-
[XXXXXX 2025] Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations
Ajay Jaiswal, Jianyu Wang, Yixiao Li, Pingzhi Li, Tianlong Chen, Zhangyang Wang, Chong Wang, Ruoming Pang, Xianzhi Du.
[Paper]
[Code]
-
[XXXXXX 2025] From GaLore to WeLore: How Low-Rank Weights Non-uniformly Emerge from Low-Rank Gradients.
Ajay Jaiswal, Lu Yin, Zhenyu Zhang, Shiwei Liu, Jiawei Zhao, Yuandong Tian, and Zhangyang Wang.
[Paper]
[Code]
[Blog]
-
[XXXXXX 2025] Q-GaLore: Quantized GaLore with INT4 Projection and Layer-Adaptive Low-Rank Gradients.
Zhang, Zhenyu, Ajay Jaiswal, Lu Yin, Shiwei Liu, Jiawei Zhao, Yuandong Tian, and Zhangyang Wang.
[Paper]
[Code]
[Blog]
-
[XXXXXX 2025] Bit-Flip Error Resilience in LLMs: A Comprehensive Analysis and Defense Framework
Yuhang Chen, Zhen Tan, Ajay Jaiswal, Huaizhi Qu, Xinyu Zhao, Qi Lin, Yu Cheng, Andrew Kwong, Zhichao Cao, Tianlong Chen.
[Paper]
[Code]
-
[EMNLP Main 2024] FFN-SkipLLM: A Hidden Gem for Autoregressive Decoding with Adaptive Feed Forward Skipping.
Ajay Jaiswal, Bodun Hu, Lu Yin, Yeonju Ro, Shiwei Liu, Tianlong Chen, and Aditya Akella.
[Paper]
[Code]
-
[ICML 2024] LLaGA: Large Language and Graph Assistant.
Chen, Runjin, Tong Zhao, Ajay Jaiswal, Neil Shah, and Zhangyang Wang.
[Paper]
[Code]
-
[ICML 2024] Junk DNA Hypothesis: A Task-Centric Angle of LLM Pre-trained Weights through Sparsity.
*Lu Yin, *Ajay Jaiswal, Shiwei Liu, Souvik Kundu, and Zhangyang Wang.
[Paper]
[Code]
-
[ICLR 2024] Compressing llms: The Truth is Rarely Pure and Never Simple.
Ajay Jaiswal, Zhe Gan, Xianzhi Du, Bowen Zhang, Zhangyang Wang, and Yinfei Yang.
[Paper]
[Code]
-
[NeurIPS 2023] The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter.
Ajay Jaiswal, Shiwei Liu, Tianlong Chen, and Zhangyang Wang.
[Paper]
[Code]
-
[ICML 2023] Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models.
Ajay Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, and Zhangyang Wang.
[Paper]
[Code]
-
[ICML 2023] Graph ladling: Shockingly Simple and Parallel GNN Training without Intermediate Communication.
Ajay Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, and Zhangyang Wang.
[Paper]
[Code]
-
[ICLR 2023] Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!
Shiwei Liu, Tianlong Chen, Zhenyu Zhang, Xuxi Chen, Tianjin Huang, Ajay Jaiswal, and Zhangyang Wang.
[Paper]
[Code]
-
[ICLR 2023] Sparse moe as the new dropout: Scaling Dense and self-slimmable transformers.
Tianlong Chen, Zhenyu Zhang, Ajay Jaiswal, Shiwei Liu, and Zhangyang Wang.
[Paper]
[Code]
-
[NeurIPS 2022] Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again.
Ajay Jaiswal, Peihao Wang, Tianlong Chen, Justin Rousseau, Ying Ding, and Zhangyang Wang.
[Paper]
[Code]
-
[ICML 2022] Training Your Sparse Neural Network Better with Any Mask.
Ajay Jaiswal, Haoyu Ma, Tianlong Chen, Ying Ding, and Zhangyang Wang.
[Paper]
[Code]
Born is a small town (Belthara) in Eastern UP, India, I got my hands on a computer first time during my undergraduate freshman year.
My journey in computer science began when I was sixteen year old with a fantastic book "Let Us C" by Yashavant Kanetkar. During my career, I have
always tried to make best use of resources and smart people around me to learn and grow. I identified my research potential while working with Prof. Animesh
Mukherjee in IIT Kharagpur and later spent some wonderful time in Samsung Research after graduating. Currently, with the grace of GOD, I am very previlaged
to work and supervised by some extremely brilliant minds in VITA
and AI Health @ UT Austin.
Somewhere, something incredible is waiting to be known.
Visitor Counter
