TAB: Transformer Attention Bottlenecks enable User Intervention and Debugging in Vision-Language Models
Multi-head self-attention (MHSA) is a key component of Transformers, a widely popular architecture in both language and vision. Multiple heads...
Explainable AI
Interpretable LLM-based Table Question Answering
Interpretability for Table Question Answering (Table QA) is critical, particularly in high-stakes industries like finance or healthcare. Although recent approaches...
HoT: Highlighted Chain of Thought for Referencing Supporting Facts from Inputs
An Achilles heel of Large Language Models (LLMs) is their tendency to hallucinate nonfactual statements. A response mixed of factual...
Fast and Interpretable Face Identification for Out-Of-Distribution Data Using Vision Transformers
Most face identification approaches employ a Siamese neural network to compare two images at the image embedding level. Yet, this...
PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans
Nearest neighbors (NN) are traditionally used to compute final decisions, e.g., in Support Vector Machines or k-NN classifiers, and to...
PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck
* Equal contribution. CLIP-based classifiers rely on the prompt containing a {class name} that is known to the text encoder....
DeepFace-EMD: Re-ranking Using Patch-wise Earth Mover’s Distance Improves Out-Of-Distribution Face Identification
Face identification (FI) is ubiquitous and drives many high-stake decisions made by law enforcement. State-of-the-art FI approaches compare two images...
Double Trouble: How to not explain a text classifier’s decisions using counterfactuals synthesized by masked language models
Explaining how important each input feature is to a classifier’s decision is critical in high-stake applications. An underlying principle behind...
gScoreCAM: What objects is CLIP looking at?
Large-scale, multimodal models trained on web data such as OpenAI’s CLIP are becoming the foundation of many applications. Yet, they...
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy
Explaining artificial intelligence (AI) predictions is increasingly important and even imperative in many high-stakes applications where humans are the ultimate...
How explainable are adversarially-robust CNNs?
Three important criteria of existing convolutional neural networks (CNNs) are (1) test-set accuracy; (2) out-of-distribution accuracy; and (3) explainability. While...
The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
Explaining the decisions of an Artificial Intelligence (AI) model is increasingly critical in many real-world, high-stake applications. Hundreds of papers...
Explaining image classifiers by removing input features using generative models
Interpretability methods often measure the contribution of an input feature to an image classifier’s decisions by heuristically removing it via...
The shape and simplicity biases of adversarially robust ImageNet-trained CNNs
* All authors contributed equally. Adversarial training has been the topic of dozens of studies and a leading method for...
SAM: The Sensitivity of Attribution Methods to Hyperparameters
* Equal contributions. Attribution methods can provide powerful insights into the reasons for a classifier’s decision. We argue that a...
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Deep neural networks (DNNs) have demonstrated state-of-the-art results on many pattern recognition tasks, especially vision classification problems. Understanding the inner...
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks
We can better understand deep neural networks by identifying which features each of their neurons have learned to detect. To...