Publications

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
arxiv.org
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)
doi.org
proceedings.mlr.press
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
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)
doi.org
arxiv.org
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)
proceedings.mlr.press
proceedings.mlr.press
Synbols: Probing Learning Algorithms with Synthetic Datasets
Alexandre Lacoste
Pau Rodríguez
Frédéric Branchaud-Charron
Parmida Atighehchian
Massimo Caccia
Issam Laradji
Alexandre Drouin
Matt Craddock
Laurent Charlin
David Vázquez
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)
doi.org
arxiv.org
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)
doi.org
proceedings.mlr.press
Tensorized Random Projections
Beheshteh T. Rakhshan
Guillaume Rabusseau
2019-12-31
AISTATS (publié)
proceedings.mlr.press
proceedings.mlr.press
On the Effectiveness of Two-Step Learning for Latent-Variable Models
Cem Subakan
Maxime Gasse
Laurent Charlin
Latent-variable generative models offer a principled solution for modeling and sampling from complex probability distributions. Implementing… (voir plus) a joint training objective with a complex prior, however, can be a tedious task, as one is typically required to derive and code a specific cost function for each new type of prior distribution. In this work, we propose a general framework for learning latent variable generative models in a two-step fashion. In the first step of the framework, we train an autoencoder, and in the second step we fit a prior model on the resulting latent distribution. This two-step approach offers a convenient alternative to joint training, as it allows for a straightforward combination of existing models without the hustle of deriving new cost functions, and the need for coding the joint training objectives. Through a set of experiments, we demonstrate that two-step learning results in performances similar to joint training, and in some cases even results in more accurate modeling.
2019-12-31
2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP) (publié)
doi.org
On the interplay between noise and curvature and its effect on optimization and generalization
Valentin Thomas
Fabian Pedregosa
Bart Van Merriënboer
Pierre-Antoine Mangazol
Yoshua Bengio
Nicolas Le Roux
The speed at which one can minimize an expected loss using stochastic methods depends on two properties: the curvature of the loss and the v… (voir plus)ariance of the gradients. While most previous works focus on one or the other of these properties, we explore how their interaction affects optimization speed. Further, as the ultimate goal is good generalization performance, we clarify how both curvature and noise are relevant to properly estimate the generalization gap. Realizing that the limitations of some existing works stems from a confusion between these matrices, we also clarify the distinction between the Fisher matrix, the Hessian, and the covariance matrix of the gradients.
2019-12-31
AISTATS (publié)
doi.org
proceedings.mlr.press
On the Morality of Artificial Intelligence
Alexandra Luccioni
Yoshua Bengio
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)
doi.org
arxiv.org