{"total":36,"rows":[{"id":"2405.20935","title":"Effective Interplay between Sparsity and Quantization: From Theory to Practice","created":"2024-05-31","cited_by_count":19,"area":null},{"id":"2405.15743","title":"Sparse maximal update parameterization: A holistic approach to sparse training dynamics","created":"2024-05-24","cited_by_count":16,"area":null},{"id":"2410.17711","title":"Beware of Calibration Data for Pruning Large Language Models","created":"2024-10-23","cited_by_count":15,"area":null},{"id":"2408.09966","title":"Mask in the Mirror: Implicit Sparsification","created":"2024-08-19","cited_by_count":14,"area":null},{"id":"2407.09111","title":"Inference Optimization of Foundation Models on AI Accelerators","created":"2024-07-12","cited_by_count":9,"area":null},{"id":"2504.19274","title":"TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks","created":"2025-04-27","cited_by_count":9,"area":null},{"id":"2403.07688","title":"Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons","created":"2024-03-12","cited_by_count":6,"area":null},{"id":"2407.05510","title":"SCATTER: Algorithm-Circuit Co-Sparse Photonic Accelerator with Thermal-Tolerant, Power-Efficient In-situ Light Redistribution","created":"2024-07-07","cited_by_count":6,"area":null},{"id":"2504.12801","title":"Sign-In to the Lottery: Reparameterizing Sparse Training From Scratch","created":"2025-04-17","cited_by_count":6,"area":null},{"id":"2501.19107","title":"Brain network science modelling of sparse neural networks enables Transformers and LLMs to perform as fully connected","created":"2025-01-31","cited_by_count":4,"area":null},{"id":"2506.11449","title":"Dynamic Sparse Training of Diagonally Sparse Networks","created":"2025-06-13","cited_by_count":4,"area":null},{"id":"2411.03171","title":"Navigating Extremes: Dynamic Sparsity in Large Output Spaces","created":"2024-11-05","cited_by_count":3,"area":null},{"id":"2411.09816","title":"Learning Fine-grained Parameter Sharing via Sparse Tensor Decomposition","created":"2024-11-14","cited_by_count":3,"area":null},{"id":"2501.18980","title":"Symmetric Pruning of Large Language Models","created":"2025-01-31","cited_by_count":3,"area":null},{"id":"2503.12829","title":"SparseLUT: Sparse Connectivity Optimization for Lookup Table-based Deep Neural Networks","created":"2025-03-17","cited_by_count":2,"area":null},{"id":"2505.22120","title":"LoKI: Low-damage Knowledge Implanting of Large Language Models","created":"2025-05-28","cited_by_count":2,"area":null},{"id":"2602.18825","title":"Bayesian Lottery Ticket Hypothesis","created":"2026-02-21","cited_by_count":2,"area":null},{"id":"2411.13545","title":"Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning","created":"2024-11-20","cited_by_count":1,"area":null},{"id":"2502.03658","title":"Advancing Weight and Channel Sparsification with Enhanced Saliency","created":"2025-02-05","cited_by_count":1,"area":null},{"id":"2505.18107","title":"Accelerating Learned Image Compression Through Modeling Neural Training Dynamics","created":"2025-05-23","cited_by_count":1,"area":null},{"id":"2505.19893","title":"ESLM: Risk-Averse Selective Language Modeling for Efficient Pretraining","created":"2025-05-26","cited_by_count":1,"area":null},{"id":"2512.06288","title":"Theoretical Compression Bounds for Wide Multilayer Perceptrons","created":"2025-12-06","cited_by_count":1,"area":null},{"id":"2606.01117","title":"HASTE: Hardware-Aware Dynamic Sparse Training for Large Output Spaces","created":"2026-05-31","cited_by_count":1,"area":null},{"id":"2508.14140","title":"Neuro-inspired Ensemble-to-Ensemble Communication Primitives for Sparse and Efficient ANNs","created":"2025-08-19","cited_by_count":0,"area":null},{"id":"2510.14812","title":"SHUFFLESPARSE: Learned Shuffles for Structured Sparse Networks","created":"2025-10-16","cited_by_count":0,"area":null},{"id":"2601.09773","title":"Enhancing LUT-based Deep Neural Networks Inference through Architecture and Connectivity Optimization","created":"2026-01-14","cited_by_count":0,"area":null},{"id":"2601.21561","title":"SAL: Selective Adaptive Learning for Backpropagation-Free Training with Sparsification","created":"2026-01-29","cited_by_count":0,"area":null},{"id":"2602.06838","title":"An Adaptive Differentially Private Federated Learning Framework","created":"2026-02-06","cited_by_count":0,"area":null},{"id":"2604.10560","title":"Heterogeneous Connectivity in Sparse Networks: Fan-in Profiles, Gradient Hierarchy, and Topological Equilibria","created":"2026-04-12","cited_by_count":0,"area":null},{"id":"2605.15435","title":"On the Stability of Growth in Structural Plasticity: Forward-Active yet Backward-Starved","created":"2026-05-14","cited_by_count":0,"area":null},{"id":"2605.21104","title":"HORST: Composing Optimizer Geometries for Sparse Transformer Training","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2605.27541","title":"SparseOpt: Addressing Normalization-induced Gradient Skew in Sparse Training","created":"2026-05-26","cited_by_count":0,"area":null},{"id":"2606.00888","title":"Memory-Efficient LLM Training with Dynamic Sparsity: From Stability to Practical Scaling","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2606.20156","title":"Modularity-Free Conflict-Averse Training for Generalized PINNs","created":"2026-06-18","cited_by_count":0,"area":null},{"id":"2610.04093","title":"Beyond Masked Sparsity: SNACK Enables Truly Sparse Neural Networks on GPU","created":"2026-10-02","cited_by_count":0,"area":null},{"id":"2610.16147","title":"BARTER: Compact Dynamic Sparse Training with Standard Dense Kernels","created":"2026-10-12","cited_by_count":0,"area":null}],"next_cursor":null}