BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing. - Linnan Wang; Wei Wu 016; Jianxiong Xiao; Yi Yang 018 (CoRR, 2015)

Large Scale Artificial Neural Network Training Using Multi-GPUs. - Linnan Wang; Wei Wu 016; Jianxiong Xiao; Yang Yi (CoRR, 2015)

BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing. - Linnan Wang; Wei Wu 016; Zenglin Xu; Jianxiong Xiao; Yi Yang 018 (ICS, 2016)

Towards Interactive Data Exploration. - Carsten Binnig; Fuat Basik; Benedetto Buratti; Ugur Çetintemel; Yeounoh Chung; Andrew Crotty; Cyrus Cousins; Dylan Ebert; Philipp Eichmann; Alex Galakatos; Benjamin Hättasch; Amir Ilkhechi; et al. (BIRTE (Revised Selected Papers), 2017)

Simple and efficient parallelization for probabilistic temporal tensor factorization. - Guangxi Li; Zenglin Xu; Linnan Wang; Jinmian Ye; Irwin King; Michael R. Lyu (IJCNN, 2017)

Efficient Communications in Training Large Scale Neural Networks. - Yiyang Zhao; Linnan Wang; Wei Wu 016; George Bosilca; Richard W. Vuduc; Jinmian Ye; Wenqi Tang; Zenglin Xu (ACM Multimedia (Thematic Workshops), 2017)

Accelerating deep neural network training with inconsistent stochastic gradient descent. - Linnan Wang; Yi Yang 018; Renqiang Min; Srimat T. Chakradhar (Neural Networks, 2017)

Learning Compact Recurrent Neural Networks With Block-Term Tensor Decomposition. - Jinmian Ye; Linnan Wang; Guangxi Li; Di Chen; Shandian Zhe; Xinqi Chu; Zenglin Xu (CVPR, 2018)

ADAPT: an event-based adaptive collective communication framework. - Xi Luo; Wei Wu 016; George Bosilca; Thananon Patinyasakdikul; Linnan Wang; Jack J. Dongarra (HPDC, 2018)

Warp-Consolidation: A Novel Execution Model for GPUs. - Ang Li 006; Weifeng Liu 002; Linnan Wang; Kevin J. Barker; Shuaiwen Leon Song (ICS, 2018)

Superneurons: dynamic GPU memory management for training deep neural networks. - Linnan Wang; Jinmian Ye; Yiyang Zhao; Wei Wu 016; Ang Li 006; Shuaiwen Leon Song; Zenglin Xu; Tim Kraska (PPoPP, 2018)

AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search. - Linnan Wang; Yiyang Zhao; Yuu Jinnai (CoRR, 2018)

SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks. - Linnan Wang; Wei Wu 016; Yiyang Zhao; Junyu Zhang; Hang Liu 001; George Bosilca; Jack J. Dongarra; Maurice Herlihy; Rodrigo Fonseca (CoRR, 2018)

AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search. - Linnan Wang; Yiyang Zhao; Yuu Jinnai; Yuandong Tian; Rodrigo Fonseca (CoRR, 2019)

Sample-Efficient Neural Architecture Search by Learning Action Space. - Linnan Wang; Saining Xie; Teng Li 009; Rodrigo Fonseca; Yuandong Tian (CoRR, 2019)

Neural Architecture Search Using Deep Neural Networks and Monte Carlo Tree Search. - Linnan Wang; Yiyang Zhao; Yuu Jinnai; Yuandong Tian; Rodrigo Fonseca (AAAI, 2020)

FFT-based Gradient Sparsification for the Distributed Training of Deep Neural Networks. - Linnan Wang; Wei Wu 016; Junyu Zhang; Hang Liu 001; George Bosilca; Maurice Herlihy; Rodrigo Fonseca (HPDC, 2020)

Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search. - Linnan Wang; Rodrigo Fonseca; Yuandong Tian (NeurIPS, 2020)

Few-Shot Neural Architecture Search. - Yiyang Zhao; Linnan Wang; Yuandong Tian; Rodrigo Fonseca; Tian Guo 001 (ICML, 2021)

Learning Space Partitions for Path Planning. - Kevin Yang; Tianjun Zhang; Chris Cummins; Brandon Cui; Benoit Steiner; Linnan Wang; Joseph E. Gonzalez; Dan Klein 001; Yuandong Tian (NeurIPS, 2021)

Multi-objective Optimization by Learning Space Partitions. - Yiyang Zhao; Linnan Wang; Kevin Yang; Tianjun Zhang; Tian Guo 001; Yuandong Tian (CoRR, 2021)

Searching the Deployable Convolution Neural Networks for GPUs. - Linnan Wang; Chenhan Yu; Satish Salian; Slawomir Kierat; Szymon Migacz; Alex Fit-Florea (CVPR, 2022)

Multi-objective Optimization by Learning Space Partition. - Yiyang Zhao; Linnan Wang; Kevin Yang; Tianjun Zhang; Tian Guo 001; Yuandong Tian (ICLR, 2022)

GPUNet: Searching the Deployable Convolution Neural Networks for GPUs. - Linnan Wang; Chenhan Yu; Satish Salian; Slawomir Kierat; Szymon Migacz; Alex Fit-Florea (CoRR, 2022)

Sample-Efficient Neural Architecture Search by Learning Actions for Monte Carlo Tree Search. - Linnan Wang; Saining Xie; Teng Li 009; Rodrigo Fonseca; Yuandong Tian (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Building an Intelligent Agent to Design Neural Networks. - Linnan Wang (2022)

Multi-Objective Neural Architecture Search by Learning Search Space Partitions. - Yiyang Zhao; Linnan Wang; Tian Guo 001 (J. Mach. Learn. Res., 2024)

Training Video Foundation Models with NVIDIA NeMo. - Zeeshan Patel; Ethan He; Parth Mannan; Xiaowei Ren; Ryan Wolf; Niket Agarwal; Jacob Samuel Huffman; Zhuoyao Wang; Carl Wang; Jack Chang; Yan Bai; Tommy Huang; et al. (CoRR, 2025)