
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 ...
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Neural Networks with Cheap Differential Operators
Gradients of neural networks can be computed efficiently for any archite...
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Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks
Natural gradient descent has proven effective at mitigating the effects ...
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Functional Variational Bayesian Neural Networks
Variational Bayesian neural networks (BNNs) perform variational inferenc...
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Neural Data Server: A LargeScale Search Engine for Transfer Learning Data
Transfer learning has proven to be a successful technique to train deep ...
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Benchmarking ModelBased Reinforcement Learning
Modelbased reinforcement learning (MBRL) is widely seen as having the p...
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SelfTuning Networks: Bilevel Optimization of Hyperparameters using Structured BestResponse Functions
Hyperparameter optimization can be formulated as a bilevel optimization ...
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MANGA: Method Agnostic Neuralpolicy Generalization and Adaptation
In this paper we target the problem of transferring policies across mult...
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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(yx)...
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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...
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Invertible Residual Networks
Reversible deep networks provide useful theoretical guarantees and have ...
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Reproducibility in Machine Learning for Health
Machine learning algorithms designed to characterize, monitor, and inter...
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Efficient Graph Generation with Graph Recurrent Attention Networks
We propose a new family of efficient and expressive deep generative mode...
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Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
We introduce a new molecular dataset, named Alchemy, for developing mach...
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Concurrent Meta Reinforcement Learning
Stateoftheart meta reinforcement learning algorithms typically assume...
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GatedSCNN: Gated Shape CNNs for Semantic Segmentation
Current stateoftheart methods for image segmentation form a dense ima...
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Learning to Predict 3D Objects with an Interpolationbased Differentiable Renderer
Many machine learning models operate on images, but ignore the fact that...
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A DerivativeFree 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...
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DeepSpline: DataDriven Reconstruction of Parametric Curves and Surfaces
Reconstruction of geometry based on different input modes, such as image...
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MetaSim: Learning to Generate Synthetic Datasets
Training models to highend performance requires availability of large l...
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Neural Ordinary Differential Equations
We introduce a new family of deep neural network models. Instead of spec...
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Deep MultiSensor Lane Detection
Reliable and accurate lane detection has been a longstanding problem in...
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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...
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Sorting out Lipschitz function approximation
Training neural networks subject to a Lipschitz constraint is useful for...
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Music Artist Classification with Convolutional Recurrent Neural Networks
Previous attempts at music artist classification use framelevel audio f...
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DMMNet: Differentiable MaskMatching Network for Video Object Segmentation
In this paper, we propose the differentiable maskmatching network (DMM...
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Dense RepPoints: Representing Visual Objects with Dense Point Sets
We present an object representation, called Dense RepPoints, for flexibl...
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Finegrained Video Classification and Captioning
We describe a DNN for finegrained action classification and video capti...
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Scalable Recommender Systems through Recursive Evidence Chains
Recommender systems can be formulated as a matrix completion problem, pr...
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A CoordinateFree Construction of Scalable Natural Gradient
Most neural networks are trained using firstorder optimization methods,...
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Reversible Recurrent Neural Networks
Recurrent neural networks (RNNs) provide stateoftheart performance in...
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Lookahead Optimizer: k steps forward, 1 step back
The vast majority of successful deep neural networks are trained using v...
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Fairness Through Causal Awareness: Learning LatentVariable Models for Biased Data
How do we learn from biased data? Historical datasets often reflect hist...
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Eigenvalue Corrected Noisy Natural Gradient
Variational Bayesian neural networks combine the flexibility of deep lea...
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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...
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Variations on the ChebyshevLagrange Activation Function
We seek to improve the data efficiency of neural networks and present no...
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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...
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Nonconvex Regularized Robust Regression with Oracle Properties in Polynomial Time
This paper investigates tradeoffs among optimization errors, statistical...
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Adversarial Geometry and Lighting using a Differentiable Renderer
Many machine learning classifiers are vulnerable to adversarial attacks,...
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Three Mechanisms of Weight Decay Regularization
Weight decay is one of the standard tricks in the neural network toolbox...
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Residual Flows for Invertible Generative Modeling
Flowbased generative models parameterize probability distributions thro...
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Exploring Modelbased Planning with Policy Networks
Modelbased reinforcement learning (MBRL) with modelpredictive control ...
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Blackbox constructions for exchangeable sequences of random multisets
We develop constructions for exchangeable sequences of point processes t...
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Picking Winning Tickets Before Training by Preserving Gradient Flow
Overparameterization has been shown to benefit both the optimization and...
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A FacetoFace Neural Conversation Model
Neural networks have recently become good at engaging in dialog. However...
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The Cells Out of Sample (COOS) dataset and benchmarks for measuring outofsample generalization of image classifiers
Understanding if classifiers generalize to outofsample datasets is a c...
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Latent ODEs for IrregularlySampled Time Series
Time series with nonuniform intervals occur in many applications, and a...
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Video Face Clustering with Unknown Number of Clusters
Understanding videos such as TV series and movies requires analyzing who...
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Recurrent Unet: Deep learning to predict daily summertime ozone in the United States
We use a hybrid deep learning model to predict JuneJulyAugust (JJA) da...
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Improving neural networks by preventing coadaptation of feature detectors
When a large feedforward neural network is trained on a small training s...
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UNIVERSITY OF TORONTO
The University of Toronto is a public research university in Toronto, Ontario, Canada, located on the grounds that surround Queen's Park.