenance_url": "https://openalex.org/W2138162238", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 3305.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "low", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2138162238", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 31, "scenario": "broad_influence", "score": 0.202975, "work_id": "paper-0014", "work_type": "paper" }, { "canonical_title": "A Few Useful Things to Know About Machine Learning", "components": [ { "confidence": "high", "contribution": 0.002926, "metric": "citation_count", "normalized": 0.014629, "provenance_url": "https://openalex.org/W2161336914", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 3250.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2161336914", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 32, "scenario": "broad_influence", "score": 0.202926, "work_id": "paper-0059", "work_type": "paper" }, { "canonical_title": "ImageNet: A Large-Scale Hierarchical Image Database", "components": [ { "confidence": "high", "contribution": 0.002651, "metric": "citation_count", "normalized": 0.013257, "provenance_url": "https://openalex.org/W4239072543", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 2945.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W4239072543", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 33, "scenario": "broad_influence", "score": 0.202651, "work_id": "paper-0054", "work_type": "paper" }, { "canonical_title": "Finding Structure in Time", "components": [ { "confidence": "high", "contribution": 0.002637, "metric": "citation_count", "normalized": 0.013185, "provenance_url": "https://openalex.org/W4254816979", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 2929.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W4254816979", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 34, "scenario": "broad_influence", "score": 0.202637, "work_id": "paper-0030", "work_type": "paper" }, { "canonical_title": "Learning to Predict by the Methods of Temporal Differences", "components": [ { "confidence": "high", "contribution": 0.002517, "metric": "citation_count", "normalized": 0.012586, "provenance_url": "https://openalex.org/W3041202696", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 2796.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W3041202696", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 35, "scenario": "broad_influence", "score": 0.202517, "work_id": "paper-0026", "work_type": "paper" }, { "canonical_title": "A Neural Probabilistic Language Model", "components": [ { "confidence": "high", "contribution": 0.002408, "metric": "citation_count", "normalized": 0.012041, "provenance_url": "https://openalex.org/W2998704965", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 2675.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2998704965", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 36, "scenario": "broad_influence", "score": 0.202408, "work_id": "paper-0048", "work_type": "paper" }, { "canonical_title": "The Symbol Grounding Problem", "components": [ { "confidence": "high", "contribution": 0.002304, "metric": "citation_count", "normalized": 0.011519, "provenance_url": "https://openalex.org/W2107019937", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 2559.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2107019937", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 37, "scenario": "broad_influence", "score": 0.202304, "work_id": "paper-0029", "work_type": "paper" }, { "canonical_title": "Fusion, Propagation, and Structuring in Belief Networks", "components": [ { "confidence": "high", "contribution": 0.001937, "metric": "citation_count", "normalized": 0.009687, "provenance_url": "https://openalex.org/W2143075689", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 2152.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2143075689", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 38, "scenario": "broad_influence", "score": 0.201937, "work_id": "paper-0025", "work_type": "paper" }, { "canonical_title": "Deep Neural Networks for Acoustic Modeling in Speech Recognition", "components": [ { "confidence": "high", "contribution": 0.001714, "metric": "citation_count", "normalized": 0.008571, "provenance_url": "https://openalex.org/W2184045248", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 1904.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2184045248", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 39, "scenario": "broad_influence", "score": 0.201714, "work_id": "paper-0057", "work_type": "paper" }, { "canonical_title": "Man-Computer Symbiosis", "components": [ { "confidence": "high", "contribution": 0.001616, "metric": "citation_count", "normalized": 0.00808, "provenance_url": "https://openalex.org/W2141373701", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 1795.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2141373701", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 40, "scenario": "broad_influence", "score": 0.201616, "work_id": "paper-0009", "work_type": "paper" }, { "canonical_title": "The Unreasonable Effectiveness of Data", "components": [ { "confidence": "high", "contribution": 0.001613, "metric": "citation_count", "normalized": 0.008066, "provenance_url": "https://openalex.org/W2103018059", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 1792.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2103018059", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 41, "scenario": "broad_influence", "score": 0.201613, "work_id": "paper-0055", "work_type": "paper" }, { "canonical_title": "Computing Machinery and Intelligence", "components": [ { "confidence": "medium", "contribution": 0.001216, "metric": "citation_count", "normalized": 0.006081, "provenance_url": "https://openalex.org/W4307979480", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 1351.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "low", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W4307979480", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 42, "scenario": "broad_influence", "score": 0.201216, "work_id": "paper-0004", "work_type": "paper" }, { "canonical_title": "Statistical Modeling: The Two Cultures", "components": [ { "confidence": "high", "contribution": 0.001211, "metric": "citation_count", "normalized": 0.006054, "provenance_url": "https://openalex.org/W2160455305", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 1345.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W2160455305", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 43, "scenario": "broad_influence", "score": 0.201211, "work_id": "paper-0044", "work_type": "paper" }, { "canonical_title": "GPS: A Program That Simulates Human Thought", "components": [ { "confidence": "medium", "contribution": 0.000739, "metric": "citation_count", "normalized": 0.003696, "provenance_url": "https://openalex.org/W1923807516", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 821.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "low", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W1923807516", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 44, "scenario": "broad_influence", "score": 0.200739, "work_id": "paper-0011", "work_type": "paper" }, { "canonical_title": "A Learning Algorithm for Boltzmann Machines", "components": [ { "confidence": "high", "contribution": 0.000669, "metric": "citation_count", "normalized": 0.003345, "provenance_url": "https://openalex.org/W4255825300", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 743.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.4, "metric": "readership_persistence", "normalized": 1.0, "provenance_url": "https://openalex.org/W4255825300", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 45, "scenario": "broad_influence", "score": 0.200669, "work_id": "paper-0022", "work_type": "paper" }, { "canonical_title": "Human-Level Control Through Deep Reinforcement Learning", "components": [ { "confidence": "high", "contribution": 0.027005, "metric": "citation_count", "normalized": 0.135023, "provenance_url": "https://openalex.org/W2145339207", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 29996.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.371429, "metric": "readership_persistence", "normalized": 0.928571, "provenance_url": "https://openalex.org/W2145339207", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 14.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 46, "scenario": "broad_influence", "score": 0.198434, "work_id": "paper-0076", "work_type": "paper" }, { "canonical_title": "Mastering the Game of Go with Deep Neural Networks and Tree Search", "components": [ { "confidence": "high", "contribution": 0.014256, "metric": "citation_count", "normalized": 0.071279, "provenance_url": "https://openalex.org/W2257979135", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 15835.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.371429, "metric": "readership_persistence", "normalized": 0.928571, "provenance_url": "https://openalex.org/W2257979135", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 14.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 47, "scenario": "broad_influence", "score": 0.185685, "work_id": "paper-0082", "work_type": "paper" }, { "canonical_title": "Neural Machine Translation by Jointly Learning to Align and Translate", "components": [ { "confidence": "medium", "contribution": 0.013162, "metric": "citation_count", "normalized": 0.06581, "provenance_url": "https://openalex.org/W2133564696", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 14620.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "low", "contribution": 0.371429, "metric": "readership_persistence", "normalized": 0.928571, "provenance_url": "https://openalex.org/W2133564696", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 14.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 48, "scenario": "broad_influence", "score": 0.184591, "work_id": "paper-0075", "work_type": "paper" }, { "canonical_title": "The PageRank Citation Ranking: Bringing Order to the Web", "components": [ { "confidence": "medium", "contribution": 0.011416, "metric": "citation_count", "normalized": 0.057078, "provenance_url": "https://openalex.org/W1854214752", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 12680.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "low", "contribution": 0.371429, "metric": "readership_persistence", "normalized": 0.928571, "provenance_url": "https://openalex.org/W1854214752", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 14.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 49, "scenario": "broad_influence", "score": 0.182845, "work_id": "paper-0042", "work_type": "paper" }, { "canonical_title": "A Mathematical Theory of Communication", "components": [ { "confidence": "high", "contribution": 0.00877, "metric": "citation_count", "normalized": 0.043852, "provenance_url": "https://openalex.org/W2041404167", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 9742.0, "weight": 0.2 }, { "metric": "library_holdings", "missing_data_penalty": 0.125, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.25 }, { "confidence": "medium", "contribution": 0.371429, "metric": "readership_persistence", "normalized": 0.928571, "provenance_url": "https://openalex.org/W2041404167", "retrieved_at": "2026-06-29", "source": "OpenAlex", "status": "present", "value": 14.0, "weight": 0.4 }, { "metric": "syllabus_adoptions", "missing_data_penalty": 0.075, "note": "recorded as missing; penalized by rule, never imputed", "status": "missing", "weight": 0.15 } ], "conflict_flag": false, "rank": 50, "scenario": "broad_influence", "score": 0.180199, "work_id": "paper-0003", "work_type": "paper" } ]