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01/27/2023
Leveraging the Third Dimension in Contrastive Learning
Self-Supervised Learning (SSL) methods operate on unlabeled data to lear...
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12/28/2022
Representation Learning in Deep RL via Discrete Information Bottleneck
Several self-supervised representation learning methods have been propos...
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11/14/2022
Towards Data-Driven Offline Simulations for Online Reinforcement Learning
Modern decision-making systems, from robots to web recommendation engine...
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11/02/2022
Neural Active Learning on Heteroskedastic Distributions
Models that can actively seek out the best quality training data hold th...
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11/01/2022
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Goal-conditioned reinforcement learning (RL) is a promising direction fo...
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10/31/2022
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information
Learning to control an agent from data collected offline in a rich pixel...
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10/18/2022
CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback
We propose a novel regularizer for supervised learning called Conditioni...
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07/17/2022
Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models
A person walking along a city street who tries to model all aspects of t...
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05/30/2022
Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning
Recurrent neural networks have a strong inductive bias towards learning ...
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02/02/2022
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization
Vector Quantization (VQ) is a method for discretizing latent representat...
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07/06/2021
Discrete-Valued Neural Communication
Deep learning has advanced from fully connected architectures to structu...
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06/12/2021
Predicting the Ordering of Characters in Japanese Historical Documents
Japan is a unique country with a distinct cultural heritage, which is re...
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03/01/2021
Coordination Among Neural Modules Through a Shared Global Workspace
Deep learning has seen a movement away from representing examples with a...
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02/27/2021
Transformers with Competitive Ensembles of Independent Mechanisms
An important development in deep learning from the earliest MLPs has bee...
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02/27/2021
A Brief Introduction to Generative Models
We introduce and motivate generative modeling as a central task for mach...
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06/30/2020
Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules
Robust perception relies on both bottom-up and top-down signals. Bottom-...
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06/29/2020
Object Files and Schemata: Factorizing Declarative and Procedural Knowledge in Dynamical Systems
Modeling a structured, dynamic environment like a video game requires ke...
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05/12/2020
Jigsaw-VAE: Towards Balancing Features in Variational Autoencoders
The latent variables learned by VAEs have seen considerable interest as ...
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02/20/2020
KaoKore: A Pre-modern Japanese Art Facial Expression Dataset
From classifying handwritten digits to generating strings of text, the d...
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12/25/2019
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks
Deep networks have achieved excellent results in perceptual tasks, yet t...
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10/21/2019
KuroNet: Pre-Modern Japanese Kuzushiji Character Recognition with Deep Learning
Kuzushiji, a cursive writing style, had been used in Japan for over a th...
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09/25/2019
GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning
We present GraphMix, a regularization technique for Graph Neural Network...
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09/24/2019
Recurrent Independent Mechanisms
Learning modular structures which reflect the dynamics of the environmen...
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06/16/2019
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy
Adversarial robustness has become a central goal in deep learning, both ...
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05/26/2019
State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations
Machine learning promises methods that generalize well from finite label...
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03/09/2019
Interpolation Consistency Training for Semi-Supervised Learning
We introduce Interpolation Consistency Training (ICT), a simple and comp...
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03/07/2019
Adversarial Mixup Resynthesizers
In this paper, we explore new approaches to combining information encode...
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12/03/2018
Deep Learning for Classical Japanese Literature
Much of machine learning research focuses on producing models which perf...
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06/13/2018
Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer
Deep networks often perform well on the data manifold on which they are ...
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04/07/2018
Fortified Networks: Improving the Robustness of Deep Networks by Modeling the Manifold of Hidden Representations
Deep networks have achieved impressive results across a variety of impor...
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12/12/2017
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models
Directed latent variable models that formulate the joint distribution as...
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11/13/2017
ACtuAL: Actor-Critic Under Adversarial Learning
Generative Adversarial Networks (GANs) are a powerful framework for deep...
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10/27/2016
Professor Forcing: A New Algorithm for Training Recurrent Networks
The Teacher Forcing algorithm trains recurrent networks by supplying obs...
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06/02/2016
Adversarially Learned Inference
We introduce the adversarially learned inference (ALI) model, which join...
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05/09/2016
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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02/09/2016
Discriminative Regularization for Generative Models
We explore the question of whether the representations learned by classi...
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11/20/2015
