Publications

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) (published)
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 (published)
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… (see more)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 (preprint)
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
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… (see more)-weighted MRI scans.
2024-11-05
Radiology: Artificial Intelligence (published)
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 (published)
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… (see more)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 (preprint)
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… (see more)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 (published)
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.… (see more) 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 (published)
doi.org
Crystal Design Amidst Noisy DFT Signals: A Reinforcement Learning Approach
Prashant Govindarajan
Mathieu Reymond
Santiago Miret
Mariano Phielipp
A. Chandar
2024-11-02
NeurIPS.cc/2024/Workshop/AI4Mat (published)
openreview.net
openreview.net
ImmunoStruct: Integration of protein sequence, structure, and biochemical properties for immunogenicity prediction and interpretation
Kevin Bijan Givechian
João Felipe Rocha
Edward Yang
Chen Liu
Kerrie Greene
Rex Ying
Etienne Caron
Akiko Iwasaki
Smita Krishnaswamy
2024-11-02
bioRxiv (preprint)
doi.org
Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders
Luke Marks
Alasdair Paren
David M. Krueger
Fazl Barez
Sparse Autoencoders (SAEs) have shown promise in improving the interpretability of neural network activations, but can learn features that a… (see more)re not features of the input, limiting their effectiveness. We propose \textsc{Mutual Feature Regularization} \textbf{(MFR)}, a regularization technique for improving feature learning by encouraging SAEs trained in parallel to learn similar features. We motivate \textsc{MFR} by showing that features learned by multiple SAEs are more likely to correlate with features of the input. By training on synthetic data with known features of the input, we show that \textsc{MFR} can help SAEs learn those features, as we can directly compare the features learned by the SAE with the input features for the synthetic data. We then scale \textsc{MFR} to SAEs that are trained to denoise electroencephalography (EEG) data and SAEs that are trained to reconstruct GPT-2 Small activations. We show that \textsc{MFR} can improve the reconstruction loss of SAEs by up to 21.21\% on GPT-2 Small, and 6.67\% on EEG data. Our results suggest that the similarity between features learned by different SAEs can be leveraged to improve SAE training, thereby enhancing performance and the usefulness of SAEs for model interpretability.
2024-11-01
ArXiv (preprint)
doi.org
openreview.net
AI-EDI-SPACE: A Co-designed Dataset for Evaluating the Quality of Public Spaces
S. Gowaikar
Hugo Berard
Rashid A. Mushkani
Emmanuel Beaudry Marchand
Toumadher Ammar
Shin Koseki
Advancements in AI heavily rely on large-scale datasets meticulously curated and annotated for training. However, concerns persist regarding… (see more) the transparency and context of data collection methodologies, especially when sourced through crowdsourcing platforms. Crowdsourcing often employs low-wage workers with poor working conditions and lacks consideration for the representativeness of annotators, leading to algorithms that fail to represent diverse views and perpetuate biases against certain groups. To address these limitations, we propose a methodology involving a co-design model that actively engages stakeholders at key stages, integrating principles of Equity, Diversity, and Inclusion (EDI) to ensure diverse viewpoints. We apply this methodology to develop a dataset and AI model for evaluating public space quality using street view images, demonstrating its effectiveness in capturing diverse perspectives and fostering higher-quality data.
2024-10-31
arXiv (published)
doi.org
arxiv.org