scaling law

Scaling Laws for Fine-Grained Mixture of Experts ICML 2024 Scaling laws for learning with real and surrogate data NeurIPS 2024 Scaling Laws for Linear Complexity Language Models EMNLP 2024 Scaling Laws for Sparsely-Connected Foundation Models ICLR 2024 Scaling Laws for the Value of Individual Data Points in Machine Learning ICML 2024 Scaling Laws in Linear Regression: Compute, Parameters, and Data NeurIPS 2024 Scaling Laws of Decoder-Only Models on the Multilingual Machine Translation Task EMNLP 2024 Scaling Laws of RoPE-based Extrapolation ICLR 2024 Scaling Laws of Synthetic Images for Model Training ... for Now CVPR 2024 Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies NeurIPS 2024 Scaling Parameter-Constrained Language Models with Quality Data EMNLP 2024 Scaling Retrieval-Based Language Models with a Trillion-Token Datastore NeurIPS 2024 Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices NeurIPS 2024 Selecting Large Language Model to Fine-tune via Rectified Scaling Law ICML 2024 Training Compute-Optimal Protein Language Models NeurIPS 2024 Understanding Scaling Laws with Statistical and Approximation Theory for Transformer Neural Networks on Intrinsically Low-dimensional Data NeurIPS 2024 Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction NeurIPS 2024 Wukong: Towards a Scaling Law for Large-Scale Recommendation ICML 2024 An Inverse Scaling Law for CLIP Training NeurIPS 2023 Are Emergent Abilities of Large Language Models a Mirage? NeurIPS 2023 Broken Neural Scaling Laws ICLR 2023 Cramming: Training a Language Model on a single GPU in one day. ICML 2023 Emergent and Predictable Memorization in Large Language Models NeurIPS 2023 Emergent Inabilities? Inverse Scaling Over the Course of Pretraining EMNLP 2023 Getting ViT in Shape: Scaling Laws for Compute-Optimal Model Design NeurIPS 2023