neural scaling law

Scaling-Aware Data Selection for End-to-End Autonomous Driving Systems CVPR 2026 Analyzing Neural Scaling Laws in Two-Layer Networks with Power-Law Data Spectra ICLR 2025 A Solvable Attention for Neural Scaling Laws ICLR 2025 DRoP: Distributionally Robust Data Pruning ICLR 2025 How Feature Learning Can Improve Neural Scaling Laws ICLR 2025 Optimizing Data Collection for Machine Learning JMLR 2025 Scaling Collapse Reveals Universal Dynamics in Compute-Optimally Trained Neural Networks ICML 2025 The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws AISTATS 2025 Towards Neural Scaling Laws for Time Series Foundation Models ICLR 2025 4+3 Phases of Compute-Optimal Neural Scaling Laws NeurIPS 2024 LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law EMNLP 2024 Position: Graph Foundation Models Are Already Here ICML 2024 Data Efficient Neural Scaling Law via Model Reusing ICML 2023 Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources NeurIPS 2023 Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data NeurIPS 2023 Beyond neural scaling laws: beating power law scaling via data pruning NeurIPS 2022 How Much More Data Do I Need? Estimating Requirements for Downstream Tasks CVPR 2022 Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality ICLR 2022 Optimizing Data Collection for Machine Learning NeurIPS 2022 Revisiting Neural Scaling Laws in Language and Vision NeurIPS 2022