3.7k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of SciencesCN257 papers
- Beijing Academy of Quantum Information SciencesCN235 papers
- Peking UniversityCN220 papers
- University of Science and Technology of ChinaCN204 papers
- Tsinghua UniversityCN193 papers
- University of Chinese Academy of SciencesCN143 papers
- University of Hong KongHK135 papers
- Institute of PhysicsPL94 papers
- National University of SingaporeSG94 papers
- Peng Cheng LaboratoryCN94 papers
- Hong Kong University of Science and TechnologyHK93 papers
- Fudan UniversityCN90 papers
7 papers · 2 filters
Towards A Flexible Accuracy-Oriented Deep Learning Module Inference Latency Prediction Framework for Adaptive Optimization Algorithms
Jingran Shen, Nikos Tziritas, Georgios Theodoropoulos
With the rapid development of Deep Learning, more and more applications on the cloud and edge tend to utilize large DNN (Deep Neural Network) models for improved task execution eff…
DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset
Weijie Liu, Xiaoxi Zhang, Jingpu Duan +3
Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data. While prior works…
Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory
Naoki Masuyama, Yusuke Nojima, Yuichiro Toda +3
With the increasing importance of data privacy protection, various privacy-preserving machine learning methods have been proposed. In the clustering domain, various algorithms with…
Class Binarization to NeuroEvolution for Multiclass Classification
Gongjin Lan, Zhenyu Gao, Lingyao Tong +1
Multiclass classification is a fundamental and challenging task in machine learning. The existing techniques of multiclass classification can be categorized as (i) decomposition in…
Rethinking Population-assisted Off-policy Reinforcement Learning
Bowen Zheng, Ran Cheng
While off-policy reinforcement learning (RL) algorithms are sample efficient due to gradient-based updates and data reuse in the replay buffer, they struggle with convergence to lo…
Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems
Ce Zhang, Kailiang Wu, Zhihai He
Given an unknown dynamical system, what is the minimum number of samples needed for effective learning of its governing laws and accurate prediction of its future evolution behavio…
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Chaopeng Shen, Alison P. Appling, Pierre Gentine +27
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powe…