1 citations · 2 across the 6 of their papers we have counts for
7 papers
Group Contrastive Learning for Weakly Paired Multimodal Data
Aditya Gorla, Hugues Van Assel, Jan-Christian Huetter +4
We present GROOVE, a semi-supervised multi-modal representation learning approach for high-content perturbation data where samples across modalities are weakly paired through share…
stable-pretraining-v1: Foundation Model Research Made Simple
Randall Balestriero, Hugues Van Assel, Sami BuGhanem +1
Foundation models and self-supervised learning (SSL) have become central to modern AI, yet research in this area remains hindered by complex codebases, redundant re-implementations…
Ditch the Denoiser: Emergence of Noise Robustness in Self-Supervised Learning from Data Curriculum
Wenquan Lu, Jiaqi Zhang, Hugues Van Assel +1
Self-Supervised Learning (SSL) has become a powerful solution to extract rich representations from unlabeled data. Yet, SSL research is mostly focused on clean, curated and high-qu…
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein
Hugues Van Assel, Cédric Vincent-Cuaz, Nicolas Courty +3
Unsupervised learning aims to capture the underlying structure of potentially large and high-dimensional datasets. Traditionally, this involves using dimensionality reduction (DR)…
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein
Hugues Van Assel, Cédric Vincent-Cuaz, Titouan Vayer +2
We present a versatile adaptation of existing dimensionality reduction (DR) objectives, enabling the simultaneous reduction of both sample and feature sizes. Correspondances betwee…
Optimal Transport with Adaptive Regularisation
Hugues Van Assel, Titouan Vayer, Remi Flamary +1
Regularising the primal formulation of optimal transport (OT) with a strictly convex term leads to enhanced numerical complexity and a denser transport plan. Many formulations impo…
SNEkhorn: Dimension Reduction with Symmetric Entropic Affinities
Hugues Van Assel, Titouan Vayer, Rémi Flamary +1
Many approaches in machine learning rely on a weighted graph to encode the similarities between samples in a dataset. Entropic affinities (EAs), which are notably used in the popul…