{"total":54,"rows":[{"id":"2210.17323","title":"GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers","created":"2022-10-31","cited_by_count":1919,"area":null},{"id":"2301.00774","title":"SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot","created":"2023-01-02","cited_by_count":1121,"area":null},{"id":"2208.11580","title":"Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning","created":"2022-08-24","cited_by_count":322,"area":null},{"id":"2203.07259","title":"The Optimal BERT Surgeon: Scalable and Accurate Second-Order Pruning for Large Language Models","created":"2022-03-14","cited_by_count":115,"area":"inference"},{"id":"2310.01382","title":"Compressing LLMs: The Truth is Rarely Pure and Never Simple","created":"2023-10-02","cited_by_count":62,"area":null},{"id":"2111.13445","title":"How Well Do Sparse Imagenet Models Transfer?","created":"2021-11-26","cited_by_count":50,"area":null},{"id":"2302.04089","title":"ZipLM: Inference-Aware Structured Pruning of Language Models","created":"2023-02-07","cited_by_count":49,"area":null},{"id":"2309.08520","title":"Scaling Laws for Sparsely-Connected Foundation Models","created":"2023-09-15","cited_by_count":43,"area":null},{"id":"2306.03805","title":"The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter","created":"2023-06-06","cited_by_count":42,"area":null},{"id":"2302.14623","title":"Fast as CHITA: Neural Network Pruning with Combinatorial Optimization","created":"2023-02-28","cited_by_count":40,"area":null},{"id":"2201.13096","title":"SPDY: Accurate Pruning with Speedup Guarantees","created":"2022-01-31","cited_by_count":36,"area":null},{"id":"2505.07004","title":"GuidedQuant: Large Language Model Quantization via Exploiting End Loss Guidance","created":"2025-05-11","cited_by_count":32,"area":null},{"id":"2303.02141","title":"Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!","created":"2023-03-03","cited_by_count":27,"area":null},{"id":"2304.03589","title":"On Efficient Training of Large-Scale Deep Learning Models: A Literature Review","created":"2023-04-07","cited_by_count":26,"area":null},{"id":"2312.01397","title":"Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective","created":"2023-12-03","cited_by_count":23,"area":null},{"id":"2210.09223","title":"CAP: Correlation-Aware Pruning for Highly-Accurate Sparse Vision Models","created":"2022-10-14","cited_by_count":20,"area":null},{"id":"2410.15567","title":"Pruning Foundation Models for High Accuracy without Retraining","created":"2024-10-21","cited_by_count":20,"area":null},{"id":"2405.15593","title":"MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence","created":"2024-05-24","cited_by_count":19,"area":null},{"id":"2405.18144","title":"4-bit Shampoo for Memory-Efficient Network Training","created":"2024-05-28","cited_by_count":18,"area":null},{"id":"2404.07946","title":"Towards Faster Training of Diffusion Models: An Inspiration of A Consistency Phenomenon","created":"2024-03-14","cited_by_count":14,"area":"diffusion"},{"id":"2209.14624","title":"Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning","created":"2022-09-29","cited_by_count":13,"area":null},{"id":"2310.02065","title":"VENOM: A Vectorized N:M Format for Unleashing the Power of Sparse Tensor Cores","created":"2023-10-03","cited_by_count":13,"area":null},{"id":"2310.02277","title":"Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs \"Difficult\" Downstream Tasks in LLMs","created":"2023-09-29","cited_by_count":13,"area":null},{"id":"2410.17170","title":"Self-calibration for Language Model Quantization and Pruning","created":"2024-10-22","cited_by_count":13,"area":null},{"id":"2304.12622","title":"Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures","created":"2023-04-25","cited_by_count":11,"area":null},{"id":"2411.18376","title":"Preserving Deep Representations In One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework","created":"2024-11-27","cited_by_count":11,"area":null},{"id":"2308.02060","title":"Accurate Neural Network Pruning Requires Rethinking Sparse Optimization","created":"2023-08-03","cited_by_count":10,"area":null},{"id":"2501.19183","title":"Position: Curvature Matrices Should Be Democratized via Linear Operators","created":"2025-01-31","cited_by_count":9,"area":null},{"id":"2302.09726","title":"Nystrom Method for Accurate and Scalable Implicit Differentiation","created":"2023-02-20","cited_by_count":8,"area":null},{"id":"2310.04680","title":"The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning","created":"2023-10-07","cited_by_count":7,"area":null},{"id":"2302.09275","title":"Structural Neural Additive Models: Enhanced Interpretable Machine Learning","created":"2023-02-18","cited_by_count":6,"area":null},{"id":"2305.00604","title":"ISAAC Newton: Input-based Approximate Curvature for Newton's Method","created":"2023-05-01","cited_by_count":6,"area":null},{"id":"2306.06098","title":"Error Feedback Can Accurately Compress Preconditioners","created":"2023-06-09","cited_by_count":6,"area":null},{"id":"2502.15790","title":"Signal Collapse in One-Shot Pruning: When Sparse Models Fail to Distinguish Neural Representations","created":"2025-02-18","cited_by_count":5,"area":null},{"id":"2302.07612","title":"Towards Optimal Compression: Joint Pruning and Quantization","created":"2023-02-15","cited_by_count":4,"area":null},{"id":"2412.02328","title":"Efficient Model Compression Techniques with FishLeg","created":"2024-12-03","cited_by_count":4,"area":null},{"id":"2410.02387","title":"BiSSL: Enhancing the Alignment Between Self-Supervised Pretraining and Downstream Fine-Tuning via Bilevel Optimization","created":"2024-10-03","cited_by_count":3,"area":null},{"id":"2506.13710","title":"Gradient-Normalized Smoothness for Optimization with Approximate Hessians","created":"2025-06-16","cited_by_count":3,"area":null},{"id":"2303.14409","title":"Vision Models Can Be Efficiently Specialized via Few-Shot Task-Aware Compression","created":"2023-03-25","cited_by_count":2,"area":null},{"id":"2405.06038","title":"From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks","created":"2024-05-09","cited_by_count":2,"area":null},{"id":"2410.19055","title":"Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms","created":"2024-10-24","cited_by_count":2,"area":null},{"id":"2506.08464","title":"MAC: An Efficient Gradient Preconditioning using Mean Activation Approximated Curvature","created":"2025-06-10","cited_by_count":2,"area":null},{"id":"2602.03515","title":"Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation","created":"2026-02-03","cited_by_count":2,"area":null},{"id":"2302.08484","title":"FOSI: Hybrid First and Second Order Optimization","created":"2023-02-16","cited_by_count":1,"area":null},{"id":"2308.02123","title":"Eva: A General Vectorized Approximation Framework for Second-order Optimization","created":"2023-08-04","cited_by_count":1,"area":null},{"id":"2401.06898","title":"Always-Sparse Training by Growing Connections with Guided Stochastic Exploration","created":"2024-01-12","cited_by_count":1,"area":null},{"id":"2404.01892","title":"Minimize Quantization Output Error with Bias Compensation","created":"2024-04-02","cited_by_count":1,"area":null},{"id":"2411.00969","title":"Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior","created":"2024-11-01","cited_by_count":1,"area":null},{"id":"2511.06477","title":"DyKAF: Dynamical Kronecker Approximation of the Fisher Information Matrix for Gradient Preconditioning","created":"2025-11-09","cited_by_count":1,"area":null},{"id":"2604.23475","title":"Supernodes and Halos: Loss-Critical Hubs in LLM Feed-Forward Layers","created":"2026-04-26","cited_by_count":1,"area":null},{"id":"2407.20496","title":"Toward Efficient Permutation for Hierarchical N:M Sparsity on GPUs","created":"2024-07-30","cited_by_count":0,"area":null},{"id":"2506.08360","title":"NysAct: A Scalable Preconditioned Gradient Descent using Nystrom Approximation","created":"2025-06-10","cited_by_count":0,"area":null},{"id":"2609.21672","title":"Accelerating Dense LLMs via L0-regularized Mixture-of-Experts","created":"2026-09-18","cited_by_count":0,"area":"moe"},{"id":"2609.33424","title":"A Light Bilevel Refinement Aligns Self-Supervised Representations for Stronger Task-Specific Learning","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}