6 citations · 7 across the 8 of their papers we have counts for
6 papers · 1 filter
When to Align, When to Predict: A Phase Diagram for Multimodal Learning
Ilay Kamai, Hugues Van Assel, Aviv Regev +2
Cross-modal alignment (CA) and cross-modal prediction (CP) are the dominant paradigms for multimodal representation learning, yet there is no systematic understanding of when each…
Generate in Reconstruction Space, Match in Semantic Space: Transport Geometry for One-Step Generation
Hugues Van Assel, Edward De Brouwer, Saeed Saremi +2
Generative modeling and self-supervised representation learning (SSL) optimize structurally different objectives: generative training rewards distributional fidelity, while SSL rew…
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…
E-valuator: Reliable Agent Verifiers with Sequential Hypothesis Testing
Shuvom Sadhuka, Drew Prinster, Clara Fannjiang +4
Agentic AI systems execute a sequence of actions, such as reasoning steps or tool calls, in response to a user prompt. To evaluate the success of their trajectories, researchers ha…
Supervised Contrastive Block Disentanglement
Taro Makino, Ji Won Park, Natasa Tagasovska +11
Real-world datasets often combine data collected under different experimental conditions. This yields larger datasets, but also introduces spurious correlations that make it diffic…
Toward the Identifiability of Comparative Deep Generative Models
Romain Lopez, Jan-Christian Huetter, Ehsan Hajiramezanali +2
Deep Generative Models (DGMs) are versatile tools for learning data representations while adequately incorporating domain knowledge such as the specification of conditional probabi…