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Research Papers

Advancing DNA Language Models: The Genomics Long-Range Benchmark

Chia-Hsiang Kao | Evan Trop | McKinley Polen | Yair Schiff | Bernardo P. de Almeida | Aaron Gokaslan | Thomas Pierrot | Volodymyr Kuleshov

LLMs4Bio AAAI 2024 | MLGenX ICLR 2024 May 2024
Advancing DNA Language Models: The Genomics Long-Range Benchmark
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ChatNT: A Multimodal Conversational Agent for DNA, RNA and Protein Tasks

Guillaume Richard* | Bernardo P. de Almeida* | Hugo Dalla-Torre | Christopher Blum | Lorenz Hexemer | Priyanka Pandey | Stefan Laurent | Marie Lopez | Alexandre Laterre | Maren Lang | Ugur Sahin | Karim Beguir | Thomas Pierrot

Apr 2024
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SegmentNT: annotating the genome at single-nucleotide resolution with DNA foundation models

Bernardo P. de Almeida | Hugo Dalla-Torre | Guillaume Richard | Christopher Blum | Lorenz Hexemer | Maxence Gelard | Priyanka Pandey | Stefan Laurent | Alexandre Laterre | Maren Lang | Ugur Sahin | Karim Beguir | Thomas Pierrot

Mar 2024
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Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX

Clément Bonnet | Daniel Luo | Donal Byrne | Shikha Surana | Vincent Coyette | Paul Duckworth | Laurence I. Midgley | Tristan Kalloniatis | Sasha Abramowitz | Cemlyn N. Waters | Andries P. Smit | Nathan Grinsztajn | Ulrich A. Mbou Sob | Omayma Mahjoub | Elshadai Tegegn | Mohamed A. Mimouni | Raphael Boige | Ruan de Kock | Daniel Furelos-Blanco | Victor Le | Arnu Pretorius | Alexandre Laterre

ICLR 2024 Mar 2024
Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX
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How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning

Omayma Mahjoub | Ruan de Kock | Siddarth Singh | Wiem Khlifi | Abidine Vall | Kale-ab Tessera | Arnu Pretorius

AAAI workshop Mar 2024
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning
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On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL

Omayma Mahjoub | Ruan de Kock | Siddarth Singh | Wiem Khlifi | Abidine Vall | Rihab Gorsane | Arnu Pretorius

AAAI workshop Mar 2024
On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL
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Efficiently Quantifying Individual Agent Importance in Cooperative MARL

Omayma Mahjoub | Ruan de Kock | Siddarth Singh | Wiem Khlifi | Abidine Vall | Rihab Gorsane | Arnu Pretorius

AAAI workshop Mar 2024
Efficiently Quantifying Individual Agent Importance in Cooperative MARL
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TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect

Hatem Haddad | Abir Messaoudi | Chayma Fourati | Moez Ben HajHmida | Ahmed Cheikh Rouhou | Abir Korched | Amel Sellami | Faten Ghriss

Mar 2024
TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect
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Graph Neural Networks for End-to-End Information Extraction from Handwritten Documents

Yessine Khanfir | Marwa Dhiaf | Emna Ghodhbani | Ahmed Cheikh Rouhou | Yousri Kessentini

WACV 2024 Mar 2024
Graph Neural Networks for End-to-End Information Extraction from Handwritten Documents
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BioCLIP: Contrasting Sequence with Structure: Pre-training Graph Representations with Protein Language Models

Louis Robinson | Timothy Atkinson | Liviu Copoiu | Patrick Bordes | Thomas Pierrot | Thomas D. Barrett

NeurIPS workshop Dec 2023
BioCLIP, a self-supervised contrastive learning framework for generating Protein Structure Models (PSMs) based on Protein Language Models (PLMs) like ESM2
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Generalisable Agents for Neural Network Optimisation

Kale-ab Tessera | Callum Rhys Tilbury | Sasha Abramowitz | Ruan de Kock | Omayma Mahjoub | Benjamin Rosman | Sara Hooker | Arnu Pretorius

NeurIPS workshop Dec 2023
Optimising deep neural networks with framework of Generalisable Agents for Neural Network Optimisation (GANNO) — a multi-agent reinforcement learning (MARL) approach that learns to improve neural network optimisation by dynamically and responsively scheduling hyperparameters during training
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Offline RL for generative design of protein binders

Denis Tarasov | Ulrich A. Mbou Sob | Miguel Arbesú | Nima Siboni | Sebastien Boyer | Andries Smit | Oliver Bent | Arnu Pretorius

NeurIPS workshop Dec 2023
a study on the application of offline Reinforcement Learning (RL) to address the bottleneck posed by the docking process, leveraging RL’s capability to optimize non-differentiable properties.
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