UNIVERSITY OF TORONTO

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

Memory-Based Graph Networks

Graph neural networks (GNNs) are a class of deep models that operate on ...
02/21/2020 ∙ by Amir Hosein Khasahmadi, et al. ∙ 65 ∙ share

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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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Few-Shot Single-View 3-D Object Reconstruction with Compositional Priors

The impressive performance of deep convolutional neural networks in sing...
04/14/2020 ∙ by Mateusz Michalkiewicz, et al. ∙ 20 ∙ 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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Towards the Role of Theory of Mind in Explanation

Theory of Mind is commonly defined as the ability to attribute mental st...
05/06/2020 ∙ by Maayan Shvo, et al. ∙ 15 ∙ share

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Using Generative Models for Pediatric wbMRI

Early detection of cancer is key to a good prognosis and requires freque...
06/01/2020 ∙ by Alex Chang, 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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BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning

Ensembles, where multiple neural networks are trained individually and t...
02/17/2020 ∙ by Yeming Wen, et al. ∙ 13 ∙ 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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Experience Replay with Likelihood-free Importance Weights

The use of past experiences to accelerate temporal difference (TD) learn...
06/23/2020 ∙ by Samarth Sinha, 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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W-Cell-Net: Multi-frame Interpolation of Cellular Microscopy Videos

Deep Neural Networks are increasingly used in video frame interpolation ...
05/14/2020 ∙ by Rohit Saha, 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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Aligning Superhuman AI and Human Behavior: Chess as a Model System

As artificial intelligence becomes increasingly intelligent—in some case...
06/02/2020 ∙ by Reid McIlroy-Young, 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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SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models

Standard variational lower bounds used to train latent variable models p...
04/01/2020 ∙ by Yucen Luo, et al. ∙ 8 ∙ share

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RigNet: Neural Rigging for Articulated Characters

We present RigNet, an end-to-end automated method for producing animatio...
05/01/2020 ∙ by Zhan Xu, et al. ∙ 7 ∙ 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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Speaker diarization with session-level speaker embedding refinement using graph neural networks

Deep speaker embedding models have been commonly used as a building bloc...
05/22/2020 ∙ by Jixuan Wang, 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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Video Monitoring Queries

Recent advances in video processing utilizing deep learning primitives a...
02/24/2020 ∙ by Nick Koudas, 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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