Publications

A Tight and Unified Analysis of Gradient-Based Methods for a Whole Spectrum of Differentiable Games.

Waiss Azizian

2020-01-01

International Conference on Artificial Intelligence and Statistics (publié)

proceedings.mlr.press

Title : Differential functional neural circuitry behind autism subtypes with marked imbalance between social-communicative and restricted repetitive behavior symptom domains

Natasha Bertelsen

Isotta Landi

Richard A.I. Bethlehem

Jakob Seidlitz

Elena

Maria Busuoli

Veronica Mandelli

Eleonora Satta

Stavros Trakoshis

Bonnie Auyeung

Prantik Kundu

Eva Loth

Guillaume Dumas

Sarah Baumeister

Christian Beckmann

Sven Bölte

Thomas Bourgeron

Tony Charman

Sarah Durston

Christine Ecker … (voir 22 de plus)

Rosemary Holt

Mark Johnson

Emily J. H. Jones

Luke Mason

-. AndreasMeyer

Lindenberg

Carolin

Moessnang

Marianne

Oldehinkel

Antonio

Persico

Julian

Tillmann

Steven C. R. Williams

Will Spooren

Declan Murphy

Katherine Jan

Buitelaar

Simon Baron-Cohen

Meng-Chuan Lai

Michael V. Lombardo

Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly … (voir plus)differ within and between individuals and is underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here we developed a phenotypic stratification model that makes highly accurate (96-98%) out-of-sample SC=RRB, SC>RRB, and RRB>SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n=509), we find replicable somatomotor-perisylvian hypoconnectivity in the SC>RRB subtype versus a typically-developing (TD) comparison group. In contrast, replicable motor-anterior salience hyperconnectivity is apparent in the SC=RRB subtype versus TD. Autism-associated genes affecting astrocytes, excitatory, and inhibitory neurons are highly expressed specifically within SC>RRB hypoconnected networks, but not SC=RRB hyperconnected networks. SC-RRB balance subtypes may indicate different paths individuals take from genome, neural circuitry, to the clinical phenotype. (CIMH). Procedures were undertaken to optimize the MRI sequences for the best scanner-specific options, and phantoms and travelling heads were employed to assure standardization and quality assurance of the multisite image-acquisition 20 . Structural images were obtained using a 5.5 minute MPRAGE sequence (TR=2300ms, TE=2.93ms, T1=900ms, voxels size=1.1x1.1x1.2mm, flip angle=9°, matrix size=256x256, FOV=270mm, 176 slices). An eight-to-ten minute resting-state fMRI (rsfMRI) scan was acquired using a multi-echo planar imaging (ME-EPI) sequence 65,66 ; TR=2300ms, TE~12ms, 31ms, and 48ms (slight variations are present across centers), flip angle=80°, matrix size=64x64, (UMCU), 215 (KCL, CIMH), 265 (RUMC, UCAM). were to relax, with eyes open and fixate on a cross presented on the screen for the duration of the rsfMRI scan.

2020-01-01

(publié)

www.semanticscholar.org

Towards Queryable and Traceable Domain Models

Rijul Saini

Gunter Mussbacher

Jin Guo

Jörg Kienzle

Model-Driven Software Engineering encompasses various modelling formalisms for supporting software development. One such formalism is domain… (voir plus) modelling which bridges the gap between requirements expressed in natural language and analyzable and more concise domain models expressed in class diagrams. Due to the lack of modelling skills among novice modellers and time constraints in industrial projects, it is often not possible to build an accurate domain model manually. To address this challenge, we aim to develop an approach to extract domain models from problem descriptions written in natural language by combining rules based on natural language processing with machine learning. As a first step, we report on an automated and tool-supported approach with an accuracy of extracted domain models higher than existing approaches. In addition, the approach generates trace links for each model element of a domain model. The trace links enable novice modellers to execute queries on the extracted domain models to gain insights into the modelling decisions taken for improving their modelling skills. Furthermore, to evaluate our approach, we propose a novel comparison metric and discuss our experimental design. Finally, we present a research agenda detailing research directions and discuss corresponding challenges.

2020-01-01

2020 IEEE 28th International Requirements Engineering Conference (RE) (publié)

doi.org

Towards robust and replicable sex differences in the intrinsic brain 1 function of autism 2 3

Dorothea L. Floris

José O. A. Filho

Meng-Chuan Lai

Steve

Giavasis

Marianne Oldehinkel

Maarten Mennes

Tony Charman

Julian

Tillmann

Guillaume Dumas

Christine Ecker

Flavio Dell’Acqua

Tobias Banaschewski

Carolin Moessnang

Simon Baron-Cohen

Sarah

Durston

Eva Loth

Declan Murphy … (voir 4 de plus)

Jan K. Buitelaar

Christian Beckmann

Michael P. Milham

A. Martino

84 Background: Marked sex differences in autism prevalence accentuate the need to understand 85 the role of biological sex-related factors i… (voir plus)n autism. Efforts to unravel sex differences in the 86 brain organization of autism have, however, been challenged by the limited availability of 87 female data. Methods: We addressed this gap by using a large sample of males and females 88 with autism and neurotypical (NT) control individuals (ABIDE; Autism: 362 males, 82 89 females; NT: 409 males, 166 females; 7-18 years). Discovery analyses examined main effects 90 of diagnosis, sex and their interaction across five resting-state fMRI (R-fMRI) metrics 91 (voxel-level Z > 3.1, cluster-level P 0.01, gaussian random field corrected). Secondary 92 analyses assessed the robustness of the results to different pre-processing approaches and 93 their replicability in two independent samples: the EU-AIMS Longitudinal European Autism 94 Project (LEAP) and the Gender Explorations of Neurogenetics and Development to Advance 95 Autism Research (GENDAAR). Results: Discovery analyses in ABIDE revealed significant 96 main effects across the intrinsic functional connectivity (iFC) of the posterior cingulate 97 cortex, regional homogeneity and voxel-mirrored homotopic connectivity (VMHC) in several 98 cortical regions, largely converging in the default network midline. Sex-by-diagnosis 99 interactions were confined to the dorsolateral occipital cortex, with reduced VMHC in 100 females with autism. All findings were robust to different pre-processing steps. Replicability 101 in independent samples varied by R-fMRI measures and effects with the targeted sex-by102 diagnosis interaction being replicated in the larger of the two replication samples – EU-AIMS 103 LEAP. Limitations: Given the lack of a priori harmonization among the discovery and 104 replication datasets available to date, sample-related variation remained and may have 105 affected replicability. Conclusions: Atypical cross-hemispheric interactions are 106 neurobiologically relevant to autism. They likely result from the combination of sex107

2020-01-01

(publié)

www.semanticscholar.org

On Variational Learning of Controllable Representations for Text without Supervision

Peng Xu

Yanshuai Cao

Jackie Cheung

The variational autoencoder (VAE) can learn the manifold of natural images on certain datasets, as evidenced by meaningful interpolating or … (voir plus)extrapolating in the continuous latent space. However, on discrete data such as text, it is unclear if unsupervised learning can discover similar latent space that allows controllable manipulation. In this work, we find that sequence VAEs trained on text fail to properly decode when the latent codes are manipulated, because the modified codes often land in holes or vacant regions in the aggregated posterior latent space, where the decoding network fails to generalize. Both as a validation of the explanation and as a fix to the problem, we propose to constrain the posterior mean to a learned probability simplex, and performs manipulation within this simplex. Our proposed method mitigates the latent vacancy problem and achieves the first success in unsupervised learning of controllable representations for text. Empirically, our method outperforms unsupervised baselines and strong supervised approaches on text style transfer, and is capable of performing more flexible fine-grained control over text generation than existing methods.

2020-01-01

ICML (publié)

proceedings.mlr.press

openreview.net

You could have said that instead: Improving Chatbots with Natural Language Feedback

Makesh Narsimhan Sreedhar

Kun Ni

Siva Reddy

The ubiquitous nature of dialogue systems and their interaction with users generate an enormous amount of data. Can we improve chatbots usin… (voir plus)g this data? A self-feeding chatbot improves itself by asking natural language feedback when a user is dissatisfied with its response and uses this feedback as an additional training sample. However, user feedback in most cases contains extraneous sequences hindering their usefulness as a training sample. In this work, we propose a generative adversarial model that converts noisy feedback into a plausible natural response in a conversation. The generator’s goal is to convert the feedback into a response that answers the user’s previous utterance and to fool the discriminator which distinguishes feedback from natural responses. We show that augmenting original training data with these modified feedback responses improves the original chatbot performance from 69.94%to 75.96% in ranking correct responses on the PERSONACHATdataset, a large improvement given that the original model is already trained on 131k samples.

2020-01-01

Conference on Empirical Methods in Natural Language Processing (publié)

doi.org

arxiv.org

Interactive Psychometrics for Autism with the Human Dynamic Clamp: Interpersonal Synchrony from Sensory-motor to Socio-cognitive Domains

Florence Baillin

Aline Lefebvre

Amandine Pedoux

Yann Beauxis

Denis-Alexander Engemann

Anna Maruani

Frederique Amsellem

Thomas Bourgeron

Richard Delorme

Guillaume Dumas

2019-12-06

(publié)

doi.org

Neuropsychiatric mutations delineate functional brain connectivity dimensions contributing to autism and schizophrenia

Clara A. Moreau

Sebastian Urchs

Pierre Orban

Catherine Schramm

Guillaume Dumas

Aurélie Labbe

Guillaume Huguet

Elise Douard

Pierre-Olivier Quirion

Amy Lin

Leila Kushan

Stephanie Grot

David Luck

Adrianna Mendrek

Stephane Potvin

Emmanuel Stip

Thomas Bourgeron

Alan C. Evans

Carrie E. Bearden

Pierre (Louis) Bellec … (voir 1 de plus)

Sébastien Jacquemont

16p11.2 and 22q11.2 Copy Number Variants (CNVs) confer high risk for Autism Spectrum Disorder (ASD), schizophrenia (SZ), and Attention-Defic… (voir plus)it-Hyperactivity-Disorder (ADHD), but their impact on functional connectivity (FC) remains unclear. We analyzed resting-state functional magnetic resonance imaging data from 101 CNV carriers, 755 individuals with idiopathic ASD, SZ, or ADHD and 1,072 controls. We used CNV FC-signatures to identify dimensions contributing to complex idiopathic conditions. CNVs had large mirror effects on FC at the global and regional level. Thalamus, somatomotor, and posterior insula regions played a critical role in dysconnectivity shared across deletions, duplications, idiopathic ASD, SZ but not ADHD. Individuals with higher similarity to deletion FC-signatures exhibited worse cognitive and behavioral symptoms. Deletion similarities identified at the connectivity level could be related to the redundant associations observed genome-wide between gene expression spatial patterns and FC-signatures. Results may explain why many CNVs affect a similar range of neuropsychiatric symptoms.

2019-12-06

bioRxiv (prépublication)

doi.org

Approximate information state for partially observed systems

Jayakumar Subramanian

Aditya Mahajan

The standard approach for modeling partially observed systems is to model them as partially observable Markov decision processes (POMDPs) an… (voir plus)d obtain a dynamic program in terms of a belief state. The belief state formulation works well for planning but is not ideal for online reinforcement learning because the belief state depends on the model and, as such, is not observable when the model is unknown.In this paper, we present an alternative notion of an information state for obtaining a dynamic program in partially observed models. In particular, an information state is a sufficient statistic for the current reward which evolves in a controlled Markov manner. We show that such an information state leads to a dynamic programming decomposition. Then we present a notion of an approximate information state and present an approximate dynamic program based on the approximate information state. Approximate information state is defined in terms of properties that can be estimated using sampled trajectories. Therefore, they provide a constructive method for reinforcement learning in partially observed systems. We present one such construction and show that it performs better than the state of the art for three benchmark models.

2019-12-01

IEEE Conference on Decision and Control (publié)

doi.org

Expressiveness and Learning of Hidden Quantum Markov Models

Sandesh M. Adhikary

Siddarth Srinivasan

Geoff Gordon

Byron Boots

Extending classical probabilistic reasoning using the quantum mechanical view of probability has been of recent interest, particularly in th… (voir plus)e development of hidden quantum Markov models (HQMMs) to model stochastic processes. However, there has been little progress in characterizing the expressiveness of such models and learning them from data. We tackle these problems by showing that HQMMs are a special subclass of the general class of observable operator models (OOMs) that do not suffer from the \emph{negative probability problem} by design. We also provide a feasible retraction-based learning algorithm for HQMMs using constrained gradient descent on the Stiefel manifold of model parameters. We demonstrate that this approach is faster and scales to larger models than previous learning algorithms.

2019-12-01

ArXiv (preprint)

arxiv.org