Publications

Reassessing Speech Translation for Low-Resource Languages: Do LLMs Redefine the State-of-the-Art Against Cascaded Models?
Jonah Dauvet
Min Ma
Jessica Ojo
David Ifeoluwa Adelani
2024-12-31
Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025) (published)
doi.org
Recovering Dantzig–Wolfe Bounds by Cutting Planes
Ying Chen
Oktay Günlük
Andrea Lodi
Leveraging Dantzig–Wolfe Decomposition in the Original Variable Space for Mixed-Integer Programming Dantzig–Wolfe decomposition has been… (see more) extensively applied to solve large-scale mixed-integer programs with decomposable structures, leading to exact solution approaches, such as branch and price. However, these approaches would require solving the problem in an extended variable space and are not readily present in off-the-shelf solvers. In “Recovering Dantzig–Wolfe Bounds by Cutting Planes,” Chen, Günlük, and Lodi propose a computational effective approach for generating cutting planes from Dantzig–Wolfe decomposition to enhance branch and cut in the space of original variables. The proposed approach requires a relatively small number of cutting planes to recover the strength of the Dantzig–Wolfe dual bound and should be easy to implement in general-purpose mixed-integer programming solvers. The authors show that these cutting planes typically lead to a formulation with lower dual degeneracy and hence, a better computational performance than naïve approaches, such as the objective function cut.
2024-12-31
Oper. Res. (published)
doi.org
arxiv.org
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control
Zhongyu Li
Xue Bin Peng
Pieter Abbeel
Sergey Levine
Glen Berseth
Koushil Sreenath
This paper presents a comprehensive study on using deep reinforcement learning (RL) to create dynamic locomotion controllers for bipedal rob… (see more)ots. Going beyond focusing on a single locomotion skill, we develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. Our RL-based controller incorporates a novel dual-history architecture, utilizing both a long-term and short-term input/output (I/O) history of the robot. This control architecture, when trained through the proposed end-to-end RL approach, consistently outperforms other methods across a diverse range of skills in both simulation and the real world. The study also delves into the adaptivity and robustness introduced by the proposed RL system in developing locomotion controllers. We demonstrate that the proposed architecture can adapt to both time-invariant dynamics shifts and time-variant changes, such as contact events, by effectively using the robot's I/O history. Additionally, we identify task randomization as another key source of robustness, fostering better task generalization and compliance to disturbances. The resulting control policies can be successfully deployed on Cassie, a torque-controlled human-sized bipedal robot. This work pushes the limits of agility for bipedal robots through extensive real-world experiments. We demonstrate a diverse range of locomotion skills, including: robust standing, versatile walking, fast running with a demonstration of a 400-meter dash, and a diverse set of jumping skills, such as standing long jumps and high jumps.
2024-12-31
Int. J. Robotics Res. (published)
doi.org
arxiv.org
Reliability Assessment of Distribution Systems With Microgrids Using Discrete-Time Markov Chains
Jean-William Lauzon
Ilhan Kocar
Antoine Lesage-Landry
Microgrids can improve the reliability and resiliency of modern distribution systems. The stochasticity of local non-dispatchable distribute… (see more)d energy resources (NDDERs), combined with the time-dependency of battery energy storage systems (BESSs) and load shedding strategies (LSSs), complicates the reliability assessment of distribution networks embedded with microgrids. In this work, we propose a minimal cut-set method using a discrete-time Markov chain to perform the time-series adequacy assessment. Our method offers an alternative to sequential Monte Carlo simulations (SMCSs) to account for the stochasticity of NDDERs and the time-dependency of BESSs and LSSs. Case studies on modified IEEE-RTBS Bus2 and IEEE 123-Test Feeder systems assess the accuracy of the method when compared with SMCSs.
2024-12-31
IEEE Transactions on Power Delivery (published)
doi.org
Representing Positional Information in Generative World Models for Object Manipulation
Stefano Ferraro
Pietro Mazzaglia
Tim Verbelen
Bart Dhoedt
Sai Rajeswar
Object manipulation capabilities are essential skills that set apart embodied agents engaging with the world, especially in the realm of rob… (see more)otics. The ability to predict outcomes of interactions with objects is paramount in this setting. While model-based control methods have started to be employed for tackling manipulation tasks, they have faced challenges in accurately manipulating objects. As we analyze the causes of this limitation, we identify the cause of underperformance in the way current world models represent crucial positional information, especially about the target's goal specification for object positioning tasks. We introduce a general approach that empowers world model-based agents to effectively solve object-positioning tasks. We propose two declinations of this approach for generative world models: position-conditioned (PCP) and latent-conditioned (LCP) policy learning. In particular, LCP employs object-centric latent representations that explicitly capture object positional information for goal specification. This naturally leads to the emergence of multimodal capabilities, enabling the specification of goals through spatial coordinates or a visual goal. Our methods are rigorously evaluated across several manipulation environments, showing favorable performance compared to current model-based control approaches.
2024-12-31
ECAI (published)
doi.org
arxiv.org
Retreever: Tree-Based Coarse-to-Fine Representations for Retrieval
Shubham Gupta
Zichao Li
Tianyi Chen
Cem Subakan
Siva Reddy
Perouz Taslakian
Valentina Zantedeschi
Document retrieval is a core component of question-answering systems, as it enables conditioning answer generation on new and large-scale co… (see more)rpora. While effective, the standard practice of encoding documents into high-dimensional embeddings for similarity search entails large memory and compute footprints, and also makes it hard to inspect the inner workings of the system. In this paper, we propose a tree-based method for organizing and representing reference documents at various granular levels, which offers the flexibility to balance cost and utility, and eases the inspection of the corpus content and retrieval operations. Our method, called ReTreever, jointly learns a routing function per internal node of a binary tree such that query and reference documents are assigned to similar tree branches, hence directly optimizing for retrieval performance. Our evaluations show that ReTreever generally preserves full representation accuracy. Its hierarchical structure further provides strong coarse representations and enhances transparency by indirectly learning meaningful semantic groupings. Among hierarchical retrieval methods, ReTreever achieves the best retrieval accuracy at the lowest latency, proving that this family of techniques can be viable in practical applications.
2024-12-31
arXiv (preprint)
doi.org
arxiv.org
Revisiting Data Augmentation for Ultrasound Images
Adam Tupper
Christian Gagné
Data augmentation is a widely used and effective technique to improve the generalization performance of deep neural networks. Yet, despite o… (see more)ften facing limited data availability when working with medical images, it is frequently underutilized. This appears to come from a gap in our collective understanding of the efficacy of different augmentation techniques across different tasks and modalities. One modality where this is especially true is ultrasound imaging. This work addresses this gap by analyzing the effectiveness of different augmentation techniques at improving model performance across a wide range of ultrasound image analysis tasks. To achieve this, we introduce a new standardized benchmark of 14 ultrasound image classification and semantic segmentation tasks from 10 different sources and covering 11 body regions. Our results demonstrate that many of the augmentations commonly used for tasks on natural images are also effective on ultrasound images, even more so than augmentations developed specifically for ultrasound images in some cases. We also show that diverse augmentation using TrivialAugment, which is widely used for natural images, is also effective for ultrasound images. Moreover, our proposed methodology represents a structured approach for assessing various data augmentations that can be applied to other contexts and modalities.
2024-12-31
Trans. Mach. Learn. Res. (published)
doi.org
openreview.net
Do Robot Snakes Dream like Electric Sheep? Investigating the Effects of Architectural Inductive Biases on Hallucination
Jerry Huang
Prasanna Parthasarathi
Mehdi Rezagholizadeh
Boxing Chen
A. Chandar
The growth in prominence of large language models (LLMs) in everyday life can be largely attributed to their generative abilities, yet some … (see more)of this is also owed to the risks and costs associated with their use. On one front is their tendency to \textit{hallucinate} false or misleading information, limiting their reliability. On another is the increasing focus on the computational limitations associated with traditional self-attention based LLMs, which has brought about new alternatives, in particular recurrent models, meant to overcome them. Yet it remains uncommon to consider these two concerns simultaneously. Do changes in architecture exacerbate/alleviate existing concerns about hallucinations? Do they affect how and where they occur? Through an extensive evaluation, we study how these architecture-based inductive biases affect the propensity to hallucinate. While hallucination remains a general phenomenon not limited to specific architectures, the situations in which they occur and the ease with which specific types of hallucinations can be induced can significantly differ based on the model architecture. These findings highlight the need for better understanding both these problems in conjunction with each other, as well as consider how to design more universal techniques for handling hallucinations.
2024-12-31
ACL (Findings) (published)
doi.org
arxiv.org
Robust Model Predictive Control for the Optimal Operation of the Indoor Environment of a Cluster of Smart Greenhouses
Ehsan Ghorbani
Ahmed Ouammi
Hanane Dagdougui
Smart greenhouses can be defined as cutting-edge technological systems that efficiently control indoor climate conditions to protect crops f… (see more)rom harsh outdoor conditions to increase their productivity. In this article, we developed and implemented a robust model predictive control approach that relies on a recursive state estimation method to cope with the impact of measurement and process signal errors. The aim of this approach is to optimally control the internal environment of intelligent greenhouses. A feedback policy problem is decomposing signals for the accessibility of uncertainties. Then, a robust feasibility set can be defined by determining the ellipsoid set on uncertainty to obtain solvable constrained optimization in the CPLEX solver. In the overall formulation, each greenhouse is considered as an independent element. This method can improve the quality of set-point tracking while reducing the computation time required to arrive at a solution. Extensive numerical simulations involving the application of an innovative and robust algorithm to a cluster of greenhouses were conducted to demonstrate the algorithm’s performance and effectiveness.
2024-12-31
IEEE Transactions on AgriFood Electronics (published)
doi.org
Robust prior-biased acquisition function for human-in-the-loop Bayesian optimization.
Rose Guay-Hottin
Lison Kardassevitch
Hugo Pham
Guillaume Lajoie
Marco Bonizzato
2024-12-31
Knowl. Based Syst. (published)
doi.org
Rootlets-based registration to the PAM50 spinal cord template
Sandrine Bédard
Jan Valosek
Valeria Oliva
Kenneth A. Weber II
Julien Cohen-Adad
Spinal cord functional MRI studies require precise localization of spinal levels for reliable voxelwise group analyses. Traditional template… (see more)-based registration of the spinal cord uses intervertebral discs for alignment. However, substantial anatomical variability across individuals exists between vertebral and spinal levels. This study proposes a novel registration approach that leverages spinal nerve rootlets to improve alignment accuracy and reproducibility across individuals. We developed a registration method leveraging dorsal cervical rootlets segmentation and aligning them non-linearly with the PAM50 spinal cord template. Validation was performed on a multi-subject, multi-site dataset (n=267, 44 sites) and a multi-subject dataset with various neck positions (n=10, 3 sessions). We further validated the method on task-based functional MRI (n=23) to compare group-level activation maps using rootlet-based registration to traditional disc-based methods. Rootlet-based registration showed superior alignment across individuals compared to the traditional disc-based method. Notably, rootlet positions were more stable across neck positions. Group-level analysis of task-based functional MRI using rootlet-based increased Z scores and activation cluster size compared to disc-based registration (number of active voxels from 3292 to 7978). Rootlet-based registration enhances both inter- and intra-subject anatomical alignment and yields better spatial normalization for group-level fMRI analyses. Our findings highlight the potential of rootlet-based registration to improve the precision and reliability of spinal cord neuroimaging group analysis.
2024-12-31
Imaging Neuroscience (published)
doi.org
arxiv.org
Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments
Luke Rowe
Roger Girgis
Anthony Gosselin
Liam Paull
Christopher Pal
Felix Heide
We introduce Scenario Dreamer, a fully data-driven generative simulator for autonomous vehicle planning that generates both the initial traf… (see more)fic scene - comprising a lane graph and agent bounding boxes - and closed-loop agent behaviours. Existing methods for generating driving simulation environments encode the initial traffic scene as a rasterized image and, as such, require parameter-heavy networks that perform unnecessary computation due to many empty pixels in the rasterized scene. Moreover, we find that existing methods that employ rule-based agent behaviours lack diversity and realism. Scenario Dreamer instead employs a novel vectorized latent diffusion model for initial scene generation that directly operates on the vectorized scene elements and an autoregressive Transformer for data-driven agent behaviour simulation. Scenario Dreamer additionally supports scene extrapolation via diffusion inpainting, enabling the generation of unbounded simulation environments. Extensive experiments show that Scenario Dreamer outperforms existing generative simulators in realism and efficiency: the vectorized scene-generation base model achieves superior generation quality with around 2x fewer parameters, 6x lower generation latency, and 10x fewer GPU training hours compared to the strongest baseline. We confirm its practical utility by showing that reinforcement learning planning agents are more challenged in Scenario Dreamer environments than traditional non-generative simulation environments, especially on long and adversarial driving environments.
2024-12-31
CVPR (published)
doi.org
arxiv.org