Publications

The World Health Organization as an engine of ideational robustness
Jean-Louis Denis
Gaelle Foucault
Pierre Larouche
Catherine Régis
Miriam Cohen
Marie-Andree Girard
2024-03-05
Policy and Society (publié)
doi.org
Enhancing and Evaluating Logical Reasoning Abilities of Large Language Models
Shujie Deng
Honghua Dong
Xujie Si
2024-03-04
ICLR.cc/2024/Workshop/SeT_LLM (publié)
openreview.net
openreview.net
A Generative Model of Symmetry Transformations
James U. Allingham
Bruno Mlodozeniec
Shreyas Padhy
Javier Antor'an
David Scott Krueger
Richard E. Turner
Eric T. Nalisnick
Jos'e Miguel Hern'andez-Lobato
Correctly capturing the symmetry transformations of data can lead to efficient models with strong generalization capabilities, though method… (voir plus)s incorporating symmetries often require prior knowledge. While recent advancements have been made in learning those symmetries directly from the dataset, most of this work has focused on the discriminative setting. In this paper, we construct a generative model that explicitly aims to capture symmetries in the data, resulting in a model that learns which symmetries are present in an interpretable way. We provide a simple algorithm for efficiently learning our generative model and demonstrate its ability to capture symmetries under affine and color transformations. Combining our symmetry model with existing generative models results in higher marginal test-log-likelihoods and robustness to data sparsification.
2024-03-04
ArXiv (prépublication)
doi.org
arxiv.org
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… (voir plus)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.
2024-03-04
ArXiv (prépublication)
doi.org
arxiv.org
Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok
Tikeng Notsawo Pascal Junior
Pascal Notsawo
Hattie Zhou
Mohammad Pezeshki
Irina Rish
Guillaume Dumas
2024-03-04
ICLR.cc/2024/Workshop/ME-FoMo (poster)
doi.org
openreview.net
Self-evaluation and self-prompting to improve the reliability of LLMs
Alexandre Piché
Aristides Milios
Dzmitry Bahdanau
Chris Pal
In order to safely deploy Large Language Models (LLMs), they must be capable of dynamically adapting their behavior based on their level of … (voir plus)knowledge and uncertainty associated with specific topics. This adaptive behavior, which we refer to as self-restraint, is non-trivial to teach since it depends on the internal knowledge of an LLM. By default, LLMs are trained to maximize the next token likelihood which does not teach the model to modulate its answer based on its level of uncertainty. In order to learn self-restraint, we devise a simple objective that can encourage the model to produce generation that the model is confident in. To optimize this objective, we introduce ReSearch, an iterative search algorithm based on self-evaluation and self-prompting. Our method results in fewer hallucinations overall, both for known and unknown topics, as the model learns to selectively restrain itself. In addition, our method elegantly incorporates the ability to decline, when the model assesses that it cannot provide a response without a high proportion of hallucination.
2024-03-04
ICLR.cc/2024/Workshop/SeT_LLM (publié)
openreview.net
openreview.net
Self-evaluation and self-prompting to improve the reliability of LLMs
Alexandre Piché
Aristides Milios
Dzmitry Bahdanau
Chris Pal
In order to safely deploy Large Language Models (LLMs), they must be capable of dynamically adapting their behavior based on their level of … (voir plus)knowledge and uncertainty associated with specific topics. This adaptive behavior, which we refer to as self-restraint, is non-trivial to teach since it depends on the internal knowledge of an LLM. By default, LLMs are trained to maximize the next token likelihood which does not teach the model to modulate its answer based on its level of uncertainty. In order to learn self-restraint, we devise a simple objective that can encourage the model to produce generation that the model is confident in. To optimize this objective, we introduce ReSearch, an iterative search algorithm based on self-evaluation and self-prompting. Our method results in fewer hallucinations overall, both for known and unknown topics, as the model learns to selectively restrain itself. In addition, our method elegantly incorporates the ability to decline, when the model assesses that it cannot provide a response without a high proportion of hallucination.
2024-03-04
ICLR.cc/2024/Workshop/SeT_LLM (publié)
openreview.net
openreview.net
On the Scalability of GNNs for Molecular Graphs
Maciej Sypetkowski
Frederik Wenkel
Farimah Poursafaei
Nia Dickson
Karush Suri
Philip Fradkin
Dominique Beaini
Scaling deep learning models has been at the heart of recent revolutions in language modelling and image generation. Practitioners have obse… (voir plus)rved a strong relationship between model size, dataset size, and performance. However, structure-based architectures such as Graph Neural Networks (GNNs) are yet to show the benefits of scale mainly due to the lower efficiency of sparse operations, large data requirements, and lack of clarity about the effectiveness of various architectures. We address this drawback of GNNs by studying their scaling behavior. Specifically, we analyze message-passing networks, graph Transformers, and hybrid architectures on the largest public collection of 2D molecular graphs. For the first time, we observe that GNNs benefit tremendously from the increasing scale of depth, width, number of molecules, number of labels, and the diversity in the pretraining datasets, resulting in a 30.25% improvement when scaling to 1 billion parameters and 28.98% improvement when increasing size of dataset to eightfold. We further demonstrate strong finetuning scaling behavior on 38 tasks, outclassing previous large models. We hope that our work paves the way for an era where foundational GNNs drive pharmaceutical drug discovery.
2024-03-04
ICLR.cc/2024/Workshop/DPFM (poster)
openreview.net
openreview.net
Towards DNA-Encoded Library Generation with GFlowNets
Michał Koziarski
Mohammed Abukalam
Vedant Shah
Louis Vaillancourt
Doris Alexandra Schuetz
Moksh J. Jain
Almer M. van der Sloot
Mathieu Bourgey
Anne Marinier
Yoshua Bengio
2024-03-04
ICLR.cc/2024/Workshop/GEM (poster)
doi.org
openreview.net
Communicating Study Design Trade-offs in Software Engineering
Martin P. Robillard
Deeksha M. Arya
Neil A. Ernst
Jin Guo
Maxime Lamothe
Mathieu Nassif
Nicole Novielli
Alexander Serebrenik
Igor Steinmacher
Klaas-Jan Stol
2024-03-02
ACM Transactions on Software Engineering and Methodology (publié)
doi.org
A Compositional Typed Semantics for Universal Dependencies
Laurestine Bradford
Timothy John O'donnell
Siva Reddy
2024-03-02
ArXiv (prépublication)
doi.org
arxiv.org
Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis
Stefan Horoi
Albert Manuel Orozco Camacho
Eugene Belilovsky
Guy Wolf
Ensembling multiple models enhances predictive performance by utilizing the varied learned features of the different models but incurs signi… (voir plus)ficant computational and storage costs. Model fusion, which combines parameters from multiple models into one, aims to mitigate these costs but faces practical challenges due to the complex, non-convex nature of neural network loss landscapes, where learned minima are often separated by high loss barriers. Recent works have explored using permutations to align network features, reducing the loss barrier in parameter space. However, permutations are restrictive since they assume a one-to-one mapping between the different models' neurons exists. We propose a new model merging algorithm, CCA Merge, which is based on Canonical Correlation Analysis and aims to maximize the correlations between linear combinations of the model features. We show that our method of aligning models leads to better performances than past methods when averaging models trained on the same, or differing data splits. We also extend this analysis into the harder many models setting where more than 2 models are merged, and we find that CCA Merge works significantly better in this setting than past methods.
2024-03-02
ICLR.cc/2024/Workshop/Re-Align (poster)
openreview.net
openreview.net