{"list":"other","total":53,"rows":[{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"2201.02177","title":"Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets","created":"2022-01-06","cited_by_count":629},{"id":"2207.04901","title":"Exploring Length Generalization in Large Language Models","created":"2022-07-11","cited_by_count":206},{"id":"2310.16028","title":"What Algorithms can Transformers Learn? A Study in Length Generalization","created":"2023-10-24","cited_by_count":199},{"id":"2207.08799","title":"Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit","created":"2022-07-18","cited_by_count":187},{"id":"2206.15144","title":"Neural Networks can Learn Representations with Gradient Descent","created":"2022-06-30","cited_by_count":161},{"id":"2302.11055","title":"SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics","created":"2023-02-21","cited_by_count":143},{"id":"2202.08658","title":"The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks","created":"2022-02-17","cited_by_count":142},{"id":"2003.10409","title":"Online stochastic gradient descent on non-convex losses from high-dimensional inference","created":"2020-03-23","cited_by_count":119},{"id":"2210.15651","title":"Learning Single-Index Models with Shallow Neural Networks","created":"2022-10-27","cited_by_count":92},{"id":"2002.07400","title":"Learning Parities with Neural Networks","created":"2020-02-18","cited_by_count":78},{"id":"2301.13105","title":"Generalization on the Unseen, Logic Reasoning and Degree Curriculum","created":"2023-01-30","cited_by_count":73},{"id":"2307.02129","title":"How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model","created":"2023-07-05","cited_by_count":71},{"id":"2305.18270","title":"How Two-Layer Neural Networks Learn, One (Giant) Step at a Time","created":"2023-05-29","cited_by_count":68},{"id":"2402.03220","title":"The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents","created":"2024-02-05","cited_by_count":52},{"id":"2406.01581","title":"Neural network learns low-dimensional polynomials with SGD near the information-theoretic limit","created":"2024-06-03","cited_by_count":46},{"id":"2103.01210","title":"Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels","created":"2021-03-01","cited_by_count":41},{"id":"2309.15111","title":"SGD Finds then Tunes Features in Two-Layer Neural Networks with near-Optimal Sample Complexity: A Case Study in the XOR problem","created":"2023-09-26","cited_by_count":39},{"id":"2406.00048","title":"Towards a theory of how the structure of language is acquired by deep neural networks","created":"2024-05-28","cited_by_count":39},{"id":"2405.15459","title":"Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions","created":"2024-05-24","cited_by_count":37},{"id":"1611.10258","title":"Reliably Learning the ReLU in Polynomial Time","created":"2016-11-30","cited_by_count":36},{"id":"2505.23683","title":"Learning Compositional Functions with Transformers from Easy-to-Hard Data","created":"2025-05-29","cited_by_count":36},{"id":"2309.03843","title":"Gradient-Based Feature Learning under Structured Data","created":"2023-09-07","cited_by_count":32},{"id":"2108.04190","title":"On the Power of Differentiable Learning versus PAC and SQ Learning","created":"2021-08-09","cited_by_count":28},{"id":"2202.05258","title":"Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks","created":"2022-02-10","cited_by_count":28},{"id":"2106.08068","title":"An Analytical Theory of Curriculum Learning in Teacher-Student Networks","created":"2021-06-15","cited_by_count":27},{"id":"1612.09057","title":"Deep Learning and Hierarchal Generative Models","created":"2016-12-29","cited_by_count":25},{"id":"2106.05418","title":"Probing transfer learning with a model of synthetic correlated datasets","created":"2021-06-09","cited_by_count":25},{"id":"2101.08303","title":"From Local Pseudorandom Generators to Hardness of Learning","created":"2021-01-20","cited_by_count":24},{"id":"2001.02992","title":"Poly-time universality and limitations of deep learning","created":"2020-01-07","cited_by_count":23},{"id":"2306.16921","title":"Provable Advantage of Curriculum Learning on Parity Targets with Mixed Inputs","created":"2023-06-29","cited_by_count":22},{"id":"1404.3378","title":"Complexity theoretic limitations on learning DNF's","created":"2014-04-13","cited_by_count":21},{"id":"2301.13833","title":"A Mathematical Model for Curriculum Learning for Parities","created":"2023-01-31","cited_by_count":19},{"id":"2208.03113","title":"On the non-universality of deep learning: quantifying the cost of symmetry","created":"2022-08-05","cited_by_count":18},{"id":"2205.13647","title":"Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures","created":"2022-05-26","cited_by_count":17},{"id":"2006.03177","title":"Hardness of Learning Neural Networks with Natural Weights","created":"2020-06-05","cited_by_count":15},{"id":"2407.05622","title":"On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries","created":"2024-07-08","cited_by_count":15},{"id":"1906.08654","title":"ID3 Learns Juntas for Smoothed Product Distributions","created":"2019-06-20","cited_by_count":13},{"id":"2302.07426","title":"Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy","created":"2023-02-15","cited_by_count":10},{"id":"2406.01589","title":"Tilting the Odds at the Lottery: the Interplay of Overparameterisation and Curricula in Neural Networks","created":"2024-06-03","cited_by_count":9},{"id":"2411.08798","title":"Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence","created":"2024-11-13","cited_by_count":9},{"id":"2506.05500","title":"The Generative Leap: Sharp Sample Complexity for Efficiently Learning Gaussian Multi-Index Models","created":"2025-06-05","cited_by_count":9},{"id":"2502.06443","title":"Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions","created":"2025-02-10","cited_by_count":8},{"id":"2412.04910","title":"Learning High-Degree Parities: The Crucial Role of the Initialization","created":"2024-12-06","cited_by_count":7},{"id":"2506.09887","title":"Learning single-index models via harmonic decomposition","created":"2025-06-11","cited_by_count":7},{"id":"1706.03935","title":"Exact Learning from an Honest Teacher That Answers Membership Queries","created":"2017-06-13","cited_by_count":6},{"id":"1706.06934","title":"Exact Learning of Juntas from Membership Queries","created":"2017-06-21","cited_by_count":6},{"id":"1301.0820","title":"Moment-Matching Polynomials","created":"2013-01-04","cited_by_count":5},{"id":"2510.25108","title":"Shift is Good: Mismatched Data Mixing Improves Test Performance","created":"2025-10-29","cited_by_count":5},{"id":"1901.07750","title":"Adaptive Exact Learning of Decision Trees from Membership Queries","created":"2019-01-23","cited_by_count":4},{"id":"2506.00764","title":"Learning Juntas under Markov Random Fields","created":"2025-06-01","cited_by_count":3},{"id":"2507.07907","title":"A statistical physics framework for optimal learning","created":"2025-07-10","cited_by_count":3},{"id":"2505.18839","title":"DNF Learning via Locally Mixing Random Walks","created":"2025-05-24","cited_by_count":2}],"next_cursor":null}