Publications

INTREPPPID—an orthologue-informed quintuplet network for cross-species prediction of protein-protein interaction
Joseph Szymborski
Amin Emad
An overwhelming majority of protein–protein interaction (PPI) studies are conducted in a select few model organisms largely due to constra… (see more)ints in time and cost of the associated ‘wet lab’ experiments. In silico PPI inference methods are ideal tools to overcome these limitations, but often struggle with cross-species predictions. We present INTREPPPID, a method that incorporates orthology data using a new ‘quintuplet’ neural network, which is constructed with five parallel encoders with shared parameters. INTREPPPID incorporates both a PPI classification task and an orthologous locality task. The latter learns embeddings of orthologues that have small Euclidean distances between them and large distances between embeddings of all other proteins. INTREPPPID outperforms all other leading PPI inference methods tested on both the intraspecies and cross-species tasks using strict evaluation datasets. We show that INTREPPPID’s orthologous locality loss increases performance because of the biological relevance of the orthologue data and not due to some other specious aspect of the architecture. Finally, we introduce PPI.bio and PPI Origami, a web server interface for INTREPPPID and a software tool for creating strict evaluation datasets, respectively. Together, these two initiatives aim to make both the use and development of PPI inference tools more accessible to the community.
2023-12-31
Briefings Bioinform. (published)
doi.org
ÌròyìnSpeech: A multi-purpose Yorùbá Speech Corpus
Tolúlope' Ògúnremí
Kọ́lá Túbọ̀sún
Aremu Anuoluwapo
Iroro Orife
David Ifeoluwa Adelani
2023-12-31
LREC/COLING (published)
doi.org
arxiv.org
KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge Distillation
Rambod Azimi
Rishav
Marek Teichmann
S Ebrahimi Kahou
2023-12-31
ENLSP (published)
doi.org
proceedings.mlr.press
Large‐scale geographic patterns of diversity and community structure of pelagic crustacean zooplankton in <scp>C</scp>anadian lakes
Bernadette Pinel‐Alloul
Adrien André
Pierre Legendre
Jeffrey A. Cardille
Kasimierz Patalas
Alex Salki
The richness‐energy theory explains diversity patterns of freshwater crustacean zooplankton in Canadian ecoprovinces. Solar radiation is t… (see more)he best predictor explaining regional species richness in ecoprovinces and community structure of pelagic crustaceans in Canadian lakes.
2023-12-31
International Conference on Machine Learning (published)
doi.org
Learnable Filters for Geometric Scattering Modules
Alexander Tong
Frederik Wenkel
Dhananjay Bhaskar
Kincaid MacDonald
Jackson Grady
Michael Perlmutter
Smita Krishnaswamy
Guy Wolf
2023-12-31
IEEE Transactions on Signal Processing (published)
doi.org
arxiv.org
Learning conditional policies for crystal design using offline reinforcement learning
Prashant Govindarajan
Santiago Miret
Jarrid Rector-Brooks
Mariano Phielipp
Janarthanan Rajendran
Sarath Chandar
Conservative Q-learning for band-gap conditioned crystal design with DFT evaluations – the model is trained on trajectories constructed fr… (see more)om crystals in the Materials Project. Results indicate promising performance for lower band gap targets.
2023-12-31
Digital Discovery (published)
doi.org
openreview.net
Learning Lagrangian Multipliers for the Travelling Salesman Problem
Augustin Parjadis
Quentin Cappart
Bistra Dilkina
Aaron M. Ferber
Louis-Martin Rousseau
2023-12-31
CP (published)
doi.org
arxiv.org
Learning Precedences for Scheduling Problems with Graph Neural Networks
Hélène Verhaeghe
Quentin Cappart
Gilles Pesant
Claude-Guy Quimper
2023-12-31
CP (published)
doi.org
On learning Whittle index policy for restless bandits with scalable regret
Nima Akbarzadeh
Aditya Mahajan
Reinforcement learning is an attractive approach to learn good resource allocation and scheduling policies based on data when the system mod… (see more)el is unknown. However, the cumulative regret of most RL algorithms scales as ˜ O(S
2023-12-31
IEEE Transactions on Control of Network Systems (published)
doi.org
arxiv.org
Low-Dimensional Embeddings of High-Dimensional Data: Algorithms and Applications (Dagstuhl Seminar 24122).
Dmitry Kobak
Fred Hamprecht
Smita Krishnaswamy
Gal Mishne
Sebastian Damrich
2023-12-31
Dagstuhl Reports (published)
doi.org
Machine Translation Hallucination Detection for Low and High Resource Languages using Large Language Models
Kenza Benkirane
Laura Gongas
Shahar Pelles
Naomi Fuchs
Joshua Darmon
Pontus Stenetorp
David Ifeoluwa Adelani
Eduardo Sánchez
Meta
2023-12-31
EMNLP (Findings) (published)
doi.org
openreview.net
MagicClay: Sculpting Meshes With Generative Neural Fields
Amir Barda
Vladimir Kim
Noam Aigerman
Amit H. Bermano
Thibault Groueix
The recent developments in neural fields have brought phenomenal capabilities to the field of shape generation, but they lack crucial proper… (see more)ties, such as incremental control - a fundamental requirement for artistic work. Triangular meshes, on the other hand, are the representation of choice for most geometry related tasks, offering efficiency and intuitive control, but do not lend themselves to neural optimization. To support downstream tasks, previous art typically proposes a two-step approach, where first a shape is generated using neural fields, and then a mesh is extracted for further processing. Instead, in this paper we introduce a hybrid approach that maintains both a mesh and a Signed Distance Field (SDF) representations consistently. Using this representation, we introduce MagicClay - an artist friendly tool for sculpting regions of a mesh according to textual prompts while keeping other regions untouched. Our framework carefully and efficiently balances consistency between the representations and regularizations in every step of the shape optimization; Relying on the mesh representation, we show how to render the SDF at higher resolutions and faster. In addition, we employ recent work in differentiable mesh reconstruction to adaptively allocate triangles in the mesh where required, as indicated by the SDF. Using an implemented prototype, we demonstrate superior generated geometry compared to the state-of-the-art, and novel consistent control, allowing sequential prompt-based edits to the same mesh for the first time.
2023-12-31
SIGGRAPH Asia (published)
doi.org
arxiv.org