Publications

Seeing Through Your Skin: A Novel Visuo-Tactile Sensor for Robotic Manipulation

Francois Hogan

Sahand Rezaei-Shoshtari

M. Jenkin

Yashveer Girdhar

David Meger

Gregory Dudek

This work describes the development of the novel tactile sensor, named Semitransparent Tactile Sensor (STS), designed to enable reactive and… (voir plus) robust manipulation skills. The design, inspired from recent developments in optical tactile sensing technology, addresses a key missing features of these sensors: the ability to capture an “in the hand” perspective prior to and during the contact interaction. Whereas optical tactile sensors are typically opaque and obscure the view of the object at the critical moment prior to manipulator-object contact, we present a sensor that has the dual capabilities of acting as a tactile sensor and as a visual camera. This paper details the design and fabrication of the sensor, showcases its dual sensing capabilities, and introduces a simulated environment of the sensor within the PyBullet simulator.

2019-12-31

(publié)

3D Shape Reconstruction from Vision and Touch

Edward J. Smith

Roberto Calandra

Adriana Romero

Georgia Gkioxari

David Meger

Jitendra Malik

Michal Drozdzal

When a toddler is presented a new toy, their instinctual behaviour is to pick it up and inspect it with their hand and eyes in tandem, clear… (voir plus)ly searching over its surface to properly understand what they are playing with. Here, touch provides high fidelity localized information while vision provides complementary global context. However, in 3D shape reconstruction, the complementary fusion of visual and haptic modalities remains largely unexplored. In this paper, we study this problem and present an effective chart-based approach to fusing vision and touch, which leverages advances in graph convolutional networks. To do so, we introduce a dataset of simulated touch and vision signals from the interaction between a robotic hand and a large array of 3D objects. Our results show that (1) leveraging both vision and touch signals consistently improves single-modality baselines; (2) our approach outperforms alternative modality fusion methods and strongly benefits from the proposed chart-based structure; (3) the reconstruction quality increases with the number of grasps provided; and (4) the touch information not only enhances the reconstruction at the touch site but also extrapolates to its local neighborhood.

2019-12-31

Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (publié)

arxiv.org

Spike-based causal inference for weight alignment

Jordan Guerguiev

Konrad Paul Kording

Blake Aaron Richards

In artificial neural networks trained with gradient descent, the weights used for processing stimuli are also used during backward passes to… (voir plus) calculate gradients. For the real brain to approximate gradients, gradient information would have to be propagated separately, such that one set of synaptic weights is used for processing and another set is used for backward passes. This produces the so-called "weight transport problem" for biological models of learning, where the backward weights used to calculate gradients need to mirror the forward weights used to process stimuli. This weight transport problem has been considered so hard that popular proposals for biological learning assume that the backward weights are simply random, as in the feedback alignment algorithm. However, such random weights do not appear to work well for large networks. Here we show how the discontinuity introduced in a spiking system can lead to a solution to this problem. The resulting algorithm is a special case of an estimator used for causal inference in econometrics, regression discontinuity design. We show empirically that this algorithm rapidly makes the backward weights approximate the forward weights. As the backward weights become correct, this improves learning performance over feedback alignment on tasks such as Fashion-MNIST, SVHN, CIFAR-10 and VOC. Our results demonstrate that a simple learning rule in a spiking network can allow neurons to produce the right backward connections and thus solve the weight transport problem.

2019-12-31

ICLR.cc/2020/Conference (poster)

openreview.net

Systematicity in a Recurrent Neural Network by Factorizing Syntax and Semantics

Jacob Russin

Jason Jo

R. O’Reilly

Standard methods in deep learning fail to capture compositional or systematic structure in their training data, as shown by their inability … (voir plus)to generalize outside of the training distribution. However, human learners readily generalize in this way, e.g. by applying known grammatical rules to novel words. The inductive biases that might underlie this powerful cognitive capacity remain unclear. Inspired by work in cognitive science suggesting a functional distinction between systems for syntactic and semantic processing, we implement a modiﬁcation to an existing deep learning architecture, imposing an analogous separation. The resulting architecture substantially out-performs standard recurrent networks on the SCAN dataset, a compositional generalization task, without any additional supervision. Our work suggests that separating syntactic from semantic learning may be a useful heuristic for capturing compositional structure, and highlights the potential of using cognitive principles to inform inductive biases in deep learning.

2019-12-31

CogSci (publié)

dblp.uni-trier.de

TDprop: Does Jacobi Preconditioning Help Temporal Difference Learning?

We investigate whether Jacobi preconditioning, accounting for the bootstrap term in temporal difference (TD) learning, can help boost perfor… (voir plus)mance of adaptive optimizers. Our method, TDprop, computes a per parameter learning rate based on the diagonal preconditioning of the TD update rule. We show how this can be used in both

2019-12-31

arXiv (prépublication)

arxiv.org

On the Morality of Artificial Intelligence

Alexandra Luccioni

Much of the existing research on the social and ethical impact of Artificial Intelligence has been focused on defining ethical principles an… (voir plus)d guidelines surrounding Machine Learning (ML) and other Artificial Intelligence (AI) algorithms [IEEE, 2017, Jobin et al., 2019]. While this is extremely useful for helping define the appropriate social norms of AI, we believe that it is equally important to discuss both the potential and risks of ML and to inspire the community to use ML for beneficial objectives. In the present article, which is specifically aimed at ML practitioners, we thus focus more on the latter, carrying out an overview of existing high-level ethical frameworks and guidelines, but above all proposing both conceptual and practical principles and guidelines for ML research and deployment, insisting on concrete actions that can be taken by practitioners to pursue a more ethical and moral practice of ML aimed at using AI for social good.

2019-12-31

IEEE Technology and Society Magazine (publié)

arxiv.org

The Variational Bandwidth Bottleneck: Stochastic Evaluation on an Information Budget

Anirudh Goyal

Matthew Botvinick

Sergey Levine

In many applications, it is desirable to extract only the relevant information from complex input data, which involves making a decision abo… (voir plus)ut which input features are relevant. The information bottleneck method formalizes this as an information-theoretic optimization problem by maintaining an optimal tradeoff between compression (throwing away irrelevant input information), and predicting the target. In many problem settings, including the reinforcement learning problems we consider in this work, we might prefer to compress only part of the input. This is typically the case when we have a standard conditioning input, such as a state observation, and a "privileged" input, which might correspond to the goal of a task, the output of a costly planning algorithm, or communication with another agent. In such cases, we might prefer to compress the privileged input, either to achieve better generalization (e.g., with respect to goals) or to minimize access to costly information (e.g., in the case of communication). Practical implementations of the information bottleneck based on variational inference require access to the privileged input in order to compute the bottleneck variable, so although they perform compression, this compression operation itself needs unrestricted, lossless access. In this work, we propose the variational bandwidth bottleneck, which decides for each example on the estimated value of the privileged information before seeing it, i.e., only based on the standard input, and then accordingly chooses stochastically, whether to access the privileged input or not. We formulate a tractable approximation to this framework and demonstrate in a series of reinforcement learning experiments that it can improve generalization and reduce access to computationally costly information.

2019-12-31

ICLR (publié)

openreview.net

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.

2019-12-31

(publié)

Toward Training Recurrent Neural Networks for Lifelong Learning.

Shagun Sodhani

Sarath Chandar

Catastrophic forgetting and capacity saturation are the central challenges of any parametric lifelong learning system. In this work, we stud… (voir plus)y these challenges in the context of sequential supervised learning with an emphasis on recurrent neural networks. To evaluate the models in the lifelong learning setting, we propose a curriculum-based, simple, and intuitive benchmark where the models are trained on tasks with increasing levels of difficulty. To measure the impact of catastrophic forgetting, the model is tested on all the previous tasks as it completes any task. As a step toward developing true lifelong learning systems, we unify gradient episodic memory (a catastrophic forgetting alleviation approach) and Net2Net (a capacity expansion approach). Both models are proposed in the context of feedforward networks, and we evaluate the feasibility of using them for recurrent networks. Evaluation on the proposed benchmark shows that the unified model is more suitable than the constituent models for lifelong learning setting.

2019-12-31

Neural Computation (publié)

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

2019-12-31

(publié)