UNIVERSITY OF TORONTO

∙ Toronto , Canada ∙ 0 followers

The University of Toronto is a public research university in Toronto, Ontario, Canada, located on the grounds that surround Queen's Park.

Featured Researchers

Ruslan Salakhutdinov
110 publications
Raquel Urtasun
59 publications
Sanja Fidler
56 publications
Hugo Larochelle
51 publications
Bin Yang
48 publications
Mohammad Norouzi
39 publications
Bo Chen
36 publications
David Duvenaud
35 publications
Richard Zemel
32 publications
Roger Grosse
31 publications
Daniel M. Roy
30 publications

Learning Global and Local Consistent Representations for Unsupervised Image Retrieval via Deep Graph Diffusion Networks

Diffusion has shown great success in improving accuracy of unsupervised ...
01/05/2020 ∙ by Zhiyong Dou, et al. ∙ 53 ∙ share

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Neural Networks with Cheap Differential Operators

Gradients of neural networks can be computed efficiently for any archite...
12/08/2019 ∙ by Ricky T. Q. Chen, et al. ∙ 36 ∙ share

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Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks

Natural gradient descent has proven effective at mitigating the effects ...
05/27/2019 ∙ by Guodong Zhang, et al. ∙ 35 ∙ share

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Functional Variational Bayesian Neural Networks

Variational Bayesian neural networks (BNNs) perform variational inferenc...
03/14/2019 ∙ by Shengyang Sun, et al. ∙ 34 ∙ share

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Neural Data Server: A Large-Scale Search Engine for Transfer Learning Data

Transfer learning has proven to be a successful technique to train deep ...
01/09/2020 ∙ by Xi Yan, et al. ∙ 34 ∙ share

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Benchmarking Model-Based Reinforcement Learning

Model-based reinforcement learning (MBRL) is widely seen as having the p...
07/03/2019 ∙ by Tingwu Wang, et al. ∙ 28 ∙ share

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Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions

Hyperparameter optimization can be formulated as a bilevel optimization ...
03/07/2019 ∙ by Matthew MacKay, et al. ∙ 26 ∙ share

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MANGA: Method Agnostic Neural-policy Generalization and Adaptation

In this paper we target the problem of transferring policies across mult...
11/19/2019 ∙ by Homanga Bharadhwaj, et al. ∙ 23 ∙ share

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Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One

We propose to reinterpret a standard discriminative classifier of p(y|x)...
12/06/2019 ∙ by Will Grathwohl, et al. ∙ 19 ∙ share

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High Mutual Information in Representation Learning with Symmetric Variational Inference

We introduce the Mutual Information Machine (MIM), a novel formulation o...
10/04/2019 ∙ by Micha Livne, et al. ∙ 18 ∙ share

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Invertible Residual Networks

Reversible deep networks provide useful theoretical guarantees and have ...
11/02/2018 ∙ by Jens Behrmann, et al. ∙ 16 ∙ share

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Reproducibility in Machine Learning for Health

Machine learning algorithms designed to characterize, monitor, and inter...
07/02/2019 ∙ by Matthew B. A. McDermott, et al. ∙ 16 ∙ share

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Efficient Graph Generation with Graph Recurrent Attention Networks

We propose a new family of efficient and expressive deep generative mode...
10/02/2019 ∙ by Renjie Liao, et al. ∙ 16 ∙ share

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Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models

We introduce a new molecular dataset, named Alchemy, for developing mach...
06/22/2019 ∙ by Guangyong Chen, et al. ∙ 15 ∙ share

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Concurrent Meta Reinforcement Learning

State-of-the-art meta reinforcement learning algorithms typically assume...
03/07/2019 ∙ by Emilio Parisotto, et al. ∙ 14 ∙ share

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Gated-SCNN: Gated Shape CNNs for Semantic Segmentation

Current state-of-the-art methods for image segmentation form a dense ima...
07/12/2019 ∙ by Towaki Takikawa, et al. ∙ 14 ∙ share

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Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer

Many machine learning models operate on images, but ignore the fact that...
08/03/2019 ∙ by Wenzheng Chen, et al. ∙ 14 ∙ share

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A Derivative-Free Method for Solving Elliptic Partial Differential Equations with Deep Neural Networks

We introduce a deep neural network based method for solving a class of e...
01/17/2020 ∙ by Jihun Han, et al. ∙ 14 ∙ share

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DeepSpline: Data-Driven Reconstruction of Parametric Curves and Surfaces

Reconstruction of geometry based on different input modes, such as image...
01/12/2019 ∙ by Jun Gao, et al. ∙ 12 ∙ share

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Meta-Sim: Learning to Generate Synthetic Datasets

Training models to high-end performance requires availability of large l...
04/25/2019 ∙ by Amlan Kar, et al. ∙ 12 ∙ share

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Neural Ordinary Differential Equations

We introduce a new family of deep neural network models. Instead of spec...
06/19/2018 ∙ by Tian Qi Chen, et al. ∙ 11 ∙ share

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Deep Multi-Sensor Lane Detection

Reliable and accurate lane detection has been a long-standing problem in...
05/04/2019 ∙ by Min Bai, et al. ∙ 10 ∙ share

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SurfConv: Bridging 3D and 2D Convolution for RGBD Images

We tackle the problem of using 3D information in convolutional neural ne...
12/04/2018 ∙ by Hang Chu, et al. ∙ 9 ∙ share

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Sorting out Lipschitz function approximation

Training neural networks subject to a Lipschitz constraint is useful for...
11/13/2018 ∙ by Cem Anil, et al. ∙ 8 ∙ share

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Music Artist Classification with Convolutional Recurrent Neural Networks

Previous attempts at music artist classification use frame-level audio f...
01/14/2019 ∙ by Zain Nasrullah, et al. ∙ 8 ∙ share

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DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation

In this paper, we propose the differentiable mask-matching network (DMM-...
09/27/2019 ∙ by Xiaohui Zeng, et al. ∙ 8 ∙ share

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Dense RepPoints: Representing Visual Objects with Dense Point Sets

We present an object representation, called Dense RepPoints, for flexibl...
12/24/2019 ∙ by Ze Yang, et al. ∙ 8 ∙ share

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Fine-grained Video Classification and Captioning

We describe a DNN for fine-grained action classification and video capti...
04/24/2018 ∙ by Farzaneh Mahdisoltani, et al. ∙ 6 ∙ share

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Scalable Recommender Systems through Recursive Evidence Chains

Recommender systems can be formulated as a matrix completion problem, pr...
07/05/2018 ∙ by Elias Tragas, et al. ∙ 6 ∙ share

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A Coordinate-Free Construction of Scalable Natural Gradient

Most neural networks are trained using first-order optimization methods,...
08/30/2018 ∙ by Kevin Luk, et al. ∙ 6 ∙ share

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Reversible Recurrent Neural Networks

Recurrent neural networks (RNNs) provide state-of-the-art performance in...
10/25/2018 ∙ by Matthew MacKay, et al. ∙ 6 ∙ share

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Lookahead Optimizer: k steps forward, 1 step back

The vast majority of successful deep neural networks are trained using v...
07/19/2019 ∙ by Michael R. Zhang, et al. ∙ 5 ∙ share

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Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Data

How do we learn from biased data? Historical datasets often reflect hist...
09/07/2018 ∙ by David Madras, et al. ∙ 4 ∙ share

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Eigenvalue Corrected Noisy Natural Gradient

Variational Bayesian neural networks combine the flexibility of deep lea...
11/30/2018 ∙ by Juhan Bae, et al. ∙ 4 ∙ share

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Asymptotic Risk of Least Squares Minimum Norm Estimator under the Spike Covariance Model

One of the recent approaches to explain good performance of neural netwo...
12/31/2019 ∙ by Yasaman Mahdaviyeh, et al. ∙ 4 ∙ share

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Variations on the Chebyshev-Lagrange Activation Function

We seek to improve the data efficiency of neural networks and present no...
06/24/2019 ∙ by Yuchen Li, et al. ∙ 3 ∙ share

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Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model

Increasing the batch size is a popular way to speed up neural network tr...
07/09/2019 ∙ by Guodong Zhang, et al. ∙ 3 ∙ share

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Nonconvex Regularized Robust Regression with Oracle Properties in Polynomial Time

This paper investigates tradeoffs among optimization errors, statistical...
07/09/2019 ∙ by Xiaoou Pan, et al. ∙ 3 ∙ share

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Adversarial Geometry and Lighting using a Differentiable Renderer

Many machine learning classifiers are vulnerable to adversarial attacks,...
08/08/2018 ∙ by Hsueh-Ti Derek Liu, et al. ∙ 2 ∙ share

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Three Mechanisms of Weight Decay Regularization

Weight decay is one of the standard tricks in the neural network toolbox...
10/29/2018 ∙ by Guodong Zhang, et al. ∙ 2 ∙ share

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Residual Flows for Invertible Generative Modeling

Flow-based generative models parameterize probability distributions thro...
06/06/2019 ∙ by Ricky T. Q. Chen, et al. ∙ 2 ∙ share

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Exploring Model-based Planning with Policy Networks

Model-based reinforcement learning (MBRL) with model-predictive control ...
06/20/2019 ∙ by Tingwu Wang, et al. ∙ 2 ∙ share

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Black-box constructions for exchangeable sequences of random multisets

We develop constructions for exchangeable sequences of point processes t...
08/17/2019 ∙ by Creighton Heaukulani, et al. ∙ 2 ∙ share

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Picking Winning Tickets Before Training by Preserving Gradient Flow

Overparameterization has been shown to benefit both the optimization and...
02/18/2020 ∙ by Chaoqi Wang, et al. ∙ 2 ∙ share

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A Face-to-Face Neural Conversation Model

Neural networks have recently become good at engaging in dialog. However...
12/04/2018 ∙ by Hang Chu, et al. ∙ 1 ∙ share

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The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers

Understanding if classifiers generalize to out-of-sample datasets is a c...
06/17/2019 ∙ by Alex X. Lu, et al. ∙ 1 ∙ share

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Latent ODEs for Irregularly-Sampled Time Series

Time series with non-uniform intervals occur in many applications, and a...
07/08/2019 ∙ by Yulia Rubanova, et al. ∙ 1 ∙ share

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Video Face Clustering with Unknown Number of Clusters

Understanding videos such as TV series and movies requires analyzing who...
08/09/2019 ∙ by Makarand Tapaswi, et al. ∙ 1 ∙ share

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Recurrent U-net: Deep learning to predict daily summertime ozone in the United States

We use a hybrid deep learning model to predict June-July-August (JJA) da...
08/16/2019 ∙ by Tai-Long He, et al. ∙ 1 ∙ share

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Improving neural networks by preventing co-adaptation of feature detectors

When a large feedforward neural network is trained on a small training s...
07/03/2012 ∙ by Geoffrey E. Hinton, et al. ∙ 0 ∙ share

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