Sort by: Year Popularity Relevance

On Consequentialism and Fairness

,  - 2020

Recent work on fairness in machine learning has primarily emphasized how to define, quantify, and encourage "fair" outcomes. Less attention has been paid, however, to the ethical foundations which underlie such efforts. Among the ethical perspectives that should be taken into consideration is consequentialism, the position that, roughly speaking,...


An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection

, , , , ,  - 2020

There are several benefits from learning disentangled representations, including interpretability, compositionality and generalisation to new tasks. Disentanglement could be done by imposing an inductive bias based on prior knowledge. Different priors make different structural assumptions about the representations. For instance, priors with granu...


SparseIDS: Learning Packet Sampling with Reinforcement Learning

, , ,  - 2020

Recurrent Neural Networks (RNNs) have been shown to be valuable for constructing Intrusion Detection Systems (IDSs) for network data. They allow determining if a flow is malicious or not already before it is over, making it possible to take action immediately. However, considering the large number of packets that have to be inspected, the questio...


The Archives Unleashed Project: Technology, Process, and Community to Improve Scholarly Access to Web Archives

, , ,  - 2020

The Archives Unleashed project aims to improve scholarly access to web archives through a multi-pronged strategy involving tool creation, process modeling, and community building - all proceeding concurrently in mutually-reinforcing efforts. As we near the end of our initially-conceived three-year project, we report on our progress and share less...


Quantisation and Pruning for Neural Network Compression and Regularisation

, ,  - 2020

Deep neural networks are typically too computationally expensive to run in real-time on consumer-grade hardware and low-powered devices. In this paper, we investigate reducing the computational and memory requirements of neural networks through network pruning and quantisation. We examine their efficacy on large networks like AlexNet compared to ...


Graph-Bert: Only Attention is Needed for Learning Graph Representations

, , ,  - 2020

The dominant graph neural networks (GNNs) over-rely on the graph links, several serious performance problems with which have been witnessed already, e.g., suspended animation problem and over-smoothing problem. What's more, the inherently inter-connected nature precludes parallelization within the graph, which becomes critical for large-sized gra...


Schema2QA: Answering Complex Queries on the Structured Web with a Neural Model

, , ,  - 2020

Virtual assistants have proprietary third-party skill platforms; they train and own the voice interface to websites based on their submitted skill information. This paper proposes Schema2QA, an open-source toolkit that leverages the Schema.org markup found in many websites to automatically build skills. Schema2QA has several advantages: (1) Schem...


Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs

, , ,  - 2020

Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communication between two distant nodes, thereby neglecting graph topology as all nodes are connected. In contrast, local node encoding considers the relations between directl...


Multi-class Gaussian Process Classification with Noisy Inputs

, , ,  - 2020

It is a common practice in the supervised machine learning community to assume that the observed data are noise-free in the input attributes. Nevertheless, scenarios with input noise are common in real problems, as measurements are never perfectly accurate. If this input noise is not taken into account, a supervised machine learning method is exp...


A context based deep learning approach for unbalanced medical image segmentation

, , , , ,  - 2020

Automated medical image segmentation is an important step in many medical procedures. Recently, deep learning networks have been widely used for various medical image segmentation tasks, with U-Net and generative adversarial nets (GANs) being some of the commonly used ones. Foreground-background class imbalance is a common occurrence in medical i...