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)

www.semanticscholar.org

SENET: A Semantic Web for Supporting Automation of Software Engineering Tasks

Yalin Liu

Jinfeng Lin

Jane Cleland-Huang

Michael Vierhauser

Jin L.C. Guo

Sugandha Lohar

The use of Natural Language (NL) interfaces to allow devices and applications to respond to verbal commands or free-form textual queries is … (voir plus)becoming increasingly prevalent in our society. To a large extent, their success in interpreting and responding to a request is dependent upon rich underlying ontologies and conceptual models that understand the technical or domain specific vocabulary of diverse users. The effective use of NL interfaces in the Software Engineering (SE) domains requires its own ontology models focusing upon software related terms and concepts. While many SE glossaries exist, they are often incomplete and tend to define the vocabulary for specific sub-fields without capturing associations between terms and phrases. This limits their usefulness for supporting NL-related tasks. In this paper we propose an approach for constructing and evolving a semantic network of software engineering concepts and phrases. Our approach starts with a set of existing SE glossaries, uses the existing glossary terms and explicitly defined associations as a starting point, uses machine learning-based techniques to dynamically identify and document additional associations between terms, leverages the network to interpret NL queries in the SE domain, and finally augments the resulting semantic network with feedback provided by users. We evaluate the viability of our approach within the sub-domain of Agile Software Development, focusing on requirements related queries, and show that the semantic network enhances the ability of an NL interface to correctly interpret and execute user queries.

2019-12-31

2020 IEEE Seventh International Workshop on Artificial Intelligence for Requirements Engineering (AIRE) (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)

Small-GAN: Speeding Up GAN Training Using Core-Sets

Samarth Sinha

Han Zhang

Anirudh Goyal

Yoshua Bengio

Hugo Larochelle

Augustus Odena

Recent work by Brock et al. (2018) suggests that Generative Adversarial Networks (GANs) benefit disproportionately from large mini-batch siz… (voir plus)es. Unfortunately, using large batches is slow and expensive on conventional hardware. Thus, it would be nice if we could generate batches that were effectively large though actually small. In this work, we propose a method to do this, inspired by the use of Coreset-selection in active learning. When training a GAN, we draw a large batch of samples from the prior and then compress that batch using Coreset-selection. To create effectively large batches of 'real' images, we create a cached dataset of Inception activations of each training image, randomly project them down to a smaller dimension, and then use Coreset-selection on those projected activations at training time. We conduct experiments showing that this technique substantially reduces training time and memory usage for modern GAN variants, that it reduces the fraction of dropped modes in a synthetic dataset, and that it allows GANs to reach a new state of the art in anomaly detection.

2019-12-31

ICML (publié)

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

A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry

Baihan Lin

Guillermo Cecchi

Djallel Bouneffouf

Jenna Reinen

Irina Rish

Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework fo… (voir plus)r reinforcement learning that extends standard Q-learning to a two-stream model for processing positive and negative rewards, and allows to incorporate a wide range of reward-processing biases -- an important component of human decision making which can help us better understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems, as well as various neuropsychiatric conditions associated with disruptions in normal reward processing. From the computational perspective, we observe that the proposed Split-QL model and its clinically inspired variants consistently outperform standard Q-Learning and SARSA methods, as well as recently proposed Double Q-Learning approaches, on simulated tasks with particular reward distributions, a real-world dataset capturing human decision-making in gambling tasks, and the Pac-Man game in a lifelong learning setting across different reward stationarities.

2019-12-31

AAMAS (publié)

Structured Conditional Continuous Normalizing Flows for Efficient Amortized Inference in Graphical Models

Christian Dietrich Weilbach

Boyan Beronov

Frank N. Wood

William Harvey

We exploit minimally faithful inversion of graphical model structures to specify sparse continuous normalizing ﬂows (CNFs) for amortized i… (voir plus)nference. We ﬁnd that the sparsity of this factorization can be exploited to reduce the numbers of parameters in the neural network, adaptive integration steps of the ﬂow, and consequently FLOPs at both training and inference time without decreasing performance in comparison to unconstrained ﬂows. By expressing the structure inversion as a compilation pass in a probabilistic programming language, we are able to apply it in a novel way to models as complex as convolutional neural networks. Furthermore, we extend the training objective for CNFs in the context of inference amortization to the symmetric Kullback-Leibler divergence, and demonstrate its theoretical and practical advantages.

2019-12-31

International Conference on Artificial Intelligence and Statistics (publié)

Synbols: Probing Learning Algorithms with Synthetic Datasets

Alexandre Lacoste

Pau Rodríguez

Frédéric Branchaud-Charron

Parmida Atighehchian

Massimo Caccia

Matt Craddock

Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithm… (voir plus)s. Enabling the design of datasets to test specific properties and failure modes of learning algorithms is thus a problem of high interest, as it has a direct impact on innovation in the field. In this sense, we introduce Synbols -- Synthetic Symbols -- a tool for rapidly generating new datasets with a rich composition of latent features rendered in low resolution images. Synbols leverages the large amount of symbols available in the Unicode standard and the wide range of artistic font provided by the open font community. Our tool's high-level interface provides a language for rapidly generating new distributions on the latent features, including various types of textures and occlusions. To showcase the versatility of Synbols, we use it to dissect the limitations and flaws in standard learning algorithms in various learning setups including supervised learning, active learning, out of distribution generalization, unsupervised representation learning, and object counting.

2019-12-31

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

Systematicity in a Recurrent Neural Network by Factorizing Syntax and Semantics

Jacob Russin

Jason Jo

R. O’Reilly

Yoshua Bengio

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

Tensor Networks for Probabilistic Sequence Modeling

Jacob Miller

Guillaume Rabusseau

John Terilla

Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied withi… (voir plus)n machine learning. In this work we utilize a uniform matrix product state (u-MPS) model for probabilistic modeling of sequence data. We first show that u-MPS enable sequence-level parallelism, with length-n sequences able to be evaluated in depth O(log n). We then introduce a novel generative algorithm giving trained u-MPS the ability to efficiently sample from a wide variety of conditional distributions, each one defined by a regular expression. Special cases of this algorithm correspond to autoregressive and fill-in-the-blank sampling, but more complex regular expressions permit the generation of richly structured data in a manner that has no direct analogue in neural generative models. Experiments on sequence modeling with synthetic and real text data show u-MPS outperforming a variety of baselines and effectively generalizing their predictions in the presence of limited data.

2019-12-31

arXiv (prépublication)

Tensorized Random Projections

Beheshteh T. Rakhshan

Guillaume Rabusseau

2019-12-31

AISTATS (publié)