Publications

Gap Minimization for Knowledge Sharing and Transfer
Boyu Wang
Jorge A. Mendez
Changjian Shui
Fan Zhou
Di Wu
Gezheng Xu
Christian Gagné
Eric R. Eaton
Learning from multiple related tasks by knowledge sharing and transfer has become increasingly relevant over the last two decades. In order … (see more)to successfully transfer information from one task to another, it is critical to understand the similarities and differences between the domains. In this paper, we introduce the notion of \emph{performance gap}, an intuitive and novel measure of the distance between learning tasks. Unlike existing measures which are used as tools to bound the difference of expected risks between tasks (e.g.,
arxiv.org
General Purpose AI Systems in the AI Act: Trying to Fit a Square Peg Into a Round Hole
Claire Boine
David Rolnick
2023-01-01
SSRN Electronic Journal (published)
doi.org
Generating QM1B with PySCF$_{\text{IPU}}$
Alexander Mathiasen
Hatem Helal
Kerstin Klaeser
Paul Balanca
Josef Dean
Carlo Luschi
Dominique Beaini
Andrew William Fitzgibbon
Dominic Masters
openreview.net
Generating QM1B with PySCFIPU
Alexander Mathiasen
Hatem Helal
Kerstin Klaser
Paul Balanca
Josef Dean
Carlo Luschi
Dominique Beaini
Andrew William Fitzgibbon
Dominic Masters
2023-01-01
NeurIPS (published)
doi.org
arxiv.org
GEODESIC SINKHORN FOR FAST AND ACCURATE OPTIMAL TRANSPORT ON MANIFOLDS
Guillaume Huguet
Alexander Tong
María Ramos Zapatero
Christopher J. Tape
Guy Wolf
Smita Krishnaswamy
Efficient computation of optimal transport distance between distributions is of growing importance in data science. Sinkhorn-based methods a… (see more)re currently the state-of-the-art for such computations, but require O(n2) computations. In addition, Sinkhorn-based methods commonly use an Euclidean ground distance between datapoints. However, with the prevalence of manifold structured scientific data, it is often desirable to consider geodesic ground distance. Here, we tackle both issues by proposing Geodesic Sinkhorn—based on diffusing a heat kernel on a manifold graph. Notably, Geodesic Sinkhorn requires only O(n log n) computation, as we approximate the heat kernel with Chebyshev polynomials based on the sparse graph Laplacian. We apply our method to the computation of barycenters of several distributions of high dimensional single cell data from patient samples undergoing chemotherapy. In particular, we define the barycentric distance as the distance between two such barycenters. Using this definition, we identify an optimal transport distance and path associated with the effect of treatment on cellular data.
2023-01-01
2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP) (published)
doi.org
arxiv.org
GFlowNets for AI-Driven Scientific Discovery
Moksh J. Jain
Tristan Deleu
Jason Hartford
Cheng-Hao Liu
Alex Hernandez-Garcia
Yoshua Bengio
Tackling the most pressing problems for humanity, such as the climate crisis and the threat of global pandemics, requires accelerating the p… (see more)ace of scientific discovery. While science has traditionally relied...
2023-01-01
Digital Discovery (published)
doi.org
arxiv.org
GFlowOut: Dropout with Generative Flow Networks
Dianbo Liu
Moksh J. Jain
Bonaventure F. P. Dossou
Qianli Shen
Salem Lahlou
Anirudh Goyal
Nikolay Malkin
Chris Emezue
Dinghuai Zhang
Nadhir Hassen
Xu Ji
Kenji Kawaguchi
Yoshua Bengio
2023-01-01
ICML (published)
doi.org
openreview.net
GFlowOut: Dropout with Generative Flow Networks
Dianbo Liu
Moksh J. Jain
Bonaventure F. P. Dossou
Qianli Shen
Salem Lahlou
Anirudh Goyal
Nikolay Malkin
Chris Emezue
Dinghuai Zhang
Nadhir Hassen
Xu Ji
Kenji Kawaguchi
Yoshua Bengio
2023-01-01
ICML (published)
doi.org
openreview.net
GitHub Copilot AI pair programmer: Asset or Liability?
Arghavan Moradi Dakhel
Vahid Majdinasab
Amin Nikanjam
Foutse Khomh
Michel C. Desmarais
Z. Jiang
Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called… (see more) Copilot, has been proposed by OpenAI and Microsoft as an industrial product. Although some studies evaluate the correctness of Copilot solutions and report its issues, more empirical evaluations are necessary to understand how developers can benefit from it effectively. In this paper, we study the capabilities of Copilot in two different programming tasks: (i) generating (and reproducing) correct and efficient solutions for fundamental algorithmic problems, and (ii) comparing Copilot's proposed solutions with those of human programmers on a set of programming tasks. For the former, we assess the performance and functionality of Copilot in solving selected fundamental problems in computer science, like sorting and implementing data structures. In the latter, a dataset of programming problems with human-provided solutions is used. The results show that Copilot is capable of providing solutions for almost all fundamental algorithmic problems, however, some solutions are buggy and non-reproducible. Moreover, Copilot has some difficulties in combining multiple methods to generate a solution. Comparing Copilot to humans, our results show that the correct ratio of humans' solutions is greater than Copilot's suggestions, while the buggy solutions generated by Copilot require less effort to be repaired.
2023-01-01
J. Syst. Softw. (published)
doi.org
arxiv.org
GOKU-UI: Ubiquitous Inference through Attention and Multiple Shooting for Continuous-time Generative Models
Germán Abrevaya
Mahta Ramezanian-Panahi
Jean-Christophe Gagnon-Audet
Irina Rish
Pablo Polosecki
Silvina Ponce Dawson
Guillermo Cecchi
Guillaume Dumas
Scientific Machine Learning (SciML) is a burgeoning field that synergistically combines domain-aware and interpretable models with agnosti… (see more)c machine learning techniques. In this work, we introduce GOKU-UI, an evolution of the SciML generative model GOKU-nets. The GOKU-UI broadens the original model’s spectrum to incorporate other classes of differential equations, such as Stochastic Differential Equations (SDEs), and integrates a distributed, i.e. ubiquitous, inference through attention mechanisms and a novel multiple shooting training strategy in the latent space. These enhancements have led to a significant increase in its performance in both reconstruction and forecast tasks, as demonstrated by our evaluation of simulated and empirical data. Specifically, GOKU-UI outperformed all baseline models on synthetic datasets even with a training set 32-fold smaller, underscoring its remarkable data efficiency. Furthermore, when applied to empirical human brain data, while incorporating stochastic Stuart-Landau
2023-01-01
arXiv.org (preprint)
doi.org
Grammar Generative Models for Music Notation
Anna (Cheng-Zhi) Huang
Deep generative models have been successfully applied in many learning experiments with digital data, such as images or audio. In the field … (see more)of music, they can also be used to generate symbolic representations, in the context of problems such as automatic music generation or transcription [1-3]. A significant challenge for generating structured symbolic data in general is obtaining well-formed results. This is especially true in the case of music. It is indeed widely accepted that musical notation represents, well beyond simple sequences of notes, a hierarchical organization of melodic and harmonic information, inducing non-local dependencies between musical objects [4]. A good representation of this information is essential for the interpretation and analysis of music pieces.
2023-01-01
(published)
www.semanticscholar.org
Graph Inductive Biases in Transformers without Message Passing
Liheng Ma
Chen Lin
Derek Lim
Adriana Romero Soriano
Puneet K. Dokania
Mark Coates
Philip Torr
Ser-Nam Lim
Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial fo… (see more)r Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph Transformers that use message-passing inherit known issues of message-passing, and differ significantly from Transformers used in other domains, thus making transfer of research advances more difficult. On the other hand, Graph Transformers without message-passing often perform poorly on smaller datasets, where inductive biases are more crucial. To bridge this gap, we propose the Graph Inductive bias Transformer (GRIT) — a new Graph Transformer that incorporates graph inductive biases without using message passing. GRIT is based on several architectural changes that are each theoretically and empirically justified, including: learned relative positional encodings initialized with random walk probabilities, a flexible attention mechanism that updates node and node-pair representations, and injection of degree information in each layer. We prove that GRIT is expressive — it can express shortest path distances and various graph propagation matrices. GRIT achieves state-of-the-art empirical performance across a variety of graph datasets, thus showing the power that Graph Transformers without message-passing can deliver.
2023-01-01
ICML (published)
doi.org
openreview.net