1 code implementation • 29 Mar 2024 • Bowen Lei, Dongkuan Xu, Ruqi Zhang, Bani Mallick
Sparse training has emerged as a promising method for resource-efficient deep neural networks (DNNs) in real-world applications.
no code implementations • 5 Feb 2024 • Bowen Lei, Rajarshi Guhaniyogi, Krishnendu Chandra, Aaron Scheffler, Bani Mallick
While there is a growing literature on image-on-image regression to delineate predictive inference of an image based on multiple images, existing approaches have limitations in efficiently borrowing information between multiple imaging modalities in the prediction of an image.
1 code implementation • ICCV 2023 • Dongyao Zhu, Bowen Lei, Jie Zhang, Yanbo Fang, Ruqi Zhang, Yiqun Xie, Dongkuan Xu
Neural networks trained on distilled data often produce over-confident output and require correction by calibration methods.
1 code implementation • 19 Jul 2023 • Longfeng Wu, Bowen Lei, Dongkuan Xu, Dawei Zhou
In particular, to quantify the uncertainties in RCA, we develop a node-level uncertainty quantification algorithm to model the overlapping support regions with high uncertainty; to handle the rarity of minority classes in miscalibration calculation, we generalize the distribution-based calibration metric to the instance level and propose the first individual calibration measurement on graphs named Expected Individual Calibration Error (EICE).
2 code implementations • 23 May 2023 • Binfeng Xu, Zhiyuan Peng, Bowen Lei, Subhabrata Mukherjee, Yuchen Liu, Dongkuan Xu
Augmented Language Models (ALMs) blend the reasoning capabilities of Large Language Models (LLMs) with tools that allow for knowledge retrieval and action execution.
no code implementations • 24 Apr 2023 • Shaoyi Huang, Haowen Fang, Kaleel Mahmood, Bowen Lei, Nuo Xu, Bin Lei, Yue Sun, Dongkuan Xu, Wujie Wen, Caiwen Ding
Experimental results show that NDSNN achieves up to 20. 52\% improvement in accuracy on Tiny-ImageNet using ResNet-19 (with a sparsity of 99\%) as compared to other SOTA methods (e. g., Lottery Ticket Hypothesis (LTH), SET-SNN, RigL-SNN).
no code implementations • 27 Feb 2023 • Yue Xiang, Dongyao Zhu, Bowen Lei, Dongkuan Xu, Ruqi Zhang
Gradients have been exploited in proposal distributions to accelerate the convergence of Markov chain Monte Carlo algorithms on discrete distributions.
1 code implementation • 18 Feb 2023 • Bowen Lei, Ruqi Zhang, Dongkuan Xu, Bani Mallick
Previous research has shown that deep neural networks tend to be over-confident, and we find that sparse training exacerbates this problem.
1 code implementation • 9 Jan 2023 • Bowen Lei, Dongkuan Xu, Ruqi Zhang, Shuren He, Bani K. Mallick
To accelerate and stabilize the convergence of sparse training, we analyze the gradient changes and develop an adaptive gradient correction method.
2 code implementations • CVPR 2023 • Lei Zhang, Jie Zhang, Bowen Lei, Subhabrata Mukherjee, Xiang Pan, Bo Zhao, Caiwen Ding, Yao Li, Dongkuan Xu
Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but efficient synthetic training datasets from large ones.
no code implementations • 30 Nov 2022 • Shaoyi Huang, Bowen Lei, Dongkuan Xu, Hongwu Peng, Yue Sun, Mimi Xie, Caiwen Ding
We further design an acquisition function and provide the theoretical guarantees for the proposed method and clarify its convergence property.
no code implementations • 2 May 2020 • Se Yoon Lee, Bowen Lei, Bani K. Mallick
In this paper, we propose a Bayesian hierarchical model that integrates global data for real-time prediction of infection trajectory for multiple countries.
Applications Methodology