research
∙
10/21/2022
Implicit Offline Reinforcement Learning via Supervised Learning
Offline Reinforcement Learning (RL) via Supervised Learning is a simple ...
share
research
∙
05/24/2022
Workflow Discovery from Dialogues in the Low Data Regime
Text-based dialogues are now widely used to solve real-world problems. I...
research
∙
11/03/2021
Learned Image Compression for Machine Perception
Recent work has shown that learned image compression strategies can outp...
research
∙
03/01/2021
Accounting for Variance in Machine Learning Benchmarks
Strong empirical evidence that one machine-learning algorithm A outperfo...
research
∙
10/21/2020
DuoRAT: Towards Simpler Text-to-SQL Models
Recent research has shown that neural text-to-SQL models can effectively...
research
∙
04/19/2020
Role-Wise Data Augmentation for Knowledge Distillation
Knowledge Distillation (KD) is a common method for transferring the “kno...
research
∙
10/29/2019
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments
Millions of blind and visually-impaired (BVI) people navigate urban envi...
research
∙
10/02/2019
Learning Neural Causal Models from Unknown Interventions
Meta-learning over a set of distributions can be interpreted as learning...
research
∙
07/31/2019
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation
Batch normalization has been widely used to improve optimization in deep...
research
∙
05/29/2019
Revision in Continuous Space: Fine-Grained Control of Text Style Transfer
Typical methods for unsupervised text style transfer often rely on two k...
research
∙
03/21/2019
Towards Standardization of Data Licenses: The Montreal Data License
This paper provides a taxonomy for the licensing of data in the fields o...
research
∙
01/13/2019
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmen...
research
∙
12/18/2018
Towards Deep Conversational Recommendations
There has been growing interest in using neural networks and deep learni...
research
∙
11/25/2018
A Survey of Mobile Computing for the Visually Impaired
The number of visually impaired or blind (VIB) people in the world is es...
research
∙
09/11/2018
Sparse Attentive Backtracking: Temporal CreditAssignment Through Reminding
Learning long-term dependencies in extended temporal sequences requires ...
research
∙
06/21/2018
Fashion-Gen: The Generative Fashion Dataset and Challenge
We introduce a new dataset of 293,008 high definition (1360 x 1360 pixel...
research
∙
06/12/2018
Focused Hierarchical RNNs for Conditional Sequence Processing
Recurrent Neural Networks (RNNs) with attention mechanisms have obtained...
research
∙
11/13/2017
ACtuAL: Actor-Critic Under Adversarial Learning
Generative Adversarial Networks (GANs) are a powerful framework for deep...
research
∙
11/07/2017
Sparse Attentive Backtracking: Long-Range Credit Assignment in Recurrent Networks
A major drawback of backpropagation through time (BPTT) is the difficult...
research
∙
08/22/2017
Twin Networks: Using the Future as a Regularizer
Being able to model long-term dependencies in sequential data, such as t...
research
∙
07/24/2017
Liver lesion segmentation informed by joint liver segmentation
We propose a model for the joint segmentation of the liver and liver les...
research
∙
01/31/2017
On orthogonality and learning recurrent networks with long term dependencies
It is well known that it is challenging to train deep neural networks an...
research
∙
08/14/2016
The Importance of Skip Connections in Biomedical Image Segmentation
In this paper, we study the influence of both long and short skip connec...
research
∙
06/03/2016
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations
We propose zoneout, a novel method for regularizing RNNs. At each timest...
research
∙
12/27/2015
Improving Facial Analysis and Performance Driven Animation through Disentangling Identity and Expression
We present techniques for improving performance driven facial animation,...
research
∙
11/19/2015
Delving Deeper into Convolutional Networks for Learning Video Representations
We propose an approach to learn spatio-temporal features in videos from ...
research
∙
05/13/2015