Publications

RapidBrachyTG43: A Geant4‐based TG‐43 parameter and dose calculation module for brachytherapy dosimetry
Jonathan Kalinowski
Shirin A. Enger
2024-01-22
Medical Physics (publié)
doi.org
Transnational conservation to anticipate future plant shifts in Europe
Yohann Chauvier-Mendes
Laura J. Pollock
Peter H. Verburg
Dirk N. Karger
Loïc Pellissier
Sébastien Lavergne
Niklaus E. Zimmermann
Wilfried Thuiller
2024-01-22
Nature Ecology & Evolution (publié)
doi.org
Gaining Biological Insights through Supervised Data Visualization
Jake S. Rhodes
Adrien Aumon
Sacha Morin
Marc Girard
Catherine Larochelle
Boaz Lahav
Elsa Brunet-Ratnasingham
Amélie Pagliuzza
Lorie Marchitto
Wei Zhang
Adele Cutler
F. Grand'Maison
Anhong Zhou
Andrés Finzi
Nicolas Chomont
Daniel E. Kaufmann
Stephanie Zandee
Alexandre Prat
Guy Wolf
Kevin R. Moon
Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHAT… (voir plus)E, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATE’s prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.
2024-01-21
bioRxiv (prépublication)
doi.org
EMA-Net: Efficient Multitask Affinity Learning for Dense Scene Predictions
Dimitrios Sinodinos
Narges Armanfard
2024-01-20
ArXiv (prépublication)
doi.org
arxiv.org
PhotoBot: Reference-Guided Interactive Photography via Natural Language
Oliver Limoyo
Jimmy Li
Dmitriy Rivkin
Jonathan Kelly
Gregory Dudek
We introduce PhotoBot, a framework for fully automated photo acquisition based on an interplay between high-level human language guidance an… (voir plus)d a robot photographer. We propose to communicate photography suggestions to the user via reference images that are selected from a curated gallery. We leverage a visual language model (VLM) and an object detector to characterize the reference images via textual descriptions and then use a large language model (LLM) to retrieve relevant reference images based on a user's language query through text-based reasoning. To correspond the reference image and the observed scene, we exploit pre-trained features from a vision transformer capable of capturing semantic similarity across marked appearance variations. Using these features, we compute pose adjustments for an RGB-D camera by solving a perspective-n-point (PnP) problem. We demonstrate our approach using a manipulator equipped with a wrist camera. Our user studies show that photos taken by PhotoBot are often more aesthetically pleasing than those taken by users themselves, as measured by human feedback. We also show that PhotoBot can generalize to other reference sources such as paintings.
2024-01-19
ArXiv (prépublication)
doi.org
arxiv.org
Deployable Reinforcement Learning with Variable Control Rate
Dong Wang
Giovanni Beltrame
2024-01-17
ArXiv (prépublication)
doi.org
arxiv.org
METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII
Burak Kocak
Tugba Akinci D’Antonoli
Nathaniel Mercaldo
Angel Alberich-Bayarri
Bettina Baessler
Ilaria Ambrosini
Anna E. Andreychenko
Spyridon Bakas
Regina G. H. Beets-Tan
Keno Bressem
Irene Buvat
Roberto Cannella
Luca Alessandro Cappellini
Armando Ugo Cavallo
Leonid L. Chepelev
Linda Chi Hang Chu
Aydin Demircioglu
Nandita M. deSouza
Matthias Dietzel
Salvatore Claudio Fanni … (voir 40 de plus)
Andrey Fedorov
Laure S. Fournier
Valentina Giannini
Rossano Girometti
Kevin B. W. Groot Lipman
Georgios Kalarakis
Brendan S. Kelly
Michail E. Klontzas
Dow-Mu Koh
Elmar Kotter
Ho Yun Lee
Mario Maas
Luis Marti-Bonmati
Henning Müller
Nancy Obuchowski
Fanny Orlhac
Nikolaos Papanikolaou
Ekaterina Petrash
Elisabeth Pfaehler
Daniel Pinto dos Santos
Andrea Ponsiglione
Sebastià Sabater
Francesco Sardanelli
Philipp Seeböck
Nanna M. Sijtsema
Arnaldo Stanzione
Alberto Traverso
Lorenzo Ugga
Martin Vallières
Lisanne V. van Dijk
Joost J. M. van Griethuysen
Robbert W. van Hamersvelt
Peter van Ooijen
Federica Vernuccio
Alan Wang
Stuart Williams
Jan Witowski
Zhongyi Zhang
Alex Zwanenburg
Renato Cuocolo
2024-01-17
Insights into Imaging (publié)
doi.org
Amortizing intractable inference in large language models
Edward J Hu
Moksh J. Jain
Eric Elmoznino
Younesse Kaddar
Guillaume Lajoie
Yoshua Bengio
Nikolay Malkin
Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This l… (voir plus)imits tractable querying of this knowledge to start-to-end autoregressive sampling. However, many tasks of interest -- including sequence continuation, infilling, and other forms of constrained generation -- involve sampling from intractable posterior distributions. We address this limitation by using amortized Bayesian inference to sample from these intractable posteriors. Such amortization is algorithmically achieved by fine-tuning LLMs via diversity-seeking reinforcement learning algorithms: generative flow networks (GFlowNets). We empirically demonstrate that this distribution-matching paradigm of LLM fine-tuning can serve as an effective alternative to maximum-likelihood training and reward-maximizing policy optimization. As an important application, we interpret chain-of-thought reasoning as a latent variable modeling problem and demonstrate that our approach enables data-efficient adaptation of LLMs to tasks that require multi-step rationalization and tool use.
2024-01-16
ICLR.cc/2024/Conference (présentation orale)
doi.org
openreview.net
Balancing Act: Constraining Disparate Impact in Sparse Models
Meraj Hashemizadeh
Juan Ramirez
Rohan Sukumaran
Golnoosh Farnadi
Simon Lacoste-Julien
Jose Gallego-Posada
Model pruning is a popular approach to enable the deployment of large deep learning models on edge devices with restricted computational or … (voir plus)storage capacities. Although sparse models achieve performance comparable to that of their dense counterparts at the level of the entire dataset, they exhibit high accuracy drops for some data sub-groups. Existing methods to mitigate this disparate impact induced by pruning (i) rely on surrogate metrics that address the problem indirectly and have limited interpretability; or (ii) scale poorly with the number of protected sub-groups in terms of computational cost. We propose a constrained optimization approach that directly addresses the disparate impact of pruning: our formulation bounds the accuracy change between the dense and sparse models, for each sub-group. This choice of constraints provides an interpretable success criterion to determine if a pruned model achieves acceptable disparity levels. Experimental results demonstrate that our technique scales reliably to problems involving large models and hundreds of protected sub-groups.
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Bridging State and History Representations: Understanding Self-Predictive RL
Tianwei Ni
Benjamin Eysenbach
Erfan SeyedSalehi
Michel Ma
Clement Gehring
Aditya Mahajan
Pierre-Luc Bacon
Representations are at the core of all deep reinforcement learning (RL) methods for both Markov decision processes (MDPs) and partially obse… (voir plus)rvable Markov decision processes (POMDPs). Many representation learning methods and theoretical frameworks have been developed to understand what constitutes an effective representation. However, the relationships between these methods and the shared properties among them remain unclear. In this paper, we show that many of these seemingly distinct methods and frameworks for state and history abstractions are, in fact, based on a common idea of self-predictive abstraction. Furthermore, we provide theoretical insights into the widely adopted objectives and optimization, such as the stop-gradient technique, in learning self-predictive representations. These findings together yield a minimalist algorithm to learn self-predictive representations for states and histories. We validate our theories by applying our algorithm to standard MDPs, MDPs with distractors, and POMDPs with sparse rewards. These findings culminate in a set of preliminary guidelines for RL practitioners.
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Closing the Gap between TD Learning and Supervised Learning -- A Generalisation Point of View.
Raj Ghugare
Matthieu Geist
Glen Berseth
Benjamin Eysenbach
Some reinforcement learning (RL) algorithms have the capability of recombining together pieces of previously seen experience to solve a task… (voir plus) never seen before during training. This oft-sought property is one of the few ways in which dynamic programming based RL algorithms are considered different from supervised learning (SL) based RL algorithms. Yet, recent RL methods based on off-the-shelf SL algorithms achieve excellent results without an explicit mechanism for stitching; it remains unclear whether those methods forgo this important stitching property. This paper studies this question in the setting of goal-reaching problems. We show that the desirable stitching property corresponds to a form of generalization: after training on a distribution of (state, goal) pairs, one would like to evaluate on (state, goal) pairs not seen \emph{together} in the training data. Our analysis shows that this sort of generalization is different from \emph{i.i.d.} generalization. This connection between stitching and generalization reveals why we should not expect existing RL methods based on SL to perform stitching, even in the limit of large datasets and models. We experimentally validate this result on carefully constructed datasets. This connection suggests a simple remedy, the same remedy for improving generalization in supervised learning: data augmentation. We propose a naive \emph{temporal} data augmentation approach and demonstrate that adding it to RL methods based on SL enables them to stitch together experience so that they succeed in navigating between states and goals unseen together during training.
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Closing the Gap between TD Learning and Supervised Learning - A Generalisation Point of View
Raj Ghugare
Matthieu Geist
Glen Berseth
Benjamin Eysenbach
Some reinforcement learning (RL) algorithms can stitch pieces of experience to solve a task never seen before during training. This oft-soug… (voir plus)ht property is one of the few ways in which RL methods based on dynamic-programming differ from RL methods based on supervised-learning (SL). Yet, certain RL methods based on off-the-shelf SL algorithms achieve excellent results without an explicit mechanism for stitching; it remains unclear whether those methods forgo this important stitching property. This paper studies this question for the problems of achieving a target goal state and achieving a target return value. Our main result is to show that the stitching property corresponds to a form of combinatorial generalization: after training on a distribution of (state, goal) pairs, one would like to evaluate on (state, goal) pairs not seen together in the training data. Our analysis shows that this sort of generalization is different from i.i.d. generalization. This connection between stitching and generalisation reveals why we should not expect SL-based RL methods to perform stitching, even in the limit of large datasets and models. Based on this analysis, we construct new datasets to explicitly test for this property, revealing that SL-based methods lack this stitching property and hence fail to perform combinatorial generalization. Nonetheless, the connection between stitching and combinatorial generalisation also suggests a simple remedy for improving generalisation in SL: data augmentation. We propose a temporal data augmentation and demonstrate that adding it to SL-based methods enables them to successfully complete tasks not seen together during training. On a high level, this connection illustrates the importance of combinatorial generalization for data efficiency in time-series data beyond tasks beyond RL, like audio, video, or text.
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net