3 citations · 5 across the 6 of their papers we have counts for
6 papers
MRT: Learning Compact Representations with Mixed RWKV-Transformer for Extreme Image Compression
Han Liu, Hengyu Man, Xingtao Wang +2
Recent advances in extreme image compression have revealed that mapping pixel data into highly compact latent representations can significantly improve coding efficiency. However,…
Rotation-Equivariant Self-Supervised Method in Image Denoising
Hanze Liu, Jiahong Fu, Qi Xie +1
Self-supervised image denoising methods have garnered significant research attention in recent years, for this kind of method reduces the requirement of large training datasets. Co…
Adaptive Batch Size Schedules for Distributed Training of Language Models with Data and Model Parallelism
Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu +2
An appropriate choice of batch sizes in large-scale model training is crucial, yet it involves an intrinsic yet inevitable dilemma: large-batch training improves training efficienc…
Pareto-Optimal Energy Alignment for Designing Nature-Like Antibodies
Yibo Wen, Chenwei Xu, Jerry Yao-Chieh Hu +2
We present a three-stage framework for training deep learning models specializing in antibody sequence-structure co-design. We first pre-train a language model using millions of an…
Communication-Efficient Adaptive Batch Size Strategies for Distributed Local Gradient Methods
Tim Tsz-Kit Lau, Weijian Li, Chenwei Xu +2
Modern deep neural networks often require distributed training with many workers due to their large size. As the number of workers increases, communication overheads become the mai…
AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods
Tim Tsz-Kit Lau, Han Liu, Mladen Kolar
The choice of batch size in minibatch stochastic gradient optimization is critical for both optimization and generalization performance in large-scale model training. Although larg…