{"total":13,"rows":[{"id":"2202.00622","title":"Datamodels: Predicting Predictions from Training Data","created":"2022-02-01","cited_by_count":189,"area":null},{"id":"2108.10573","title":"The staircase property: How hierarchical structure can guide deep learning","created":"2021-08-24","cited_by_count":62,"area":null},{"id":"2104.08760","title":"Solving Inefficiency of Self-supervised Representation Learning","created":"2021-04-18","cited_by_count":41,"area":null},{"id":"2108.03489","title":"Impact of Aliasing on Generalization in Deep Convolutional Networks","created":"2021-08-07","cited_by_count":25,"area":null},{"id":"2108.00207","title":"The Separation Capacity of Random Neural Networks","created":"2021-07-31","cited_by_count":14,"area":null},{"id":"2310.13225","title":"Scalable Neural Network Kernels","created":"2023-10-20","cited_by_count":9,"area":null},{"id":"2104.05785","title":"A Recipe for Global Convergence Guarantee in Deep Neural Networks","created":"2021-04-12","cited_by_count":8,"area":null},{"id":"2202.06499","title":"Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations","created":"2022-02-14","cited_by_count":2,"area":null},{"id":"2601.22563","title":"EUGens: Efficient, Unified, and General Dense Layers","created":"2026-01-30","cited_by_count":2,"area":null},{"id":"2306.17501","title":"Efficient uniform approximation using Random Vector Functional Link networks","created":"2023-06-30","cited_by_count":1,"area":null},{"id":"2303.16372","title":"On the Query Complexity of Training Data Reconstruction in Private Learning","created":"2023-03-29","cited_by_count":0,"area":null},{"id":"2410.09771","title":"EUGens: Efficient, Unified, and General Dense Layers","created":"2024-10-13","cited_by_count":0,"area":null},{"id":"2512.09163","title":"WTNN: Weibull-Tailored Neural Networks for survival analysis","created":"2025-12-09","cited_by_count":0,"area":null}],"next_cursor":null}