Publications

Refining SARS-CoV-2 Intra-host Variation by Leveraging Large-scale Sequencing Data
Fatima Mostefai
Jean-Christophe Grenier
Raphael Poujol
Julie Hussin
2024-05-01
bioRxiv (preprint)
doi.org
Robust Data-driven Prescriptiveness Optimization
Mehran Poursoltani
Érick Delage
Angelos Georghiou
2024-05-01
ICML.cc/2024/Conference (poster)
doi.org
openreview.net
Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features
Aleksandr Beznosikov
David Dobre
Gauthier Gidel
2024-05-01
ICML.cc/2024/Conference (poster)
doi.org
openreview.net
A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries
Zeinab Sherkatghanad
Amin Ghazanfari
Vladimir Makarenkov
2024-05-01
Journal of Energy Storage (published)
doi.org
SelfIE: Self-Interpretation of Large Language Model Embeddings
Haozhe Chen
Carl Vondrick
Chengzhi Mao
How do large language models (LLMs) obtain their answers? The ability to explain and control an LLM's reasoning process is key for reliabili… (see more)ty, transparency, and future model developments. We propose SelfIE (Self-Interpretation of Embeddings), a framework that enables LLMs to interpret their own embeddings in natural language by leveraging their ability to respond to inquiries about a given passage. Capable of interpreting open-world concepts in the hidden embeddings, SelfIE reveals LLM internal reasoning in cases such as making ethical decisions, internalizing prompt injection, and recalling harmful knowledge. SelfIE's text descriptions on hidden embeddings also open up new avenues to control LLM reasoning. We propose Supervised Control, which allows editing open-ended concepts while only requiring gradient computation of individual layer. We extend RLHF to hidden embeddings and propose Reinforcement Control that erases harmful knowledge in LLM without supervision targets.
2024-05-01
ICML.cc/2024/Conference (poster)
doi.org
openreview.net
Stochastic positional embeddings improve masked image modeling
Amir Bar
Florian Bordes
Assaf Shocher
Mahmoud Assran
Pascal Vincent
Nicolas Ballas
Trevor Darrell
Amir Globerson
Yann LeCun
Masked Image Modeling (MIM) is a promising self-supervised learning approach that enables learning from unlabeled images. Despite its recent… (see more) success, learning good representations through MIM remains challenging because it requires predicting the right semantic content in accurate locations. For example, given an incomplete picture of a dog, we can guess that there is a tail, but we cannot determine its exact location. In this work, we propose to incorporate location uncertainty into MIM by using stochastic positional embeddings (StoP). Specifically, we condition the model on stochastic masked token positions drawn from a Gaussian distribution. StoP reduces overfitting to location features and guides the model toward learning features that are more robust to location uncertainties. Quantitatively, StoP improves downstream MIM performance on a variety of downstream tasks, including
2024-05-01
ICML.cc/2024/Conference (poster)
openreview.net
openreview.net
Stop Regressing: Training Value Functions via Classification for Scalable Deep RL
Jesse Farebrother
Jordi Orbay
Quan Vuong
Adrien Ali Taiga
Yevgen Chebotar
Ted Xiao
Alex Irpan
Sergey Levine
Pablo Samuel Castro
Aleksandra Faust
Aviral Kumar
Rishabh Agarwal
2024-05-01
ICML.cc/2024/Conference (oral)
doi.org
openreview.net
A Tensor Decomposition Perspective on Second-order RNNs
Maude Lizaire
Michael Rizvi-Martel
Marawan Gamal
Guillaume Rabusseau
2024-05-01
ICML.cc/2024/Conference (spotlight)
proceedings.mlr.press
openreview.net
WebLINX: Real-World Website Navigation with Multi-Turn Dialogue
Xing Han Lu
Zdeněk Kasner
Siva Reddy
2024-05-01
ICML.cc/2024/Conference (spotlight)
doi.org
openreview.net
Semantically Consistent Video Inpainting with Conditional Diffusion Models
Dylan Green
William Harvey
Saeid Naderiparizi
Matthew Niedoba
Yunpeng Liu
Xiaoxuan Liang
Jonathan Wilder Lavington
Ke Zhang
Vasileios Lioutas
Setareh Dabiri
Adam Ścibior
Berend Zwartsenberg
Frank Wood
Current state-of-the-art methods for video inpainting typically rely on optical flow or attention-based approaches to inpaint masked regions… (see more) by propagating visual information across frames. While such approaches have led to significant progress on standard benchmarks, they struggle with tasks that require the synthesis of novel content that is not present in other frames. In this paper we reframe video inpainting as a conditional generative modeling problem and present a framework for solving such problems with conditional video diffusion models. We highlight the advantages of using a generative approach for this task, showing that our method is capable of generating diverse, high-quality inpaintings and synthesizing new content that is spatially, temporally, and semantically consistent with the provided context.
2024-04-30
ArXiv (preprint)
doi.org
arxiv.org
Sequential predictive learning is a unifying theory for hippocampal representation and replay
Daniel Levenstein
Aleksei Efremov
Roy Henha Eyono
Adrien Peyrache
Blake Richards
The mammalian hippocampus contains a cognitive map that represents an animal’s position in the environment 1 and generates offline “repl… (see more)ay” 2,3 for the purposes of recall 4, planning 5,6, and forming long term memories 7. Recently, it’s been found that artificial neural networks trained to predict sensory inputs develop spatially tuned cells 8, aligning with predictive theories of hippocampal function 9–11. However, whether predictive learning can also account for the ability to produce offline replay is unknown. Here, we find that spatially tuned cells, which robustly emerge from all forms of predictive learning, do not guarantee the presence of a cognitive map with the ability to generate replay. Offline simulations only emerged in networks that used recurrent connections and head-direction information to predict multi-step observation sequences, which promoted the formation of a continuous attractor reflecting the geometry of the environment. These offline trajectories were able to show wake-like statistics, autonomously replay recently experienced locations, and could be directed by a virtual head direction signal. Further, we found that networks trained to make cyclical predictions of future observation sequences were able to rapidly learn a cognitive map and produced sweeping representations of future positions reminiscent of hippocampal theta sweeps 12. These results demonstrate how hippocampal-like representation and replay can emerge in neural networks engaged in predictive learning, and suggest that hippocampal theta sequences reflect a circuit that implements a data-efficient algorithm for sequential predictive learning. Together, this framework provides a unifying theory for hippocampal functions and hippocampal-inspired approaches to artificial intelligence.
2024-04-29
bioRxiv (preprint)
doi.org
Spinal cord perfusion impairments in the M83 mouse model of Parkinson’s disease
Benjamin F. Combes
Sandeep Kumar Kalva
Pierre-Louis Benveniste
Agathe Tournant
Man Hoi Law
Joshua Newton
Maik Krüger
Rebecca Z. Weber
Inês Dias
Daniela Noain
Xose Luis Dean-Ben
Uwe Konietzko
Christian R. Baumann
Per-Göran Gillberg
Christoph Hock
Roger M. Nitsch
Julien Cohen-Adad
Daniel Razansky
Ruiqing Ni
Metabolism and bioenergetics in the central nervous system play important roles in the pathophysiology of Parkinson’s disease (PD). Here, … (see more)we employed a multimodal imaging approach to assess oxygenation changes in the spinal cord of a transgenic M83 murine model of PD in comparison to non-transgenic littermates at 9-12 months-of-age. A lower oxygen saturation (SO2)SVOT was detected in vivo with spiral volumetric optoacoustic tomography (SVOT) in the spinal cord of M83 mice compared to non-transgenic littermate mice. Ex-vivo high-field T1-weighted magnetic resonance imaging (MRI) and immunostaining for alpha-synuclein (phospho-S129) and vascular organisation (CD31 and GLUT1) were used to investigate the nature of the abnormalities detected via in vivo imaging. Ex-vivo analysis showed that the vascular network in the spinal cord was not impaired in the spinal cord of M83 mice. Ex-vivo MRI assisted with deep learning-based automatic segmentation showed no volumetric atrophy in the spinal cord of M83 mice compared to non-transgenic littermates, whereas nuclear alpha-synuclein phosphorylated at Ser129 site could be linked to early pathology and metabolic dysfunction. The proposed and validated non-invasive high-resolution imaging tool to study oxygen saturation in the spinal cord of PD mice holds promise for assessing early changes preceding motor deficits in PD mice.
2024-04-29
bioRxiv (preprint)
doi.org