{"total":20,"rows":[{"id":"2310.04064","title":"How to Capture Higher-order Correlations? Generalizing Matrix Softmax Attention to Kronecker Computation","created":"2023-10-06","cited_by_count":44,"area":null},{"id":"2405.03103","title":"Learning from Students: Applying t-Distributions to Explore Accurate and Efficient Formats for LLMs","created":"2024-05-06","cited_by_count":18,"area":null},{"id":"2405.18710","title":"To FP8 and Back Again: Quantifying Reduced Precision Effects on LLM Training Stability","created":"2024-05-29","cited_by_count":9,"area":null},{"id":"2409.01990","title":"Designing Large Foundation Models for Efficient Training and Inference: A Survey","created":"2024-09-03","cited_by_count":7,"area":null},{"id":"2411.05197","title":"Hardware and Software Platform Inference","created":"2024-11-07","cited_by_count":7,"area":null},{"id":"2407.04272","title":"Accelerating Communication in Deep Learning Recommendation Model Training with Dual-Level Adaptive Lossy Compression","created":"2024-07-05","cited_by_count":5,"area":null},{"id":"2502.01070","title":"An Inquiry into Datacenter TCO for LLM Inference with FP8","created":"2025-02-03","cited_by_count":5,"area":null},{"id":"2502.15092","title":"Optimizing Singular Spectrum for Large Language Model Compression","created":"2025-02-20","cited_by_count":4,"area":null},{"id":"2601.22787","title":"Float8@2bits: Entropy Coding Enables Data-Free Model Compression","created":"2026-01-30","cited_by_count":2,"area":null},{"id":"2602.01410","title":"SNIP: An Adaptive Mixed Precision Framework for Subbyte Large Language Model Training","created":"2026-02-01","cited_by_count":2,"area":null},{"id":"2604.24088","title":"TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training","created":"2026-04-27","cited_by_count":2,"area":null},{"id":"2312.02194","title":"Local Masking Meets Progressive Freezing: Crafting Efficient Vision Transformers for Self-Supervised Learning","created":"2023-12-02","cited_by_count":1,"area":null},{"id":"2407.02610","title":"Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point","created":"2024-07-02","cited_by_count":1,"area":null},{"id":"2411.00907","title":"On the Impact of White-box Deployment Strategies for Edge AI on Latency and Model Performance","created":"2024-11-01","cited_by_count":1,"area":null},{"id":"2504.21553","title":"Precision Where It Matters: A Novel Spike Aware Mixed-Precision Quantization Strategy for LLaMA-based Language Models","created":"2025-04-30","cited_by_count":1,"area":null},{"id":"2508.03306","title":"Reliable Evaluation Protocol for Low-Precision Retrieval","created":"2025-08-05","cited_by_count":0,"area":null},{"id":"2511.08093","title":"Quantizing Whisper-small: How design choices affect ASR performance","created":"2025-11-11","cited_by_count":0,"area":null},{"id":"2511.12376","title":"BitSnap: Checkpoint Sparsification and Quantization in LLM Training","created":"2025-11-15","cited_by_count":0,"area":null},{"id":"2512.14090","title":"Arithmetic-Intensity-Aware Quantization","created":"2025-12-16","cited_by_count":0,"area":null},{"id":"2601.03290","title":"Lightweight Transformer Architectures for Edge Devices in Real-Time Applications","created":"2026-01-05","cited_by_count":0,"area":null}],"next_cursor":null}