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Found 12 papers, 1 papers with code
Trigger Hunting with a Topological Prior for Trojan Detection
Despite their success and popularity, deep neural networks (DNNs) are vulnerable when facing backdoor attacks.
Improving Users' Mental Model with Attention-directed Counterfactual Edits
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.
Confidence Calibration for Domain Generalization under Covariate Shift
Existing calibration algorithms address the problem of covariate shift via unsupervised domain adaptation.
Modular Adaptation for Cross-Domain Few-Shot Learning
Adapting pre-trained representations has become the go-to recipe for learning new downstream tasks with limited examples.
Generating and Evaluating Explanations of Attended and Error-Inducing Input Regions for VQA Models
Hence, we propose Error Maps that clarify the error by highlighting image regions where the model is prone to err.
Machine Learning Applications on Neuroimaging for Diagnosis and Prognosis of Epilepsy: A Review
Machine learning is playing an increasingly important role in medical image analysis, spawning new advances in the clinical application of neuroimaging.
Hybrid Consistency Training with Prototype Adaptation for Few-Shot Learning
Few-Shot Learning (FSL) aims to improve a model's generalization capability in low data regimes.
The Impact of Explanations on AI Competency Prediction in VQA
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).
Progressive Growing of Neural ODEs
Neural Ordinary Differential Equations (NODEs) have proven to be a powerful modeling tool for approximating (interpolation) and forecasting (extrapolation) irregularly sampled time series data.
A Study on Multimodal and Interactive Explanations for Visual Question Answering
Explainability and interpretability of AI models is an essential factor affecting the safety of AI.
Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval
Our experiments, therefore, demonstrate that ExAG is an effective means to evaluate the efficacy of AI-generated explanations on a human-AI collaborative task.
Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation
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.
