Publications

Learning conditional policies for crystal design using offline reinforcement learning
Prashant Govindarajan
Santiago Miret
Jarrid Rector-Brooks
Mariano Phielipp
Janarthanan Rajendran
Sarath Chandar
Conservative Q-learning for band-gap conditioned crystal design with DFT evaluations – the model is trained on trajectories constructed fr… (voir plus)om crystals in the Materials Project. Results indicate promising performance for lower band gap targets.
2023-12-31
Digital Discovery (publié)
doi.org
openreview.net
Learning Lagrangian Multipliers for the Travelling Salesman Problem
Augustin Parjadis
Quentin Cappart
Bistra Dilkina
Aaron M. Ferber
Louis-Martin Rousseau
2023-12-31
CP (publié)
doi.org
arxiv.org
Learning Precedences for Scheduling Problems with Graph Neural Networks
Hélène Verhaeghe
Quentin Cappart
Gilles Pesant
Claude-Guy Quimper
2023-12-31
CP (publié)
doi.org
On learning Whittle index policy for restless bandits with scalable regret
Nima Akbarzadeh
Aditya Mahajan
Reinforcement learning is an attractive approach to learn good resource allocation and scheduling policies based on data when the system mod… (voir plus)el is unknown. However, the cumulative regret of most RL algorithms scales as ˜ O(S
2023-12-31
IEEE Transactions on Control of Network Systems (publié)
doi.org
arxiv.org
Low-Dimensional Embeddings of High-Dimensional Data: Algorithms and Applications (Dagstuhl Seminar 24122).
Dmitry Kobak
Fred Hamprecht
Smita Krishnaswamy
Gal Mishne
Sebastian Damrich
2023-12-31
Dagstuhl Reports (publié)
doi.org
Machine Translation Hallucination Detection for Low and High Resource Languages using Large Language Models
Kenza Benkirane
Laura Gongas
Shahar Pelles
Naomi Fuchs
Joshua Darmon
Pontus Stenetorp
David Ifeoluwa Adelani
Eduardo Sánchez
Meta
2023-12-31
EMNLP (Findings) (publié)
doi.org
openreview.net
MagicClay: Sculpting Meshes With Generative Neural Fields
Amir Barda
Vladimir Kim
Noam Aigerman
Amit H. Bermano
Thibault Groueix
The recent developments in neural fields have brought phenomenal capabilities to the field of shape generation, but they lack crucial proper… (voir plus)ties, such as incremental control - a fundamental requirement for artistic work. Triangular meshes, on the other hand, are the representation of choice for most geometry related tasks, offering efficiency and intuitive control, but do not lend themselves to neural optimization. To support downstream tasks, previous art typically proposes a two-step approach, where first a shape is generated using neural fields, and then a mesh is extracted for further processing. Instead, in this paper we introduce a hybrid approach that maintains both a mesh and a Signed Distance Field (SDF) representations consistently. Using this representation, we introduce MagicClay - an artist friendly tool for sculpting regions of a mesh according to textual prompts while keeping other regions untouched. Our framework carefully and efficiently balances consistency between the representations and regularizations in every step of the shape optimization; Relying on the mesh representation, we show how to render the SDF at higher resolutions and faster. In addition, we employ recent work in differentiable mesh reconstruction to adaptively allocate triangles in the mesh where required, as indicated by the SDF. Using an implemented prototype, we demonstrate superior generated geometry compared to the state-of-the-art, and novel consistent control, allowing sequential prompt-based edits to the same mesh for the first time.
2023-12-31
SIGGRAPH Asia (publié)
doi.org
arxiv.org
McGill NLP Group Submission to the MRL 2024 Shared Task: Ensembling Enhances Effectiveness of Multilingual Small LMs
Senyu Li
Hao Yu
Jessica Ojo
David Ifeoluwa Adelani
We present our systems for the three tasks and five languages included in the MRL 2024 Shared Task on Multilingual Multi-task Information Re… (voir plus)trieval: (1) Named Entity Recognition, (2) Free-form Question Answering, and (3) Multiple-choice Question Answering. For each task, we explored the impact of selecting different multilingual language models for fine-tuning across various target languages, and implemented an ensemble system that generates final outputs based on predictions from multiple fine-tuned models. All models are large language models fine-tuned on task-specific data. Our experimental results show that a more balanced dataset would yield better results. However, when training data for certain languages are scarce, fine-tuning on a large amount of English data supplemented by a small amount of “triggering data” in the target language can produce decent results.
2023-12-31
MRL (publié)
doi.org
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems
Tom'as Gonz'alez
Crist'obal Guzm'an
Courtney Paquette
2023-12-31
COLT (publié)
doi.org
arxiv.org
Mitigating Translationese in Low-resource Languages: The Storyboard Approach
Garry Kuwanto
Eno-Abasi Urua
Priscilla A. Amuok
Shamsuddeen Hassan Muhammad
Aremu Anuoluwapo
Verrah Akinyi Otiende
Loice Emma Nanyanga
T. Nyoike
A. D. Akpan
Nsima Ab Udouboh
Idongesit Udeme Archibong
Idara Effiong Moses
Ifeoluwatayo A. Ige
Benjamin A. Ajibade
Olumide Benjamin Awokoya
Idris Abdulmumin
Saminu Mohammad Aliyu
Ruqayya Nasir Iro
Ibrahim Ahmad
Deontae Smith … (voir 4 de plus)
Praise-EL Michaels
David Ifeoluwa Adelani
Derry Tanti Wijaya
Anietie U Andy
Low-resource languages often face challenges in acquiring high-quality language data due to the reliance on translation-based methods, which… (voir plus) can introduce the translationese effect. This phenomenon results in translated sentences that lack fluency and naturalness in the target language. In this paper, we propose a novel approach for data collection by leveraging storyboards to elicit more fluent and natural sentences. Our method involves presenting native speakers with visual stimuli in the form of storyboards and collecting their descriptions without direct exposure to the source text. We conducted a comprehensive evaluation comparing our storyboard-based approach with traditional text translation-based methods in terms of accuracy and fluency. Human annotators and quantitative metrics were used to assess translation quality. The results indicate a preference for text translation in terms of accuracy, while our method demonstrates worse accuracy but better fluency in the language focused.
2023-12-31
International Conference on Language Resources and Evaluation (publié)
doi.org
arxiv.org
Model-based graph reinforcement learning for inductive traffic signal control
François-Xavier Devailly
Denis Larocque
Laurent Charlin
Most reinforcement learning methods for adaptive-traffic-signal-control require training from scratch to be applied on any new intersection … (voir plus)or after any modification to the road network, traffic distribution, or behavioral constraints experienced during training. Considering 1) the massive amount of experience required to train such methods, and 2) that experience must be gathered by interacting in an exploratory fashion with real road-network-users, such a lack of transferability limits experimentation and applicability. Recent approaches enable learning policies that generalize for unseen road-network topologies and traffic distributions, partially tackling this challenge. However, the literature remains divided between the learning of cyclic (the evolution of connectivity at an intersection must respect a cycle) and acyclic (less constrained) policies, and these transferable methods 1) are only compatible with cyclic constraints and 2) do not enable coordination. We introduce a new model-based method, MuJAM, which, on top of enabling explicit coordination at scale for the first time, pushes generalization further by allowing a generalization to the controllers' constraints. In a zero-shot transfer setting involving both road networks and traffic settings never experienced during training, and in a larger transfer experiment involving the control of 3,971 traffic signal controllers in Manhattan, we show that MuJAM, using both cyclic and acyclic constraints, outperforms domain-specific baselines as well as another transferable approach.
2023-12-31
IEEE Open Journal of Intelligent Transportation Systems (publié)
doi.org
arxiv.org
Multidomain Object Detection Framework Using Feature Domain Knowledge Distillation.
Da-Wei Jaw
Shih-Chia Huang
Zhihui Lu
Benjamin C. M. Fung
Sy-Yen Kuo
Object detection techniques have been widely studied, utilized in various works, and have exhibited robust performance on images with suffic… (voir plus)ient luminance. However, these approaches typically struggle to extract valuable features from low-luminance images, which often exhibit blurriness and dim appearence, leading to detection failures. To overcome this issue, we introduce an innovative unsupervised feature domain knowledge distillation (KD) framework. The proposed framework enhances the generalization capability of neural networks across both low-and high-luminance domains without incurring additional computational costs during testing. This improvement is made possible through the integration of generative adversarial networks and our proposed unsupervised KD process. Furthermore, we introduce a region-based multiscale discriminator designed to discern feature domain discrepancies at the object level rather than from the global context. This bolsters the joint learning process of object detection and feature domain distillation tasks. Both qualitative and quantitative assessments shown that the proposed method, empowered by the region-based multiscale discriminator and the unsupervised feature domain distillation process, can effectively extract beneficial features from low-luminance images, outperforming other state-of-the-art approaches in both low-and sufficient-luminance domains.
2023-12-31
IEEE Trans. Cybern. (publié)
doi.org