Publications

Stimulus information guides the emergence of behavior-related signals in primary somatosensory cortex during learning.
Mariangela Panniello
Colleen J Gillon
Roberto Maffulli
Marco Celotto
Stefano Panzeri
Blake Richards
Michael M Kohl
2024-05-25
Cell Reports (published)
doi.org
Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches
Léo Boisvert
Hélène Verhaeghe
Quentin Cappart
2024-05-25
Integration of Constraint Programming, Artificial Intelligence, and Operations Research (published)
doi.org
arxiv.org
Assortment Optimization with Visibility Constraints
Théo Barré
Omar El Housni
Andrea Lodi
2024-05-22
Integer Programming and Combinatorial Optimization (published)
doi.org
arxiv.org
Towards Modular LLMs by Building and Reusing a Library of LoRAs
Oleksiy Ostapenko
Zhan Su
Edoardo Ponti
Laurent Charlin
Nicolas Le Roux
Matheus Pereira
Lucas Caccia
Alessandro Sordoni
The growing number of parameter-efficient adaptations of a base large language model (LLM) calls for studying whether we can reuse such trai… (see more)ned adapters to improve performance for new tasks. We study how to best build a library of adapters given multi-task data and devise techniques for both zero-shot and supervised task generalization through routing in such library. We benchmark existing approaches to build this library and introduce model-based clustering, MBC, a method that groups tasks based on the similarity of their adapter parameters, indirectly optimizing for transfer across the multi-task dataset. To re-use the library, we present a novel zero-shot routing mechanism, Arrow, which enables dynamic selection of the most relevant adapters for new inputs without the need for retraining. We experiment with several LLMs, such as Phi-2 and Mistral, on a wide array of held-out tasks, verifying that MBC-based adapters and Arrow routing lead to superior generalization to new tasks. We make steps towards creating modular, adaptable LLMs that can match or outperform traditional joint training.
2024-05-18
ArXiv (preprint)
arxiv.org
arxiv.org
GFETM: Genome Foundation-based Embedded Topic Model for scATAC-seq Modeling
Yimin Fan
Adrien Osakwe
Yu Li
Jun Ding
Yue Li
2024-05-17
Lecture Notes in Computer Science (published)
doi.org
What Mechanisms Does Knowledge Distillation Distill?
Cindy Wu
Ekdeep Singh Lubana
Bruno Mlodozeniec
Robert Kirk
David Scott Krueger
Knowledge distillation is a commonly-used compression method in ML due to the popularity of increasingly large-scale models, but it is uncle… (see more)ar if all the information a teacher model contains is distilled into the smaller student model. We aim to formalize the concept of ‘knowledge’ to investigate how knowledge is transferred during distillation, focusing on shared invariant outputs to counterfactual changes of dataset latent variables (we call these latents mechanisms). We define a student model to be a good stand-in model for a teacher if it shares the teacher’s learned mechanisms, and find that Jacobian matching and contrastive representation learning are viable methods by which to train such models. While these methods do not result in perfect transfer of mechanisms, we show they often improve student fidelity or mitigate simplicity bias (as measured by the teacher-to-student KL divergence and accuracy on various out-of-distribution test datasets), especially on datasets with spurious statistical correlations.
2024-05-14
Proceedings of UniReps: the First Workshop on Unifying Representations in Neural Models (published)
proceedings.mlr.press
openreview.net
ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning
Qiao Gu
Alihusein Kuwajerwala
Sacha Morin
Krishna Murthy
Bipasha Sen
Aditya Agarwal
Corban Rivera
William Paul
Kirsty Ellis
Rama Chellappa
Chuang Gan
Celso M de Melo
Joshua B. Tenenbaum
Antonio Torralba
Florian Shkurti
Liam Paull
For robots to perform a wide variety of tasks, they require a 3D representation of the world that is semantically rich, yet compact and effi… (see more)cient for task-driven perception and planning. Recent approaches have attempted to leverage features from large vision-language models to encode semantics in 3D representations. However, these approaches tend to produce maps with per-point feature vectors, which do not scale well in larger environments, nor do they contain semantic spatial relationships between entities in the environment, which are useful for downstream planning. In this work, we propose ConceptGraphs, an open-vocabulary graph-structured representation for 3D scenes. ConceptGraphs is built by leveraging 2D foundation models and fusing their output to 3D by multi-view association. The resulting representations generalize to novel semantic classes, without the need to collect large 3D datasets or finetune models. We demonstrate the utility of this representation through a number of downstream planning tasks that are specified through abstract (language) prompts and require complex reasoning over spatial and semantic concepts. (Project page: https://concept-graphs.github.io/ Explainer video: https://youtu.be/mRhNkQwRYnc )
2024-05-13
2024 IEEE International Conference on Robotics and Automation (ICRA) (published)
doi.org
openreview.net
Globally Stable Neural Imitation Policies
Amin Abyaneh
Mariana Sosa Guzmán
Hsiu-Chin Lin
2024-05-13
2024 IEEE International Conference on Robotics and Automation (ICRA) (published)
doi.org
arxiv.org
Reinforcement Learning for Blind Stair Climbing with Legged and Wheeled-Legged Robots
Simon Chamorro
Victor Klemm
Miguel de La Iglesia Valls
Chris Pal
Roland Siegwart
2024-05-13
2024 IEEE International Conference on Robotics and Automation (ICRA) (published)
doi.org
arxiv.org
TEMPLATES: Characterization of a Merger in the Dusty Lensing SPT0418-47 System
Jared Cathey
Anthony H. Gonzalez
Sidney Lower
Kedar A. Phadke
Justin Spilker
Manuel Aravena
Matthew Bayliss
Jack E. Birkin
Simon Birrer
Scott Chapman
Håkon Dahle
Christopher C. Hayward
Yashar Hezaveh
Ryley Hill
Taylor A. Hutchison
Keunho J. Kim
Guillaume Mahler
Daniel P. Marrone
Desika Narayanan
Alexander Navarre … (see 7 more)
Cassie Reuter
Jane R Rigby
Keren Sharon
Manuel Solimano
Nikolaus Sulzenauer
Joaquin Vieira
David Vizgan
2024-05-13
The Astrophysical Journal (published)
doi.org
arxiv.org
Uncertainty-aware hybrid paradigm of nonlinear MPC and model-based RL for offroad navigation: Exploration of transformers in the predictive model
Faraz Lotfi
Khalil Virji
Farnoosh Faraji
Lucas Berry
Andrew Holliday
David Meger
Gregory Dudek
In this paper, we investigate a hybrid scheme that combines nonlinear model predictive control (MPC) and model-based reinforcement learning … (see more)(RL) for navigation planning of an autonomous model car across offroad, unstructured terrains without relying on predefined maps. Our innovative approach takes inspiration from BADGR, an LSTM-based network that primarily concentrates on environment modeling, but distinguishes itself by substituting LSTM modules with transformers to greatly elevate the performance our model. Addressing uncertainty within the system, we train an ensemble of predictive models and estimate the mutual information between model weights and outputs, facilitating dynamic horizon planning through the introduction of variable speeds. Further enhancing our methodology, we incorporate a nonlinear MPC controller that accounts for the intricacies of the vehicle's model and states. The model-based RL facet produces steering angles and quantifies inherent uncertainty. At the same time, the nonlinear MPC suggests optimal throttle settings, striking a balance between goal attainment speed and managing model uncertainty influenced by velocity. In the conducted studies, our approach excels over the existing baseline by consistently achieving higher metric values in predicting future events and seamlessly integrating the vehicle's kinematic model for enhanced decision-making. The code and the evaluation data are available at https://github.com/FARAZLOTFI/offroad_autonomous_navigation/).
2024-05-13
2024 IEEE International Conference on Robotics and Automation (ICRA) (published)
doi.org
arxiv.org
An AI-Resilient Text Rendering Technique for Reading and Skimming Documents
Ziwei Gu
Ian Arawjo
Kenneth Li
Jonathan K. Kummerfeld
Elena L. Glassman
2024-05-11
Proceedings of the CHI Conference on Human Factors in Computing Systems (published)
doi.org
arxiv.org