Publications

Interpretable Machine Learning for Finding Intermediate-mass Black Holes
Mario Pasquato
Piero Trevisan
Abbas Askar
Pablo Lemos
Gaia Carenini
Michela Mapelli
Yashar Hezaveh
2024-04-09
The Astrophysical Journal (publié)
doi.org
arxiv.org
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Parishad BehnamGhader
Vaibhav Adlakha
Marius Mosbach
Dzmitry Bahdanau
Nicolas Chapados
Siva Reddy
2024-04-09
ArXiv (prépublication)
doi.org
arxiv.org
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Parishad BehnamGhader
Vaibhav Adlakha
Marius Mosbach
Dzmitry Bahdanau
Nicolas Chapados
Siva Reddy
Large decoder-only language models (LLMs) are the state-of-the-art models on most of today's NLP tasks and benchmarks. Yet, the community is… (voir plus) only slowly adopting these models for text embedding tasks, which require rich contextualized representations. In this work, we introduce LLM2Vec, a simple unsupervised approach that can transform any decoder-only LLM into a strong text encoder. LLM2Vec consists of three simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. We demonstrate the effectiveness of LLM2Vec by applying it to 3 popular LLMs ranging from 1.3B to 7B parameters and evaluate the transformed models on English word- and sequence-level tasks. We outperform encoder-only models by a large margin on word-level tasks and reach a new unsupervised state-of-the-art performance on the Massive Text Embeddings Benchmark (MTEB). Moreover, when combining LLM2Vec with supervised contrastive learning, we achieve state-of-the-art performance on MTEB among models that train only on publicly available data. Our strong empirical results and extensive analysis demonstrate that LLMs can be effectively transformed into universal text encoders in a parameter-efficient manner without the need for expensive adaptation or synthetic GPT-4 generated data.
2024-04-09
ArXiv (prépublication)
doi.org
arxiv.org
Structure-function coupling and decoupling during movie-watching and resting-state: Novel insights bridging EEG and structural imaging
Venkatesh Subramani
Giulia Lioi
Karim Jerbi
Nicolas Farrugia
2024-04-09
bioRxiv (prépublication)
doi.org
Learning Heuristics for Transit Network Design and Improvement with Deep Reinforcement Learning
Andrew Holliday
A. El-geneidy
Gregory Dudek
2024-04-08
ArXiv (prépublication)
doi.org
arxiv.org
Learning Minimal NAP Specifications for Neural Network Verification
Chuqin Geng
Zhaoyue Wang
Haolin Ye
Saifei Liao
Xujie Si
Specifications play a crucial role in neural network verification. They define the precise input regions we aim to verify, typically represe… (voir plus)nted as L-infinity norm balls. While recent research suggests using neural activation patterns (NAPs) as specifications for verifying unseen test set data, it focuses on computing the most refined NAPs, often limited to very small regions in the input space. In this paper, we study the following problem: Given a neural network, find a minimal (coarsest) NAP that is sufficient for formal verification of the network's robustness. Finding the minimal NAP specification not only expands verifiable bounds but also provides insights into which neurons contribute to the model's robustness. To address this problem, we propose several exact and approximate approaches. Our exact approaches leverage the verification tool to find minimal NAP specifications in either a deterministic or statistical manner. Whereas the approximate methods efficiently estimate minimal NAPs using adversarial examples and local gradients, without making calls to the verification tool. This allows us to inspect potential causal links between neurons and the robustness of state-of-the-art neural networks, a task for which existing verification frameworks fail to scale. Our experimental results suggest that minimal NAP specifications require much smaller fractions of neurons compared to the most refined NAP specifications, yet they can significantly expand the verifiable boundaries to several orders of magnitude larger.
2024-04-06
ArXiv (prépublication)
doi.org
arxiv.org
SAT-DIFF: A Tree Diffing Framework Using SAT Solving
Chuqin Geng
Haolin Ye
Yihan Zhang
Brigitte Pientka
Xujie Si
Computing differences between tree-structured data is a critical but challenging problem in software analysis. In this paper, we propose a n… (voir plus)ovel tree diffing approach called SatDiff, which reformulates the structural diffing problem into a MaxSAT problem. By encoding the necessary transformations from the source tree to the target tree, SatDiff generates correct, minimal, and type safe low-level edit scripts with formal guarantees. We then synthesize concise high-level edit scripts by effectively merging low-level edits in the appropriate topological order. Our empirical results demonstrate that SatDiff outperforms existing heuristic-based approaches by a significant margin in terms of conciseness while maintaining a reasonable runtime.
2024-04-06
ArXiv (prépublication)
doi.org
arxiv.org
PopulAtion Parameter Averaging (PAPA)
Alexia Jolicoeur-Martineau
Emy Gervais
Kilian FATRAS
Yan Zhang
Simon Lacoste-Julien
2024-04-05
TMLR (accepté)
doi.org
openreview.net
Regulating advanced artificial agents
Michael K. Cohen
Noam Kolt
Yoshua Bengio
Gillian K. Hadfield
Stuart Russell
2024-04-05
Science (publié)
doi.org
Applying Recurrent Neural Networks and Blocked Cross-Validation to Model Conventional Drinking Water Treatment Processes
Aleksandar Jakovljevic
Laurent Charlin
Benoit Barbeau
The jar test is the current standard method for predicting the performance of a conventional drinking water treatment (DWT) process and opti… (voir plus)mizing the coagulant dose. This test is time-consuming and requires human intervention, meaning it is infeasible for making continuous process predictions. As a potential alternative, we developed a machine learning (ML) model from historical DWT plant data that can operate continuously using real-time sensor data without human intervention for predicting clarified water turbidity 15 min in advance. We evaluated three types of models: multilayer perceptron (MLP), the long short-term memory (LSTM) recurrent neural network (RNN), and the gated recurrent unit (GRU) RNN. We also employed two training methodologies: the commonly used holdout method and the theoretically correct blocked cross-validation (BCV) method. We found that the RNN with GRU was the best model type overall and achieved a mean absolute error on an independent production set of as low as 0.044 NTU. We further found that models trained using BCV typically achieve errors equal to or lower than their counterparts trained using holdout. These results suggest that RNNs trained using BCV are superior for the development of ML models for DWT processes compared to those reported in earlier literature.
2024-04-04
Water (publié)
doi.org
Assessing the emergence time of SARS-CoV-2 zoonotic spillover
Stéphane Samson
Étienne Lord
Vladimir Makarenkov
2024-04-04
PLoS ONE (publié)
doi.org
Layerwise Early Stopping for Test Time Adaptation
Sabyasachi Sahoo
Mostafa ElAraby
Jonas Ngnawe
Yann Batiste Pequignot
Frederic Precioso
Christian Gagné
2024-04-04
ArXiv (prépublication)
doi.org
arxiv.org