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Oct 16, 2019
Human-Computer Interaction
Computation and Language
Machine Learning

Designing Style Matching Conversational Agents

Advances in machine intelligence have enabled conversational interfaces that have the potential to radically change the way humans interact with mach…
Oct 16, 2019
Machine Learning
Optimization and Control

On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach

Many tasks in modern machine learning can be formulated as finding equilibria in \emph{sequential} games. In particular, two-player zero-sum sequenti…
Oct 16, 2019
Computer Vision

LOST: A flexible framework for semi-automatic image annotation

State-of-the-art computer vision approaches rely on huge amounts of annotated data. The collection of such data is a time consuming process since it …
Oct 16, 2019
Machine Learning

Excess risk bounds in robust empirical risk minimization

This paper investigates robust versions of the general empirical risk minimization algorithm, one of the core techniques underlying modern statistica…
Oct 16, 2019
Machine Learning

MAVEN: Multi-Agent Variational Exploration

Centralised training with decentralised execution is an important setting for cooperative deep multi-agent reinforcement learning due to communicatio…
Oct 16, 2019
Computation and Language
Neural and Evolutionary Computation

Imperial College London Submission to VATEX Video Captioning Task

This paper describes the Imperial College London team's submission to the 2019' VATEX video captioning challenge, where we first explore two sequence…
Oct 16, 2019
Machine Learning

Conditional Importance Sampling for Off-Policy Learning

The principal contribution of this paper is a conceptual framework for off-policy reinforcement learning, based on conditional expectations of import…
Oct 16, 2019
Machine Learning

Adaptive Trade-Offs in Off-Policy Learning

A great variety of off-policy learning algorithms exist in the literature, and new breakthroughs in this area continue to be made, improving theoreti…
Oct 16, 2019
Machine Learning
Disordered Systems and Neural Networks

Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sig…

We study layered neural networks of rectified linear units (ReLU) in a modelling framework for stochastic training processes. The comparison with sig…
Oct 16, 2019
By plewis
Computation and Language
Artificial Intelligence
Machine Learning

MLQA: Evaluating Cross-lingual Extractive Question Answering

Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. Such annotated datase…
Oct 16, 2019
Machine Learning
Artificial Intelligence

The Universal Marginaliser (UM) is based on a feed-forward neural network, used to perform fast, single-pass approximate inference on probabilistic programs at any scale. In this section we introduce the notation and discuss the UM building and trai…

Universal Marginaliser for Deep Amortised Inference for Probabilistic…

Probabilistic programming languages (PPLs) are powerful modelling tools which allow to formalise our knowledge about the world and reason about its i…
Oct 16, 2019
Image and Video Processing
Computer Vision

A Survey on Recent Advancements for AI Enabled Radiomics in Neuro-O…

Artificial intelligence (AI) enabled radiomics has evolved immensely especially in the field of oncology. Radiomics provide assistancein diagnosis of…
Oct 16, 2019
Computation and Language

Fine-grained evaluation of Quality Estimation for Machine translation…

We present an alternative method of evaluating Quality Estimation systems, which is based on a linguistically-motivated Test Suite. We create a test-…
Oct 16, 2019
By B.Zhang
Machine Learning
Computation and Language

Root Mean Square Layer Normalization

Layer normalization (LayerNorm) has been successfully applied to various deep neural networks to help stabilize training and boost model convergence …
Oct 16, 2019
Computation and Language

Fine-grained evaluation of German-English Machine Translation based o…

We present an analysis of 16 state-of-the-art MT systems on German-English based on a linguistically-motivated test suite. The test suite has been de…
Oct 16, 2019
By barati
Machine Learning
Robotics

Creativity in Robot Manipulation with Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) has emerged as a powerful control technique in robotic science. In contrast to control theory, DRL is more robust i…
Oct 16, 2019
Computation and Language

Linguistic evaluation of German-English Machine Translation using a T…

We present the results of the application of a grammatical test suite for German$\rightarrow$English MT on the systems submitted at WMT19, with a det…
Oct 16, 2019
Computation and Language

Bridging the Knowledge Gap: Enhancing Question Answering with World a…

In this paper we present OSCAR (Ontology-based Semantic Composition Augmented Regularization), a method for injecting task-agnostic knowledge from an…
Oct 16, 2019
Machine Learning
Computer Vision

On the Global Optima of Kernelized Adversarial Representation Learning

Adversarial representation learning is a promising paradigm for obtaining data representations that are invariant to certain sensitive attributes whi…
Oct 16, 2019
Networking and Internet Architecture
Machine Learning

Deep Reinforcement Learning meets Graph Neural Networks: An optical …

Recent advances in Deep Reinforcement Learning (DRL) have shown a significant improvement in decision-making problems. The networking community has s…
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