Paper-Conference

Hierarchical Balance Packing: Towards Efficient Supervised Fine-tuning for Long-Context LLM

HarmoniCa: Harmonizing Training and Inference for Better Feature Caching in Diffusion Transformer Acceleration

DA-KD: Difficulty-Aware Knowledge Distillation for Efficient Large Language Models

LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit

TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models

Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection

Fast and Controllable Post-training Sparsity: Learning Optimal Sparsity Allocation with Global Constraint in Minutes

QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models

PTSBench: A Comprehensive Post-Training Sparsity Benchmark Towards Algorithms and Models

PRoof: A Comprehensive Hierarchical Profiling Framework for Deep Neural Networks with Roofline Analysis