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Neural Arithmetic Units

,  - 2020

Neural networks can approximate complex functions, but they struggle to perform exact arithmetic operations over real numbers. The lack of inductive bias for arithmetic operations leaves neural networks without the underlying logic necessary to extrapolate on tasks such as addition, subtraction, and multiplication. We present two new neural netwo...


Personalized Activity Recognition with Deep Triplet Embeddings

,  - 2020

A significant challenge for a supervised learning approach to inertial human activity recognition is the heterogeneity of data between individual users, resulting in very poor performance of impersonal algorithms for some subjects. We present an approach to personalized activity recognition based on deep embeddings derived from a fully convolutio...


Retrosynthesis Prediction with Conditional Graph Logic Network

, , , ,  - 2020

Retrosynthesis is one of the fundamental problems in organic chemistry. The task is to identify reactants that can be used to synthesize a specified product molecule. Recently, computer-aided retrosynthesis is finding renewed interest from both chemistry and computer science communities. Most existing approaches rely on template-based models that...


Parameter Space Factorization for Zero-Shot Learning across Tasks and Languages

, , , , ,  - 2020

Most combinations of NLP tasks and language varieties lack in-domain examples for supervised training because of the paucity of annotated data. How can neural models make sample-efficient generalizations from task-language combinations with available data to low-resource ones? In this work, we propose a Bayesian generative model for the space of ...


TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network

, , , , ,  - 2020

Taxonomies consist of machine-interpretable semantics and provide valuable knowledge for many web applications. For example, online retailers (e.g., Amazon and eBay) use taxonomies for product recommendation, and web search engines (e.g., Google and Bing) leverage taxonomies to enhance query understanding. Enormous efforts have been made on const...


Head and Tail Localization of C. elegans

, ,  - 2020

C. elegans is commonly used in neuroscience for behaviour analysis because of it's compact nervous system with well-described connectivity. Localizing the animal and distinguishing between its head and tail are important tasks to track the worm during behavioural assays and to perform quantitative analyses. We demonstrate a neural network based a...


Dual Adversarial Domain Adaptation

, , , , ,  - 2020

Unsupervised domain adaptation aims at transferring knowledge from the labeled source domain to the unlabeled target domain. Previous adversarial domain adaptation methods mostly adopt the discriminator with binary or $K$-dimensional output to perform marginal or conditional alignment independently. Recent experiments have shown that when the dis...


Vertebra-Focused Landmark Detection for Scoliosis Assessment

, , , ,  - 2020

Adolescent idiopathic scoliosis (AIS) is a lifetime disease that arises in children. Accurate estimation of Cobb angles of the scoliosis is essential for clinicians to make diagnosis and treatment decisions. The Cobb angles are measured according to the vertebrae landmarks. Existing regression-based methods for the vertebra landmark detection typ...


Teaching Software Engineering for AI-Enabled Systems

,  - 2020

Software engineers have significant expertise to offer when building intelligent systems, drawing on decades of experience and methods for building systems that are scalable, responsive and robust, even when built on unreliable components. Systems with artificial-intelligence or machine-learning (ML) components raise new challenges and require ca...


Multi-task self-supervised learning for Robust Speech Recognition

, , , , , ,  - 2020

Despite the growing interest in unsupervised learning, extracting meaningful knowledge from unlabelled audio remains an open challenge. To take a step in this direction, we recently proposed a problem-agnostic speech encoder (PASE), that combines a convolutional encoder followed by multiple neural networks, called workers, tasked to solve self-su...