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Learning to Summarize Long Texts with Memory Compression and Transfer
We introduce Mem2Mem, a memory-to-memory mechanism for hierarchical recu...
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Reinforcement Learning with Random Delays
Action and observation delays commonly occur in many Reinforcement Learn...
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Measuring Systematic Generalization in Neural Proof Generation with Transformers
We are interested in understanding how well Transformer language models ...
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Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters Less Data
Multi-Task Learning (MTL) has emerged as a promising approach for transf...
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Action-Based Representation Learning for Autonomous Driving
Human drivers produce a vast amount of data which could, in principle, b...
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Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Adversarial imitation learning alternates between learning a discriminat...
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AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation
Entropy is ubiquitous in machine learning, but it is in general intracta...
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On the impressive performance of randomly weighted encoders in summarization tasks
In this work, we investigate the performance of untrained randomly initi...
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Real-Time Reinforcement Learning
Markov Decision Processes (MDPs), the mathematical framework underlying ...
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Neural Multisensory Scene Inference
For embodied agents to infer representations of the underlying 3D physic...
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On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
We present a method to produce abstractive summaries of long documents t...
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Interactive Language Learning by Question Answering
Humans observe and interact with the world to acquire knowledge. However...
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Interactive Machine Comprehension with Information Seeking Agents
Existing machine reading comprehension (MRC) models do not scale effecti...
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Promoting Coordination through Policy Regularization in Multi-Agent Reinforcement Learning
A central challenge in multi-agent reinforcement learning is the inducti...
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Towards Understanding Generalization in Gradient-Based Meta-Learning
In this work we study generalization of neural networks in gradient-base...
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Supervise Thyself: Examining Self-Supervised Representations in Interactive Environments
Self-supervised methods, wherein an agent learns representations solely ...
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Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study
Neural generative models have been become increasingly popular when buil...
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Adversarial Mixup Resynthesizers
In this paper, we explore new approaches to combining information encode...
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A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
We propose to meta-learn causal structures based on how fast a learner a...
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Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks
This paper addresses a challenging problem - how to reduce energy consum...
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Recurrent Transition Networks for Character Locomotion
Manually authoring transition animations for a complete locomotion syste...
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Unsupervised Depth Estimation, 3D Face Rotation and Replacement
We present an unsupervised approach for learning to estimate three dimen...
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Improving Landmark Localization with Semi-Supervised Learning
We present two techniques to improve landmark localization from partiall...
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A step towards procedural terrain generation with GANs
Procedural terrain generation for video games has been traditionally bee...
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Adversarial Generation of Natural Language
Generative Adversarial Networks (GANs) have gathered a lot of attention ...
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Unimodal probability distributions for deep ordinal classification
Probability distributions produced by the cross-entropy loss for ordinal...
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ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
Then detection and identification of extreme weather events in large-sca...
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A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering
While deep convolutional neural networks frequently approach or exceed h...
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Convolutional Residual Memory Networks
Very deep convolutional neural networks (CNNs) yield state of the art re...
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Movie Description
Audio Description (AD) provides linguistic descriptions of movies and al...
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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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Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation
Deep neural networks with alternating convolutional, max-pooling and dec...
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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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Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research
In this work, we introduce a dataset of video annotated with high qualit...
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Describing Videos by Exploiting Temporal Structure
Recent progress in using recurrent neural networks (RNNs) for image desc...
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