24 citations · 35 across the 11 of their papers we have counts for
11 papers
Course Correcting Koopman Representations
Mahan Fathi, Clement Gehring, Jonathan Pilault +3
Koopman representations aim to learn features of nonlinear dynamical systems (NLDS) which lead to linear dynamics in the latent space. Theoretically, such features can be used to s…
On Conditional and Compositional Language Model Differentiable Prompting
Jonathan Pilault, Can Liu, Mohit Bansal +1
Prompts have been shown to be an effective method to adapt a frozen Pretrained Language Model (PLM) to perform well on downstream tasks. Prompts can be represented by a human-engin…
Block-State Transformers
Mahan Fathi, Jonathan Pilault, Orhan Firat +3
State space models (SSMs) have shown impressive results on tasks that require modeling long-range dependencies and efficiently scale to long sequences owing to their subquadratic r…
JaxPruner: A concise library for sparsity research
Joo Hyung Lee, Wonpyo Park, Nicole Mitchell +22
This paper introduces JaxPruner, an open-source JAX-based pruning and sparse training library for machine learning research. JaxPruner aims to accelerate research on sparse neural…
Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction
Jonathan Pilault, Xavier Garcia, Arthur Bražinskas +1
Crosslingual conditional generation (e.g., machine translation) has long enjoyed the benefits of scaling. Nonetheless, there are still issues that scale alone may not overcome. A s…
Using Graph Algorithms to Pretrain Graph Completion Transformers
Jonathan Pilault, Michael Galkin, Bahare Fatemi +3
Recent work on Graph Neural Networks has demonstrated that self-supervised pretraining can further enhance performance on downstream graph, link, and node classification tasks. How…