Publications

Sharpness-Aware Minimization Scaled by Outlier Normalization for Robust DNNs on In-Memory Computing Accelerators
Sébastien Henwood
Goncalo Mordido
Yvon Savaria
A. Chandar
François Leduc-Primeau
Many deep neural network (DNN) models consume a significant amount of energy at inference time, in large part due to energy consumed by memo… (voir plus)ry access. In-memory computing addresses this problem by eliminating many memory accesses, but exposes model weights to noise and circuit variations. While several methods have been proposed to train DNNs robust to weight noise they typically require knowledge of the noise distribution, or degrade the DNN performance in noiseless setting. In this work, we first show that applying sharpness-aware training, by optimizing for both the loss value and loss sharpness, significantly improves robustness to noisy weights at inference time. Then, we propose a new adaptive sharpness-aware method that conditions the worst-case perturbation of a given weight not only on its magnitude but also on the range of the weight distribution. This is achieved by performing sharpness-aware minimization scaled by outlier normalization (SAMSON). Results on computer-vision benchmarks show that SAMSON increases model robustness to noisy weights without compromising generalization performance in noiseless regimes.
2024-10-26
Asilomar Conference on Signals, Systems and Computers (publié)
doi.org
Brain-like learning with exponentiated gradients
Jonathan Cornford
Roman Pogodin
Arna Ghosh
Kaiwen Sheng
Brendan A. Bicknell
Olivier Codol
Beverley A. Clark
Guillaume Lajoie
Blake A. Richards
Computational neuroscience relies on gradient descent (GD) for training artificial neural network (ANN) models of the brain. The advantage o… (voir plus)f GD is that it is effective at learning difficult tasks. However, it produces ANNs that are a poor phenomenological fit to biology, making them less relevant as models of the brain. Specifically, it violates Dale’s law, by allowing synapses to change from excitatory to inhibitory, and leads to synaptic weights that are not log-normally distributed, contradicting experimental data. Here, starting from first principles of optimisation theory, we present an alternative learning algorithm, exponentiated gradient (EG), that respects Dale’s Law and produces log-normal weights, without losing the power of learning with gradients. We also show that in biologically relevant settings EG outperforms GD, including learning from sparsely relevant signals and dealing with synaptic pruning. Altogether, our results show that EG is a superior learning algorithm for modelling the brain with ANNs.
2024-10-25
bioRxiv (prépublication)
doi.org
Efficient Biological Data Acquisition through Inference Set Design
Ihor Neporozhnii
Julien Roy
Emmanuel Bengio
Jason Hartford
In drug discovery, highly automated high-throughput laboratories are used to screen a large number of compounds in search of effective drugs… (voir plus). These experiments are expensive, so one might hope to reduce their cost by only experimenting on a subset of the compounds, and predicting the outcomes of the remaining experiments. In this work, we model this scenario as a sequential subset selection problem: we aim to select the smallest set of candidates in order to achieve some desired level of accuracy for the system as a whole. Our key observation is that, if there is heterogeneity in the difficulty of the prediction problem across the input space, selectively obtaining the labels for the hardest examples in the acquisition pool will leave only the relatively easy examples to remain in the inference set, leading to better overall system performance. We call this mechanism inference set design, and propose the use of a confidence-based active learning solution to prune out these challenging examples. Our algorithm includes an explicit stopping criterion that interrupts the acquisition loop when it is sufficiently confident that the system has reached the target performance. Our empirical studies on image and molecular datasets, as well as a real-world large-scale biological assay, show that active learning for inference set design leads to significant reduction in experimental cost while retaining high system performance.
2024-10-24
ArXiv (prépublication)
doi.org
arxiv.org
Investigating Alpha-DaRT Source Daughter Diffusion in Intra-Rectal Animal Models
Mélodie Cyr
Behnaz Behmand
Naim Chabaytah
Joud Babik
Mirta Dumancic
Joanna Li
Guillaume St-Jean
Shirin A. Enger
2024-10-24
Brachytherapy (publié)
doi.org
Prediction of Final Phosphorus Content of Steel in a Scrap-Based Electric Arc Furnace Using Artificial Neural Networks
Riadh Azzaz
Valentin Hurel
Patrice Ménard
M. Jahazi
S Ebrahimi Kahou
Elmira Moosavi-Khoonsari
2024-10-24
ArXiv (prépublication)
doi.org
arxiv.org
ConvNTC: Convolutional neural tensor completion for predicting the disease-related miRNA pairs and cell-related drug pairs
Pei Liu
Xiao Liang
Yuemei Li
Jiawei Luo
2024-10-23
bioRxiv (prépublication)
doi.org
The roles of neural networks in language acquisition
Eva Portelance
Masoud Jasbi

How can modern neural networks like large language models be useful to the field of language acquisition, and more broadly cognitive scie… (voir plus)nce, if they are not a priori designed to be cognitive models? As developments towards natural language understanding and generation have improved leaps and bounds, with models like GPT-4, the question of how they can inform our understanding of human language acquisition has re-emerged. As such, it is critical to examine how in practice linking hypotheses between models and human learners can be safely established. To address these questions, we propose a model taxonomy, including four modeling approaches, each having differing goals, from exploratory hypothesis generation to hypothesis differentiation and testing. We show how the goals of these approaches align with the overarching goals of science and linguistics by connecting our taxonomy to the realist vs. instrumentalist approaches in philosophy of science. We survey recent work having adopted each of our modelling approaches and address the importance of computational modelling in language acquisition studies.

2024-10-23
Language and Linguistics Compass (publié)
doi.org
Minimally Invasive Morphology Adaptation via Parameter Efficient Fine-Tuning
Michael Przystupa
Hongyao Tang
Mariano Phielipp
Santiago Miret
Martin Jagersand
Glen Berseth
Learning reinforcement learning policies to control individual robots is often computationally non-economical because minor variations in ro… (voir plus)bot morphology (e.g. dynamics or number of limbs) can negatively impact policy performance. This limitation has motivated morphology agnostic policy learning, in which a monolithic deep learning policy learns to generalize between robotic morphologies. Unfortunately, these policies still have sub-optimal zero-shot performance compared to end-to-end finetuning on target morphologies. This limitation has ramifications in practical robotic applications, as online finetuning large neural networks can require immense computation. In this work, we investigate \textit{parameter efficient finetuning} techniques to specialize morphology-agnostic policies to a target robot that minimizes the number of learnable parameters adapted during online learning. We compare direct finetuning, which update subsets of the base model parameters, and input-learnable approaches, which add additional parameters to manipulate inputs passed to the base model. Our analysis concludes that tuning relatively few parameters (0.01\% of the base model) can measurably improve policy performance over zero shot. These results serve a prescriptive purpose for future research for which scenarios certain PEFT approaches are best suited for adapting policy's to new robotic morphologies.
2024-10-22
corl.org/2024/Workshop/MAPoDeL (publié)
openreview.net
openreview.net
Multilingual Hallucination Gaps in Large Language Models
Cl'ea Chataigner
Afaf Taïk
Golnoosh Farnadi
Large language models (LLMs) are increasingly used as alternatives to traditional search engines given their capacity to generate text that … (voir plus)resembles human language. However, this shift is concerning, as LLMs often generate hallucinations, misleading or false information that appears highly credible. In this study, we explore the phenomenon of hallucinations across multiple languages in freeform text generation, focusing on what we call multilingual hallucination gaps. These gaps reflect differences in the frequency of hallucinated answers depending on the prompt and language used. To quantify such hallucinations, we used the FactScore metric and extended its framework to a multilingual setting. We conducted experiments using LLMs from the LLaMA, Qwen, and Aya families, generating biographies in 19 languages and comparing the results to Wikipedia pages. Our results reveal variations in hallucination rates, especially between high and low resource languages, raising important questions about LLM multilingual performance and the challenges in evaluating hallucinations in multilingual freeform text generation.
2024-10-22
ArXiv (prépublication)
doi.org
arxiv.org
Overcoming State and Action Space Disparities in Multi-Domain, Multi-Task Reinforcement Learning
Reginald McLean
Kai Yuan
Isaac Woungang
Nariman Farsad
Pablo Samuel Castro
Current multi-task reinforcement learning (MTRL) methods have the ability to perform a large number of tasks with a single policy. However w… (voir plus)hen attempting to interact with a new domain, the MTRL agent would need to be re-trained due to differences in domain dynamics and structure. Because of these limitations, we are forced to train multiple policies even though tasks may have shared dynamics, leading to needing more samples and is thus sample inefficient. In this work, we explore the ability of MTRL agents to learn in various domains with various dynamics by simultaneously learning in multiple domains, without the need to fine-tune extra policies. In doing so we find that a MTRL agent trained in multiple domains induces an increase in sample efficiency of up to 70\% while maintaining the overall success rate of the MTRL agent.
2024-10-22
corl.org/2024/Workshop/MAPoDeL (publié)
openreview.net
openreview.net
Symmetry-Aware Generative Modeling through Learned Canonicalization
Kusha Sareen
Daniel Levy
Arnab Kumar Mondal
Sékou-Oumar Kaba
Tara Akhound-Sadegh
Siamak Ravanbakhsh
Generative modeling of symmetric densities has a range of applications in AI for science, from drug discovery to physics simulations. The ex… (voir plus)isting generative modeling paradigm for invariant densities combines an invariant prior with an equivariant generative process. However, we observe that this technique is not necessary and has several drawbacks resulting from the limitations of equivariant networks. Instead, we propose to model a learned slice of the density so that only one representative element per orbit is learned. To accomplish this, we learn a group-equivariant canonicalization network that maps training samples to a canonical pose and train a non-equivariant generative model over these canonicalized samples. We implement this idea in the context of diffusion models. Our preliminary experimental results on molecular modeling are promising, demonstrating improved sample quality and faster inference time.
2024-10-22
NeurIPS.cc/2024/Workshop/NeurReps (poster)
doi.org
openreview.net
Correction: Al content detection in the emerging information ecosystem: new obligations for media and tech companies
Alistair Knott
Dino Pedreschi
Toshiya Jitsuzumi
Susan Leavy
David Eyers
Tapabrata Chakraborti
Andrew Trotman
Sundar Sundareswaran
Ricardo Baeza-Yates
Przemyslaw Biecek
Adrian Weller
Paul D. Teal
Subhadip Basu
Mehmet Haklidir
Virginia Morini
Stuart Russell
Yoshua Bengio
2024-10-21
Ethics and Information Technology (publié)
doi.org