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Academic
Deepfake Detection
Circumventing shortcuts in audio-visual deepfake detection datasets with unsupervised learning
Links: arXiv · GitHub
Abstract Good datasets are essential for developing and benchmarking any machine learning system. Their importance is even more …
Academic
Generalization & Interpretability
Do We Always Need the Simplicity Bias? Looking for Optimal Inductive Biases in the Wild
Links: arXiv · CVF Open Access
Abstract Common choices of architecture give neural networks a preference for fitting data with simple functions. This …
Links: arXiv Proceedings GitHub
Abstract Generative models can create entirely new images, but they can also partially modify real images in ways that …
Academic
Generalization & Interpretability
Robust Novelty Detection through Style-Conscious Feature Ranking
Links: arXiv Proceedings GitHub
Abstract Novelty detection seeks to identify samples deviating from a known distribution, yet data shifts in a …
The decoder block of a Transformer is the basic unit of all modern LLMs. Most of the compute used for it is spent on self-attention and the MLP, with …
Academic
Generalization & Interpretability
ConceptDrift: Uncovering Biases through the Lens of Foundational Models
Links: arXiv
Abstract Datasets and pre-trained models come with intrinsic biases. Most methods rely on spotting them by analysing misclassified …
Academic
Natural Language Processing
MolMix: A Simple Yet Effective Baseline for Multimodal Molecular Representation Learning
Links: arXiv GitHub
Abstract In this work, we propose a simple transformer-based baseline for multimodal molecular representation learning, …
Academic
Generalization & Interpretability
WASP: A Weight-Space Approach to Detecting Learned Spuriousness
Links: arXiv GitHub
Abstract It is of crucial importance to train machine learning models such that they clearly understand what defines each class in …
Links: arXiv GitHub
Abstract Message-passing graph neural networks (MPNNs) have emerged as a powerful paradigm for graph-based machine learning. …