Publications

Towards Enhancing the Reproducibility of Deep Learning Bugs: An Empirical Study
Mehil B. Shah
Mohammad Masudur Rahman
Foutse Khomh
2024-11-08
Empirical Software Engineering (publié)
doi.org
arxiv.org
A new species of Hoplostethus from Sumatra, eastern Indian Ocean, with comments on its most similar congeners (Trachichthyiformes: Trachichthyidae).
Yo Su
Alexander N. Kotlyar
Hsiu-Chin Lin
Toshio Kawai
HSUAN-CHING HO
2024-11-07
Journal of Fish Biology (publié)
doi.org
Unlearning in- vs. out-of-distribution data in LLMs under gradient-based method
Teodora Baluta
Pascal Lamblin
Daniel Tarlow
Fabian Pedregosa
Gintare Karolina Dziugaite
Machine unlearning aims to solve the problem of removing the influence of selected training examples from a learned model. Despite the incre… (voir plus)asing attention to this problem, it remains an open research question how to evaluate unlearning in large language models (LLMs), and what are the critical properties of the data to be unlearned that affect the quality and efficiency of unlearning. This work formalizes a metric to evaluate unlearning quality in generative models, and uses it to assess the trade-offs between unlearning quality and performance. We demonstrate that unlearning out-of-distribution examples requires more unlearning steps but overall presents a better trade-off overall. For in-distribution examples, however, we observe a rapid decay in performance as unlearning progresses. We further evaluate how example's memorization and difficulty affect unlearning under a classical gradient ascent-based approach.
2024-11-06
ArXiv (prépublication)
doi.org
arxiv.org
CSGraph2Vec: Distributed Graph-Based Representation Learning for Assembly Functions
Wael J. Alhashemi
Benjamin C. M. Fung
Adel Abusitta
Claude Fachkha
2024-11-05
2024 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE) (publié)
doi.org
GAPS Phase III: incorporation of capacity based weighting in the global assessment for pediatric surgery
Yasmine Yousef
Emmanuel Ameh
Luc Malemo Kalisya
Dan Poenaru
2024-11-05
Pediatric Surgery International (publié)
doi.org
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset
Alexandre Galashov
Michalis K. Titsias
Andr'as Gyorgy
Clare Lyle
Razvan Pascanu
Yee Whye Teh
Maneesh Sahani
Neural networks are traditionally trained under the assumption that data come from a stationary distribution. However, settings which violat… (voir plus)e this assumption are becoming more popular; examples include supervised learning under distributional shifts, reinforcement learning, continual learning and non-stationary contextual bandits. In this work we introduce a novel learning approach that automatically models and adapts to non-stationarity, via an Ornstein-Uhlenbeck process with an adaptive drift parameter. The adaptive drift tends to draw the parameters towards the initialisation distribution, so the approach can be understood as a form of soft parameter reset. We show empirically that our approach performs well in non-stationary supervised and off-policy reinforcement learning settings.
2024-11-05
ArXiv (prépublication)
doi.org
arxiv.org
SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans
Enamundram Naga Karthik
Jan Valosek
Andrew C. Smith
Dario Pfyffer
Simon Schading-Sassenhausen
Lynn Farner
Kenneth A. Weber
Patrick Freund
Julien Cohen-Adad
The proposed deep learning model accurately segmented the spinal cord and spinal cord injury lesions in a diverse, multicenter dataset of T2… (voir plus)-weighted MRI scans.
2024-11-05
Radiology: Artificial Intelligence (publié)
doi.org
Spinal cord evaluation in multiple sclerosis: clinical and radiological associations, present and future
B Mark Keegan
Martina Absinta
Julien Cohen-Adad
Eoin P Flanagan
Roland G Henry
Eric C Klawiter
Shannon Kolind
Stephen Krieger
Cornelia Laule
John A Lincoln
Steven Messina
Jiwon Oh
Nico Papinutto
Seth Aaron Smith
Anthony Traboulsee
2024-11-05
Brain Communications (publié)
doi.org
Towards Optimizing SQL Generation via LLM Routing
Mohammadhossein Malekpour
Nour Shaheen
Foutse Khomh
Amine Mhedhbi
Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capabl… (voir plus)e large language models (LLMs) achieve strong accuracy for complex queries, they incur unnecessary latency and dollar cost for simpler ones. In this paper, we introduce the first LLM routing approach for Text-to-SQL, which dynamically selects the most cost-effective LLM capable of generating accurate SQL for each query. We present two routing strategies (score- and classification-based) that achieve accuracy comparable to the most capable LLM while reducing costs. We design the routers for ease of training and efficient inference. In our experiments, we highlight a practical and explainable accuracy-cost trade-off on the BIRD dataset.
2024-11-05
ArXiv (prépublication)
doi.org
arxiv.org
GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models
Nizar Islah
Justine Gehring
Diganta Misra
Eilif Muller
Irina Rish
Terry Yue Zhuo
Massimo Caccia
2024-11-04
arXiv (prépublication)
doi.org
arxiv.org
Temporal Residual Jacobians For Rig-free Motion Transfer
Sanjeev Muralikrishnan
Niladri Dutt
Siddhartha Chaudhuri
Noam Aigerman
Vladimir Kim
Matthew Fisher
Niloy J. Mitra
We introduce Temporal Residual Jacobians as a novel representation to enable data-driven motion transfer. Our approach does not assume acces… (voir plus)s to any rigging or intermediate shape keyframes, produces geometrically and temporally consistent motions, and can be used to transfer long motion sequences. Central to our approach are two coupled neural networks that individually predict local geometric and temporal changes that are subsequently integrated, spatially and temporally, to produce the final animated meshes. The two networks are jointly trained, complement each other in producing spatial and temporal signals, and are supervised directly with 3D positional information. During inference, in the absence of keyframes, our method essentially solves a motion extrapolation problem. We test our setup on diverse meshes (synthetic and scanned shapes) to demonstrate its superiority in generating realistic and natural-looking animations on unseen body shapes against SoTA alternatives. Supplemental video and code are available at https://temporaljacobians.github.io/ .
2024-11-04
Lecture Notes in Computer Science (publié)
doi.org
arxiv.org
Efficient Assignment with Time Constraints for Heterogeneous DSP Systems.
Jiajie Li
Christophe Dubach
Warren J. Gross
High-level synthesis (HLS) produces hardware au-tomatically by scheduling and assigning resources based on an input control/data-flow graph.… (voir plus) One particular aspect of HLS for the digital signal processing (DSP) architecture is the het-erogeneous assignment problem (HAP) which maps operations into different types of functional units available in the electronic design automation tools to build efficient implementations. An optimal solution to this assignment problem can be found by formulating the problem as integer linear programming (ILP) and using a solver. However, given the slow nature of this process, heuristics tend to be used instead leading to sub-optimal designs. This paper revisits the classical ILP formulation of the HAP with time constraints for the DSP architecture by identifying redundant constraints. This paper proves theoretically, and demonstrates experimentally, that removing these constraints does not affect the obtained solution. This technique achieves speedups of more than 100 × in terms of runtime and reductions of more than 50 × in terms of memory usage of the solver. Also, this work proposes an updated heuristic that keeps reducing the latency of a path instead of finding a new critical path after giving a new node assignment. Runtime reductions (more than another 10×) due to reduced numbers of critical path searches are observed while returning similar results.
2024-11-03
IEEE Workshop on Signal Processing Systems (publié)
doi.org