
Stochastic Hamiltonian Gradient Methods for Smooth Games
The success of adversarial formulations in machine learning has brought ...
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Sharp Analysis of Smoothed Bellman Error Embedding
The Smoothed Bellman Error Embedding algorithm <cit.>, known as SBEED, w...
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Adversarial Example Games
The existence of adversarial examples capable of fooling trained neural ...
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Revisiting Loss Modelling for Unstructured Pruning
By removing parameters from deep neural networks, unstructured pruning m...
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Do sequencetosequence VAEs learn global features of sentences?
A longstanding goal in NLP is to compute global sentence representations...
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Stable Policy Optimization via OffPolicy Divergence Regularization
Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization...
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An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation
Batch normalization has been widely used to improve optimization in deep...
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A Closer Look at the Optimization Landscapes of Generative Adversarial Networks
Generative adversarial networks have been very successful in generative ...
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Stochastic Neural Network with Kronecker Flow
Recent advances in variational inference enable the modelling of highly ...
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SVRG for Policy Evaluation with Fewer Gradient Evaluations
Stochastic variancereduced gradient (SVRG) is an optimization method or...
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Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition
The goal of MRI reconstruction is to restore a high fidelity image from ...
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fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measuremen...
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Fast Approximate Natural Gradient Descent in a Kroneckerfactored Eigenbasis
Optimization algorithms that leverage gradient covariance information, s...
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Randomized Value Functions via Multiplicative Normalizing Flows
Randomized value functions offer a promising approach towards the challe...
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A Variational Inequality Perspective on Generative Adversarial Nets
Stability has been a recurrent issue in training generative adversarial ...
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Improving Landmark Localization with SemiSupervised Learning
We present two techniques to improve landmark localization from partiall...
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Parametric Adversarial Divergences are Good Task Losses for Generative Modeling
Generative modeling of high dimensional data like images is a notoriousl...
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Learning to Compute Word Embeddings On the Fly
Words in natural language follow a Zipfian distribution whereby some wor...
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Convergent TreeBackup and Retrace with Function Approximation
Offpolicy learning is key to scaling up reinforcement learning as it al...
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Learning to Generate Samples from Noise through Infusion Training
In this work, we investigate a novel training procedure to learn a gener...
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A Cheap Linear Attention Mechanism with Fast Lookups and FixedSize Representations
The softmax contentbased attention mechanism has proven to be very bene...
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Exact gradient updates in time independent of output size for the spherical loss family
An important class of problems involves training deep neural networks wi...
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Hierarchical Memory Networks
Memory networks are neural networks with an explicit memory component th...
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Theano: A Python framework for fast computation of mathematical expressions
Theano is a Python library that allows to define, optimize, and evaluate...
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The Zloss: a shift and scale invariant classification loss belonging to the Spherical Family
Despite being the standard loss function to train multiclass neural net...
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Recombinator Networks: Learning CoarsetoFine Feature Aggregation
Deep neural networks with alternating convolutional, maxpooling and dec...
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An Exploration of Softmax Alternatives Belonging to the Spherical Loss Family
In a multiclass classification problem, it is standard to model the out...
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Artificial Neural Networks Applied to Taxi Destination Prediction
We describe our firstplace solution to the ECML/PKDD discovery challeng...
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Clustering is Efficient for Approximate Maximum Inner Product Search
Efficient Maximum Inner Product Search (MIPS) is an important task that ...
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Dropout as data augmentation
Dropout is typically interpreted as bagging a large number of models sha...
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GSNs : Generative Stochastic Networks
We introduce a novel training principle for probabilistic models that is...
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EmoNets: Multimodal deep learning approaches for emotion recognition in video
The task of the emotion recognition in the wild (EmotiW) Challenge is to...
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Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets
An important class of problems involves training deep neural networks wi...
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Generalized Denoising AutoEncoders as Generative Models
Recent work has shown how denoising and contractive autoencoders implici...
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Highdimensional sequence transduction
We investigate the problem of transforming an input sequence into a high...
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A Generative Process for Sampling Contractive AutoEncoders
The contractive autoencoder learns a representation of the input data t...
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Modeling Temporal Dependencies in HighDimensional Sequences: Application to Polyphonic Music Generation and Transcription
We investigate the problem of modeling symbolic sequences of polyphonic ...
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Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data rep...
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Learning invariant features through local space contraction
We present in this paper a novel approach for training deterministic aut...
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Adding noise to the input of a model trained with a regularized objective
Regularization is a well studied problem in the context of neural networ...
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Pascal Vincent
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Associate Professor Montreal Institute for Learning Algorithms (MILA) Department of Computer Science and Operational Research University of Montreal, Research Scientist at Facebook A.I. Research