Publications

Feature Likelihood Score: Evaluating Generalization of Generative Models Using Samples
Marco Jiralerspong
Avishek Joey Bose
Gauthier Gidel
Deep generative models have demonstrated the ability to generate complex, high-dimensional, and photo-realistic data. However, a unified fr… (see more)amework for evaluating different generative modeling families remains a challenge. Indeed, likelihood-based metrics do not apply in many cases while pure sample-based metrics such as FID fail to capture known failure modes such as overfitting on training data. In this work, we introduce the Feature Likelihood Score (FLS), a parametric sample-based score that uses density estimation to quantitatively measure the quality/diversity of generated samples while taking into account overfitting. We empirically demonstrate the ability of FLS to identify specific overfitting problem cases, even when previously proposed metrics fail. We further perform an extensive experimental evaluation on various image datasets and model classes. Our results indicate that FLS matches intuitions of previous metrics, such as FID, while providing a more holistic evaluation of generative models that highlights models whose generalization abilities are under or overappreciated. Code for computing FLS is provided at https://github.com/marcojira/fls.
2023-01-01
arXiv.org (preprint)
doi.org
Filtering Pixel Latent Variables for Unmixing Volumetric Images
Catherine Bouchard
Vincent Boulanger
Flavie Lavoie-Cardinal
Christian Gagné
Measurements of different overlapping components require robust unmixing algorithms to convert the raw multi-dimensional measurements to use… (see more)ful unmixed images. Such algorithms perform reliable separation of the components when the raw signal is fully resolved and contains enough information to fit curves on the raw distributions. In experimental physics, measurements are often noisy, undersam-pled, or unresolved spatially or spectrally. We propose a novel method where bandpass filters are applied to the latent space of a multi-dimensional convolutional neural network to separate the overlapping signal components and extract each of their relative contributions. Simultaneously processing all dimensions with multi-dimensional convolution kernels empowers the network to combine the information from adjacent pixels and time-or spectral-bins, facilitating component separation in instances where individual pixels lack well-resolved information. We demonstrate the applicability of the method to real experimental physics problems using fluorescence lifetime microscopy and mode decomposition in optical fibers as test cases. The successful application of our approach to these two distinct experimental cases, characterized by different measured distributions, highlights the versatility of our approach in addressing a wide array of imaging tasks.
2023-01-01
arXiv.org (preprint)
doi.org
Filtering Pixel Latent Variables for Unmixing Volumetric Images
Catherine Bouchard
Vincent Boulanger
Flavie Lavoie-Cardinal
Christian Gagné
Measurements of different overlapping components require robust unmixing algorithms to convert the raw multi-dimensional measurements to use… (see more)ful unmixed images. Such algorithms perform reliable separation of the components when the raw signal is fully resolved and contains enough information to fit curves on the raw distributions. In experimental physics, measurements are often noisy, undersam-pled, or unresolved spatially or spectrally. We propose a novel method where bandpass filters are applied to the latent space of a multi-dimensional convolutional neural network to separate the overlapping signal components and extract each of their relative contributions. Simultaneously processing all dimensions with multi-dimensional convolution kernels empowers the network to combine the information from adjacent pixels and time-or spectral-bins, facilitating component separation in instances where individual pixels lack well-resolved information. We demonstrate the applicability of the method to real experimental physics problems using fluorescence lifetime microscopy and mode decomposition in optical fibers as test cases. The successful application of our approach to these two distinct experimental cases, characterized by different measured distributions, highlights the versatility of our approach in addressing a wide array of imaging tasks.
2023-01-01
arXiv.org (preprint)
doi.org
Findings of the 1st Shared Task on Multi-lingual Multi-task Information Retrieval at MRL 2023
Francesco Tinner
David Ifeoluwa Adelani
Chris Emezue
Mammad Hajili
Omer Goldman
Muhammad Farid Adilazuarda
Muhammad Dehan Al Kautsar
Aziza Mirsaidova
Müge Kural
Dylan Massey
Chiamaka Ijeoma Chukwuneke
CHINEDU EMMANUEL MBONU
Damilola Oluwaseun Oloyede
Kayode Olaleye
Jonathan Atala
Benjamin A. Ajibade
Saksham Bassi
Rahul Aralikatte
Najoung Kim
Duygu Ataman
Large language models (LLMs) excel in language understanding and generation, especially in English which has ample public benchmarks for var… (see more)ious natural language processing (NLP) tasks. Nevertheless, their reliability across different languages and domains remains uncertain. Our new shared task introduces a novel benchmark to assess the ability of multilingual LLMs to comprehend and produce language under sparse settings, particularly in scenarios with under-resourced languages, with an emphasis on the ability to capture logical, factual, or causal relationships within lengthy text contexts. The shared task consists of two sub-tasks crucial to information retrieval: Named Entity Recognition (NER) and Reading Comprehension (RC), in 7 data-scarce languages: Azerbaijani, Igbo, Indonesian, Swiss German, Turkish, Uzbek and Yorùbá, which previously lacked annotated resources in information retrieval tasks. Our evaluation of leading LLMs reveals that, despite their competitive performance, they still have notable weaknesses such as producing output in the non-target language or providing counterfactual information that cannot be inferred from the context. As more advanced models emerge, the benchmark will remain essential for supporting fairness and applicability in information retrieval systems.
2023-01-01
MRL (published)
doi.org
Formal and Empirical Studies of Counting Behaviour in ReLU RNNs.
Nadine El-Naggar
Andrew Ryzhikov
Laure Daviaud
Pranava Madhyastha
Tillman Weyde
François Coste
Faissal Ouardi
Guillaume Rabusseau
2023-01-01
International Conference on Graphics and Interaction (published)
dblp.uni-trier.de
Formalizing locality for normative synaptic plasticity models
Colin Bredenberg
Ezekiel Williams
Cristina Savin
Blake Richards
Guillaume Lajoie
openreview.net
Formation of Giant Plasma Membrane Vesicles for Biological and Medical Applications: A Review
Yang Li
Songyang Liu
Wanyu Xu
Kemin Wang
Fengjiao He
Jianbo Liu
Plasma membrane vesicles (PMVs) are micron-sized biomembrane vesicles that are isolated directly from living cells. They retain the lipid an… (see more)d protein complexity of the plasma membrane of the parent cell...
2023-01-01
Sensors & Diagnostics (published)
doi.org
Formation of Giant Plasma Membrane Vesicles for Biological and Medical Applications: A Review
Yangping Li
Songyang Liu
Wanyu Xu
Kemin Wang
Feng-jiang He
Jianbo Liu
Plasma membrane vesicles (PMVs) are micron-sized biomembrane vesicles that are isolated directly from living cells. They retain the lipid an… (see more)d protein complexity of the plasma membrane of the parent cell...
2023-01-01
Sensors & Diagnostics (published)
doi.org
From Words to Blocks: Building Objects by Grounding Language Models with Reinforcement Learning
Michael Ahn
Anthony Brohan
Noah Brown
liang Dai
Dan Su
Holy Lovenia Ziwei Bryan Wilie
Tiezheng Yu
Willy Chung
Quyet V. Do
Paul Barde
Tristan Karch
Derek Nowrouzezahrai
C. Bonial
Mitchell Abrams
David R. Traum
Hyung Won
Le Hou
Shayne Longpre
Yi Zoph
William Tay … (see 32 more)
Eric Fedus
Xuezhi Li
Lasse Espeholt
Hubert Soyer
Remi Munos
Karen Si-801
Vlad Mnih
Tom Ward
Yotam Doron
Wenlong Huang
Pieter Abbeel
Deepak Pathak
Julia Kiseleva
Ziming Li
Mohammad Aliannejadi
Shrestha Mohanty
Maartje Ter Hoeve
Mikhail Burtsev
Alexey Skrynnik
Artem Zholus
A. Panov
Kavya Srinet
A. Szlam
Yuxuan Sun
Katja Hofmann
Marc-Alexandre Côté
Ahmed Hamid Awadallah
Linar Abdrazakov
Igor Churin
Putra Manggala
Kata Naszádi
Michiel Van Der Meer
Leveraging pre-trained language models to gen-001 erate action plans for embodied agents is an 002 emerging research direction. However, exe… (see more)-003 cuting instructions in real or simulated envi-004 ronments necessitates verifying the feasibility 005 of actions and their relevance in achieving a 006 goal. We introduce a novel method that in-007 tegrates a language model and reinforcement 008 learning for constructing objects in a Minecraft-009 like environment, based on natural language 010 instructions. Our method generates a set of 011 consistently achievable sub-goals derived from 012 the instructions and subsequently completes the 013 associated sub-tasks using a pre-trained RL pol-014 icy. We employ the IGLU competition, which 015 is based on the Minecraft-like simulator, as our 016 test environment, and compare our approach 017 to the competition’s top-performing solutions. 018 Our approach outperforms existing solutions in 019 terms of both the quality of the language model 020 and the quality of the structures built within the 021 IGLU environment. 022
2023-01-01
(published)
www.semanticscholar.org
Functional architecture of the aging brain
Roni Setton
Laetitia Mwilambwe-Tshilobo
Manesh Girn
Amber W. Lockrow
Giulia Baracchini
Alexander J. Lowe
Benjamin N. Cassidy
Jian Li
Wen-Ming Luh
Danilo Bzdok
Richard M. Leahy
Tian Ge
Daniel S. Margulies
Bratislav Mišić
Boris C Bernhardt
W. Dale Stevens
Felipe De Brigard
Prantik Kundu
Gary R. Turner
R. Nathan Spreng
The intrinsic functional connectome can reveal how a lifetime of learning and lived experience is represented in the functional architecture… (see more) of the aging brain. We investigated whether network dedifferentiation, a hallmark of brain aging, reflects a global shift in network dynamics, or comprises network-specific changes that reflect the changing landscape of aging cognition. We implemented a novel multi-faceted strategy involving multi-echo fMRI acquisition and de-noising, individualized cortical parcellation, and multivariate (gradient and edge-level) functional connectivity methods. Twenty minutes of resting-state fMRI data and cognitive assessments were collected in younger (n=181) and older (n=120) adults. Dimensionality in the BOLD signal was lower for older adults, consistent with global network dedifferentiation. Functional connectivity gradients were largely age-invariant. In contrast, edge-level connectivity showed widespread changes with age, revealing discrete, network-specific dedifferentiation patterns. Visual and somatosensory regions were more integrated within the functional connectome; default and frontoparietal regions showed greater coupling; and the dorsal attention network was less differentiated from transmodal regions. Associations with cognition suggest that the formation and preservation of integrated, large-scale brain networks supports complex cognitive abilities. However, into older adulthood, the connectome is dominated by large-scale network disintegration, global dedifferentiation and network-specific dedifferentiation associated with age-related cognitive change.
2023-01-01
(published)
www.semanticscholar.org
FusionRetro: Molecule Representation Fusion via Reaction Graph for Retrosynthetic Planning
Songtao Liu
Zhengkai Tu
Minkai Xu
Zuobai Zhang
Peilin Zhao
Jian Tang
Rex Ying
Lu Lin
Dinghao Wu
Retrosynthetic planning is a fundamental problem in drug discovery and organic chemistry, which aims to find a complete multi-step syntheti… (see more)c route from a set of starting materials to the target molecule, determining crucial process flow in chemical production. Existing approaches combine single-step retrosynthesis models and search algorithms to find synthetic routes. However, these approaches generally consider the two pieces in a decoupled manner, taking only the product as the input to predict the reactants per planning step and largely ignoring the important context information from other intermediates along the synthetic route. In this work, we perform a series of experiments to identify the limitations of this decoupled view and propose a novel retrosynthesis framework that also exploits context information for retrosynthetic planning. We view synthetic routes as reaction graphs, and propose to incorporate the context by three principled steps: encode molecules into embeddings, aggregate information over routes, and readout to predict reactants. The whole framework can be efficiently optimized in an end-to-end fashion. Comprehensive experiments show that by fusing in context information over routes, our model sig-nificantly improves the performance of retrosyn-thetic planning over baselines that are not context-aware, especially for long synthetic routes.
2023-01-01
(published)
www.semanticscholar.org
FusionRetro: Molecule Representation Fusion via In-Context Learning for Retrosynthetic Planning
Songtao Liu
Zhengkai Tu
Minkai Xu
Zuobai Zhang
Lu Lin
Rex Ying
Zhitao Ying
Jian Tang
Peilin Zhao
Dinghao Wu
2023-01-01
ICML (published)
openreview.net
openreview.net