6 citations · 12 across the 4 of their papers we have counts for
5 papers
MOFFieldBench: A Machine Learning Benchmark for Continuous Electronic Fields in Metal-Organic Frameworks
Zhaolin Hu, Hehe Fan, Chenhao Rao +4
Machine-learning models for metal-organic frameworks (MOFs) predominantly rely on atomic structures, whereas electron-density (ED) and electrostatic-potential (ESP) fields remain u…
ASDR: Exploiting Adaptive Sampling and Data Reuse for CIM-based Instant Neural Rendering
Fangxin Liu, Haomin Li, Bowen Zhu +4
Neural Radiance Fields (NeRF) offer significant promise for generating photorealistic images and videos. However, existing mainstream neural rendering models often fall short in me…
Samoyeds: Accelerating MoE Models with Structured Sparsity Leveraging Sparse Tensor Cores
Chenpeng Wu, Qiqi Gu, Heng Shi +2
The escalating size of Mixture-of-Experts (MoE) based Large Language Models (LLMs) presents significant computational and memory challenges, necessitating innovative solutions to e…
SkyMask: Attack-agnostic Robust Federated Learning with Fine-grained Learnable Masks
Peishen Yan, Hao Wang, Tao Song +5
Federated Learning (FL) is becoming a popular paradigm for leveraging distributed data and preserving data privacy. However, due to the distributed characteristic, FL systems are v…
Eliminating Domain Bias for Federated Learning in Representation Space
Jianqing Zhang, Yang Hua, Jian Cao +5
Recently, federated learning (FL) is popular for its privacy-preserving and collaborative learning abilities. However, under statistically heterogeneous scenarios, we observe that…