Tensor Networks for Probabilistic Sequence Modeling
Jacob Miller
Guillaume Rabusseau
John Anthony Terilla
Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied withi… (see more)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 text in a manner that has no direct analogue in current generative models. Experiments on synthetic text data find u-MPS outperforming LSTM baselines in several sampling tasks, and demonstrate strong generalization in the presence of limited data.
2020-03-02
International Conference on Artificial Intelligence and Statistics (published)
dblp.uni-trier.de
Tensor Networks for Probabilistic Sequence Modeling
Jacob Miller
Guillaume Rabusseau
John Anthony Terilla
Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied withi… (see more)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 text in a manner that has no direct analogue in current generative models. Experiments on synthetic text data find u-MPS outperforming LSTM baselines in several sampling tasks, and demonstrate strong generalization in the presence of limited data.
2020-03-02
International Conference on Artificial Intelligence and Statistics (published)
proceedings.mlr.press
Seven pillars of precision digital health and medicine
Arash Shaban-Nejad
Martin Michalowski
Niels Peek
John S. Brownstein
David Buckeridge
2020-03-01
Artificial Intelligence in Medicine (published)
doi.org
On the Morality of Artificial Intelligence
Alexandra Luccioni
Yoshua Bengio
Examines ethical principles and guidelines that surround machine learning and artificial intelligence.
2020-03-01
IEEE Technology and Society Magazine (published)
doi.org
arxiv.org
On Catastrophic Interference in Atari 2600 Games
William Fedus
Dibya Ghosh
John D. Martin
Marc Gendron-Bellemare
Yoshua Bengio
Hugo Larochelle
Model-free deep reinforcement learning is sample inefficient. One hypothesis -- speculated, but not confirmed -- is that catastrophic interf… (see more)erence within an environment inhibits learning. We test this hypothesis through a large-scale empirical study in the Arcade Learning Environment (ALE) and, indeed, find supporting evidence. We show that interference causes performance to plateau; the network cannot train on segments beyond the plateau without degrading the policy used to reach there. By synthetically controlling for interference, we demonstrate performance boosts across architectures, learning algorithms and environments. A more refined analysis shows that learning one segment of a game often increases prediction errors elsewhere. Our study provides a clear empirical link between catastrophic interference and sample efficiency in reinforcement learning.
2020-02-28
ArXiv (preprint)
arxiv.org
arxiv.org
Machine learning analysis of exome trios to contrast the genomic architecture of autism and schizophrenia
Sameer Sardaar
Bill Qi
Alexandre Dionne-Laporte
Guy. A. Rouleau
Reihaneh Rabbany
Yannis Trakadis
2020-02-28
BMC Psychiatry (published)
doi.org
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
Nicolas Loizou
Sharan Vaswani
Issam Hadj Laradji
Simon Lacoste-Julien
We propose a stochastic variant of the classical Polyak step-size (Polyak, 1987) commonly used in the subgradient method. Although computing… (see more) the Polyak step-size requires knowledge of the optimal function values, this information is readily available for typical modern machine learning applications. Consequently, the proposed stochastic Polyak step-size (SPS) is an attractive choice for setting the learning rate for stochastic gradient descent (SGD). We provide theoretical convergence guarantees for SGD equipped with SPS in different settings, including strongly convex, convex and non-convex functions. Furthermore, our analysis results in novel convergence guarantees for SGD with a constant step-size. We show that SPS is particularly effective when training over-parameterized models capable of interpolating the training data. In this setting, we prove that SPS enables SGD to converge to the true solution at a fast rate without requiring the knowledge of any problem-dependent constants or additional computational overhead. We experimentally validate our theoretical results via extensive experiments on synthetic and real datasets. We demonstrate the strong performance of SGD with SPS compared to state-of-the-art optimization methods when training over-parameterized models.
2020-02-24
ArXiv (preprint)
arxiv.org
arxiv.org
Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning
Devansh Arpit
Huan Wang
Caiming Xiong
Richard Socher
Yoshua Bengio
2020-02-20
ArXiv (preprint)
arxiv.org
arxiv.org
The Geometry of Sign Gradient Descent
Lukas Balles
Fabian Pedregosa
Nicolas Le Roux
2020-02-19
ArXiv (preprint)
openreview.net
openreview.net
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Michel Deudon
Alfredo Kalaitzis
Israel Goytom
Md Rifat Arefin
Zhichao Lin
Kris Sankaran
Vincent Michalski
Samira Ebrahimi Kahou
Julien Cornebise
Yoshua Bengio
Generative deep learning has sparked a new wave of Super-Resolution (SR) algorithms that enhance single images with impressive aesthetic res… (see more)ults, albeit with imaginary details. Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views. This is important for satellite monitoring of human impact on the planet -- from deforestation, to human rights violations -- that depend on reliable imagery. To this end, we present HighRes-net, the first deep learning approach to MFSR that learns its sub-tasks in an end-to-end fashion: (i) co-registration, (ii) fusion, (iii) up-sampling, and (iv) registration-at-the-loss. Co-registration of low-resolution views is learned implicitly through a reference-frame channel, with no explicit registration mechanism. We learn a global fusion operator that is applied recursively on an arbitrary number of low-resolution pairs. We introduce a registered loss, by learning to align the SR output to a ground-truth through ShiftNet. We show that by learning deep representations of multiple views, we can super-resolve low-resolution signals and enhance Earth Observation data at scale. Our approach recently topped the European Space Agency's MFSR competition on real-world satellite imagery.
2020-02-15
ArXiv (preprint)
arxiv.org
arxiv.org
Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets
Gauthier Gidel
D. Balduzzi
Wojciech M. Czarnecki
M. Garnelo
Yoram Bachrach
Adversarial training, a special case of multi-objective optimization, is an increasingly useful tool in machine learning. For example, two-p… (see more)layer zero-sum games are important for generative modeling (GANs) and for mastering games like Go or Poker via self-play. A classic result in Game Theory states that one must mix strategies, as pure equilibria may not exist. Surprisingly, machine learning practitioners typically train a \emph{single} pair of agents -- instead of a pair of mixtures -- going against Nash's principle. Our main contribution is a notion of limited-capacity-equilibrium for which, as capacity grows, optimal agents -- not mixtures -- can learn increasingly expressive and realistic behaviors. We define \emph{latent games}, a new class of game where agents are mappings that transform latent distributions. Examples include generators in GANs, which transform Gaussian noise into distributions on images, and StarCraft II agents, which transform sampled build orders into policies. We show that minimax equilibria in latent games can be approximated by a \emph{single} pair of dense neural networks. Finally, we apply our latent game approach to solve differentiable Blotto, a game with an infinite strategy space.
2020-02-14
ArXiv (preprint)
arxiv.org
arxiv.org
Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets
Gauthier Gidel
D. Balduzzi
Wojciech M. Czarnecki
M. Garnelo
Yoram Bachrach
Adversarial training, a special case of multi-objective optimization, is an increasingly useful tool in machine learning. For example, two-p… (see more)layer zero-sum games are important for generative modeling (GANs) and for mastering games like Go or Poker via self-play. A classic result in Game Theory states that one must mix strategies, as pure equilibria may not exist. Surprisingly, machine learning practitioners typically train a \emph{single} pair of agents -- instead of a pair of mixtures -- going against Nash's principle. Our main contribution is a notion of limited-capacity-equilibrium for which, as capacity grows, optimal agents -- not mixtures -- can learn increasingly expressive and realistic behaviors. We define \emph{latent games}, a new class of game where agents are mappings that transform latent distributions. Examples include generators in GANs, which transform Gaussian noise into distributions on images, and StarCraft II agents, which transform sampled build orders into policies. We show that minimax equilibria in latent games can be approximated by a \emph{single} pair of dense neural networks. Finally, we apply our latent game approach to solve differentiable Blotto, a game with an infinite strategy space.
2020-02-14
ArXiv (preprint)
arxiv.org
arxiv.org