No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier Paper • 2303.10058 • Published Mar 17, 2023
Masking as an Efficient Alternative to Finetuning for Pretrained Language Models Paper • 2004.12406 • Published Apr 26, 2020 • 1
PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology Paper • 2401.16355 • Published Jan 29, 2024 • 2
FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction Paper • 2205.13462 • Published May 26, 2022
Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models Paper • 2405.14297 • Published May 23, 2024 • 2
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuning Paper • 2407.01320 • Published Jul 1, 2024
FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering Paper • 2301.12379 • Published Jan 29, 2023
Revisiting Weighted Aggregation in Federated Learning with Neural Networks Paper • 2302.10911 • Published Feb 14, 2023 • 1
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration Paper • 2407.00203 • Published Jun 28, 2024
Switch EMA: A Free Lunch for Better Flatness and Sharpness Paper • 2402.09240 • Published Feb 14, 2024 • 3
On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm Paper • 2312.03526 • Published Dec 6, 2023
Efficient Generative Model Training via Embedded Representation Warmup Paper • 2504.10188 • Published Apr 14 • 12