power law

A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules ICLR 2025 A New Formulation of Zipf’s Meaning-Frequency Law through Contextual Diversity ACL 2025 Investigating the Overlooked Hessian Structure: From CNNs to LLMs ICML 2025 (Mis)Fitting Scaling Laws: A Survey of Scaling Law Fitting Techniques in Deep Learning ICLR 2025 Pre-trained Models Perform the Best When Token Distributions Follow Zipf’s Law EMNLP 2025 Scaling Laws for Pre-training Agents and World Models ICML 2025 Zipf’s and Heaps’ Laws for Tokens and LLM-generated Texts EMNLP 2025 A Meta-Learning Approach to Predicting Performance and Data Requirements CVPR 2023 Scaling Laws for Hyperparameter Optimization NeurIPS 2023 The Quantization Model of Neural Scaling NeurIPS 2023 Data Scaling Laws in NMT: The Effect of Noise and Architecture ICML 2022 Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning ICML 2022 Learning Curves for Gaussian Process Regression with Power-Law Priors and Targets ICLR 2022 Ownership Concentration and Wealth Inequality in Russia IJCAI 2022 Scaling Laws for Neural Machine Translation ICLR 2022 Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments EMNLP 2022 Data and Parameter Scaling Laws for Neural Machine Translation EMNLP 2021 Explicit loss asymptotics in the gradient descent training of neural networks NeurIPS 2021 A Three-Parameter Rank-Frequency Relation in Natural Languages ACL 2020 On Power Laws in Deep Ensembles NeurIPS 2020 Random Clique Covers for Graphs with Local Density and Global Sparsity UAI 2019 Power-law efficient neural codes provide general link between perceptual bias and discriminability NeurIPS 2018 Taylor’s law for Human Linguistic Sequences ACL 2018 Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models NeurIPS 2016 Preferential Attachment in Graphs with Affinities AISTATS 2015