1 code implementation • ICML 2020 • Jize Zhang, Bhavya Kailkhura, T. Yong-Jin Han
This paper studies the problem of post-hoc calibration of machine learning classifiers.
no code implementations • 29 Feb 2024 • Hao Cheng, Erjia Xiao, Jindong Gu, Le Yang, Jinhao Duan, Jize Zhang, Jiahang Cao, Kaidi Xu, Renjing Xu
Large Vision-Language Models (LVLMs) rely on vision encoders and Large Language Models (LLMs) to exhibit remarkable capabilities on various multi-modal tasks in the joint space of vision and language.
no code implementations • 18 Nov 2023 • Hao Cheng, Jiahang Cao, Erjia Xiao, Mengshu Sun, Le Yang, Jize Zhang, Xue Lin, Bhavya Kailkhura, Kaidi Xu, Renjing Xu
It posits that within dense neural networks, there exist winning tickets or subnetworks that are sparser but do not compromise performance.
no code implementations • 23 Sep 2023 • Hao Cheng, Jinhao Duan, Hui Li, Lyutianyang Zhang, Jiahang Cao, Ping Wang, Jize Zhang, Kaidi Xu, Renjing Xu
Recently, there has been a surge of interest and attention in Transformer-based structures, such as Vision Transformer (ViT) and Vision Multilayer Perceptron (VMLP).
no code implementations • 24 Dec 2021 • Jize Zhang, Haolin Wang, Xiaohe Wu, WangMeng Zuo
Existing unpaired low-light image enhancement approaches prefer to employ the two-way GAN framework, in which two CNN generators are deployed for enhancement and degradation separately.
no code implementations • 3 Nov 2021 • Ehsan Adeli, Jize Zhang, Alexandros A. Taflanidis
The proposed method's performance by considering the improvements and adaptations required for the storm surge data is assessed and compared to the original GAIN and a few other techniques.
2 code implementations • NeurIPS 2021 • James Diffenderfer, Brian R. Bartoldson, Shreya Chaganti, Jize Zhang, Bhavya Kailkhura
Successful adoption of deep learning (DL) in the wild requires models to be: (1) compact, (2) accurate, and (3) robust to distributional shifts.
1 code implementation • 9 May 2021 • Hewei Tang, Pengcheng Fu, Christopher S. Sherman, Jize Zhang, Xin Ju, François Hamon, Nicholas A. Azzolina, Matthew Burton-Kelly, Joseph P. Morris
Fast assimilation of monitoring data to update forecasts of pressure buildup and carbon dioxide (CO2) plume migration under geologic uncertainties is a challenging problem in geologic carbon storage.
no code implementations • 2 Dec 2020 • Jize Zhang, Bhavya Kailkhura, T. Yong-Jin Han
In this paper, we leverage predictive uncertainty of deep neural networks to answer challenging questions material scientists usually encounter in machine learning based materials applications workflows.
no code implementations • NeurIPS 2020 • Bhavya Kailkhura, Jayaraman J. Thiagarajan, Qunwei Li, Jize Zhang, Yi Zhou, Timo Bremer
Using this framework, we show that space-filling sample designs, such as blue noise and Poisson disk sampling, which optimize spectral properties, outperform random designs in terms of the generalization gap and characterize this gain in a closed-form.
no code implementations • 16 Jul 2020 • Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, T. Yong-Jin Han
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges.
no code implementations • 30 Jun 2020 • Shusen Liu, Bhavya Kailkhura, Jize Zhang, Anna M. Hiszpanski, Emily Robertson, Donald Loveland, T. Yong-Jin Han
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges.
1 code implementation • 16 Mar 2020 • Jize Zhang, Bhavya Kailkhura, T. Yong-Jin Han
We show that none of the existing methods satisfy all three requirements, and demonstrate how Mix-n-Match calibration strategies (i. e., ensemble and composition) can help achieve remarkably better data-efficiency and expressive power while provably maintaining the classification accuracy of the original classifier.
no code implementations • 17 Mar 2018 • Jize Zhang, Tim Leung, Aleksandr Y. Aravkin
We study an optimization-based approach to con- struct a mean-reverting portfolio of assets.