Search Results for author:
Found 14 papers, 4 papers with code
Mix-n-Match : Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
This paper studies the problem of post-hoc calibration of machine learning classifiers.
Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model
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.
Pursing the Sparse Limitation of Spiking Deep Learning Structures
It posits that within dense neural networks, there exist winning tickets or subnetworks that are sparser but do not compromise performance.
RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias
Recently, there has been a surge of interest and attention in Transformer-based structures, such as Vision Transformer (ViT) and Vision Multilayer Perceptron (VMLP).
Invertible Network for Unpaired Low-light Image Enhancement
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.
Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations
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.
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness
Successful adoption of deep learning (DL) in the wild requires models to be: (1) compact, (2) accurate, and (3) robust to distributional shifts.
A Deep Learning-Accelerated Data Assimilation and Forecasting Workflow for Commercial-Scale Geologic Carbon Storage
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.
Leveraging Uncertainty from Deep Learning for Trustworthy Materials Discovery Workflows
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.
A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning
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.
Explainable Deep Learning for Uncovering Actionable Scientific Insights for Materials Discovery and Design
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges.
Actionable Attribution Maps for Scientific Machine Learning
The scientific community has been increasingly interested in harnessing the power of deep learning to solve various domain challenges.
Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
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.
Mean Reverting Portfolios via Penalized OU-Likelihood Estimation
We study an optimization-based approach to con- struct a mean-reverting portfolio of assets.
