Large language models (LLMs) such as ChatGPT have received immense interest for their general-purpose language understanding and, in particular, their ability to generate high-quality text …
Sparse linear models are a gold standard tool for interpretable machine learning, a field of emerging importance as predictive models permeate decision-making in many domains. …
GOLEM is a method for causal discovery. It formulates a likelihood-based score function with soft sparsity and DAG constraints for learning linear DAGs, which guarantees …
We propose a method to control material attributes of objects like roughness, metallic, albedo, and transparency in real images. Our method capitalizes on the generative …
Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, for classifying a node these methods only consider nodes that …
ML reading group
Kayewords: Machine learningGraph Neural Networks (GNNs) are powerful tools for graph representation learning. Despite their rapid development, GNNs also faces some challenges, such as over-fitting, over-smoothing, and …
Despite the advancements of open-source large language models (LLMs) and their variants, e.g., LLaMA and Vicuna, they remain significantly limited in performing higher-level tasks, such …
Given the growing importance of AI literacy, we decided to write this tutorial to help narrow the gap between the discourse among those who study …
Graph neural networks (GNNs) are a popular class of machine learning models whose major advantage is their ability to incorporate a sparse and discrete dependency …
Contrastive learning (CL) has emerged as a powerful framework for learning representations of images and text in a self-supervised manner while enhancing model robustness against …
Evaluating the accuracy of outputs generated by Large Language Models (LLMs) is especially important in the climate science and policy domain. We introduce the Expert …
Recent research shows the potential of enhancing the problem-solving ability of large language models (LLMs) through the use of external tools. However, prior work along …
From microchips to batteries and photovoltaics, discovery of inorganic crystals is a fundamental problem in materials science. Graph Networks for Materials Science (GNoME) is a …
A Python library for Graph Neural Networks (GNNs) in JAX.
Vision Transformers (ViTs) have revolutionized the field of computer vision, yet their deployments on resource-constrained devices remain challenging due to high computational demands. To expedite …
The official GTP-ViT code for token summarisation for efficient vision transformers via graph-based token propagation.
Drug combination refers to the use of two or more drugs to treat a specific disease at the same time. It is currently the mainstream …
Deep learning models are often trained on distributed, webscale datasets crawled from the internet. In this paper, we introduce two new dataset poisoning attacks that …
decoding is an integral but computationally demanding step in the implementation of quantum error correction for fault-tolerant quantum computing. Here, we report the development and …
We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and …
To practice reading and learning from ML-related papers Date and time prepared each month
Kayewords: Machine LearningThe genome sequence contains the blueprint for governing cellular processes. While the availability of genomes has vastly increased over the last decades, experimental annotation of …
Graph Neural Networks (GNNs) have been widely studied for graph data representation and learning. However, existing GNNs generally conduct context-aware learning on node feature representation …
Large-scale pre-trained language models such as BERT are popular solutions for text classification. Due to the superior performance of these advanced methods, nowadays, people often …