no code implementations • ICLR 2022 • Xiaoling Hu, Xiao Lin, Michael Cogswell, Yi Yao, Susmit Jha, Chao Chen
Despite their success and popularity, deep neural networks (DNNs) are vulnerable when facing backdoor attacks.
no code implementations • 13 Oct 2021 • Kamran Alipour, Arijit Ray, Xiao Lin, Michael Cogswell, Jurgen P. Schulze, Yi Yao, Giedrius T. Burachas
In the domain of Visual Question Answering (VQA), studies have shown improvement in users' mental model of the VQA system when they are exposed to examples of how these systems answer certain Image-Question (IQ) pairs.
no code implementations • ICCV 2021 • Yunye Gong, Xiao Lin, Yi Yao, Thomas G. Dietterich, Ajay Divakaran, Melinda Gervasio
Existing calibration algorithms address the problem of covariate shift via unsupervised domain adaptation.
1 code implementation • 1 Apr 2021 • Xiao Lin, Meng Ye, Yunye Gong, Giedrius Buracas, Nikoletta Basiou, Ajay Divakaran, Yi Yao
Adapting pre-trained representations has become the go-to recipe for learning new downstream tasks with limited examples.
no code implementations • 26 Mar 2021 • Arijit Ray, Michael Cogswell, Xiao Lin, Kamran Alipour, Ajay Divakaran, Yi Yao, Giedrius Burachas
Hence, we propose Error Maps that clarify the error by highlighting image regions where the model is prone to err.
no code implementations • 5 Feb 2021 • Jie Yuan, Xuming Ran, Keyin Liu, Chen Yao, Yi Yao, Haiyan Wu, Quanying Liu
Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging.
no code implementations • 19 Nov 2020 • Meng Ye, Xiao Lin, Giedrius Burachas, Ajay Divakaran, Yi Yao
Few-Shot Learning (FSL) aims to improve a model's generalization capability in low data regimes.
no code implementations • 2 Jul 2020 • Kamran Alipour, Arijit Ray, Xiao Lin, Jurgen P. Schulze, Yi Yao, Giedrius T. Burachas
In this paper, we evaluate the impact of explanations on the user's mental model of AI agent competency within the task of visual question answering (VQA).
no code implementations • ICLR Workshop DeepDiffEq 2019 • Hammad A. Ayyubi, Yi Yao, Ajay Divakaran
Neural Ordinary Differential Equations (NODEs) have proven to be a powerful modeling tool for approximating (interpolation) and forecasting (extrapolation) irregularly sampled time series data.
no code implementations • 1 Mar 2020 • Kamran Alipour, Jurgen P. Schulze, Yi Yao, Avi Ziskind, Giedrius Burachas
Explainability and interpretability of AI models is an essential factor affecting the safety of AI.
no code implementations • 5 Apr 2019 • Arijit Ray, Yi Yao, Rakesh Kumar, Ajay Divakaran, Giedrius Burachas
Our experiments, therefore, demonstrate that ExAG is an effective means to evaluate the efficacy of AI-generated explanations on a human-AI collaborative task.
no code implementations • 26 Nov 2018 • Pallabi Ghosh, Yi Yao, Larry S. Davis, Ajay Divakaran
We show results on CAD120 (which provides pre-computed node features and edge weights for fair performance comparison across algorithms) as well as a more complex real-world activity dataset, Charades.