246 citations · 1.2k across the 31 of their papers we have counts for
36 papers
Pre-training with Graph Transformers
Jiaming Wang, Thomas Laurent, Xavier Bresson
This article investigates pre-training strategies for graph transformers in the biochemistry domain. By conducting comprehensive experiments, the study reveals that supervised pre-…
Composable Crystals: Controllable Materials Discovery via Concept Learning
Nian Liu, Yuwei Zeng, Ryoji Kubo +7
De novo crystal generation, a central task in materials discovery, aims to generate crystals that are simultaneously valid, stable, unique, and novel. Existing methods mainly rely…
Crys-JEPA: Accelerating Crystal Discovery via Embedding Screening and Generative Refinement
Nian Liu, Nikita Kazeev, Stephen Gregory Dale +8
De novo crystal generation seeks to discover materials that are not merely realistic, but also stable and novel. However, most existing generative models are trained to maximize th…
Generative diffusion models from a PDE perspective
Fei Cao, Kimball Johnston, Thomas Laurent +2
Diffusion models have become the de facto framework for generating new datasets. The core of these models lies in the ability to reverse a diffusion process in time. The goal of th…
A General Graph Spectral Wavelet Convolution via Chebyshev Order Decomposition
Nian Liu, Xiaoxin He, Thomas Laurent +3
Spectral graph convolution, an important tool of data filtering on graphs, relies on two essential decisions: selecting spectral bases for signal transformation and parameterizing…
Train Ego-Path Detection on Railway Tracks Using End-to-End Deep Learning
Thomas Laurent
This paper introduces the task of "train ego-path detection", a refined approach to railway track detection designed for intelligent onboard vision systems. Whereas existing resear…