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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 …

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 …

Links: arXiv Abstract Datasets and pre-trained models come with intrinsic biases. Most methods rely on spotting them by analysing misclassified …

Links: arXiv GitHub Abstract In this work, we propose a simple transformer-based baseline for multimodal molecular representation learning, …

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. …

Large Language Models (LLMs) took the world by storm in 2023, revolutionizing the way people search and generate text content. LLMs for code have …

This is a sister blogpost to the previous one about a similar scheme (BFV) and it's part of the series that covers fully homomorphic encryption …