Deep Learning
GradOrth: A Simple yet Efficient Out-of-Distribution Detection with Orthogonal Projection of Gradients
Detecting out-of-distribution (OOD) data is crucial for ensuring the safe deployment of machine learning models in real-world applications.
Quilt-1M: One Million Image-Text Pairs for Histopathology Wisdom O. Ikezogwo
Recent accelerations in multi-modal applications have been made possible with the plethora of image and text data available online.
Mixed Samples as Probes for Unsupervised Model Selection in Domain Adaptation
In this paper, we present MixV al, an innovative model selection method that operates solely with unlabeled target data during inference.
KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training
This paper proposes a method for hiding the least-important samples during the training of deep neural networks to increase efficiency, i.e., to reduce the cost of
Tracr: Compiled Transformers as a Laboratory for Interpretability David Lindner
We show how to "compile" human-readable programs into standard decoder-only transformer models.
Learning to Receive Help: Intervention-Aware Concept Embedding Models
We argue that this is rooted in a CBM's lack of