Sort by: Year Popularity Relevance

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...


Understanding and mitigating gradient pathologies in physics-informed neural networks

, ,  - 2020

The widespread use of neural networks across different scientific domains often involves constraining them to satisfy certain symmetries, conservation laws, or other domain knowledge. Such constraints are often imposed as soft penalties during model training and effectively act as domain-specific regularizers of the empirical risk loss. Physics-i...


Blue River Controls: A toolkit for Reinforcement Learning Control Systems on Hardware

, ,  - 2020

We provide a simple hardware wrapper around the Quanser's hardware-in-the-loop software development kit (HIL SDK) to allow for easy development of new Quanser hardware. To connect to the hardware we use a module written in Cython. The internal QuanserWrapper class handles most of the difficult aspects of interacting with hardware, including the t...


A System for Real-Time Interactive Analysis of Deep Learning Training

, ,  - 2020

Performing diagnosis or exploratory analysis during the training of deep learning models is challenging but often necessary for making a sequence of decisions guided by the incremental observations. Currently available systems for this purpose are limited to monitoring only the logged data that must be specified before the training process starts...


Understanding the Automated Parameter Optimization on Transfer Learning for CPDP: An Empirical Study

, , , ,  - 2020

Data-driven defect prediction has become increasingly important in software engineering process. Since it is not uncommon that data from a software project is insufficient for training a reliable defect prediction model, transfer learning that borrows data/knowledge from other projects to facilitate the model building at the current project, name...


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...


NPLDA: A Deep Neural PLDA Model for Speaker Verification

, ,  - 2020

The state-of-art approach for speaker verification consists of a neural network based embedding extractor along with a backend generative model such as the Probabilistic Linear Discriminant Analysis (PLDA). In this work, we propose a neural network approach for backend modeling in speaker recognition. The likelihood ratio score of the generative ...


Variational Depth Search in ResNets

, ,  - 2020

One-shot neural architecture search allows joint learning of weights and network architecture, reducing computational cost. We limit our search space to the depth of residual networks and formulate an analytically tractable variational objective that allows for obtaining an unbiased approximate posterior over depths in one-shot. We propose a heur...


A Common Semantic Space for Monolingual and Cross-Lingual Meta-Embeddings

, ,  - 2020

This paper presents a new technique for creating monolingual and cross-lingual meta-embeddings. Our method integrates multiple word embeddings created from complementary techniques, textual sources, knowledge bases and languages. Existing word vectors are projected to a common semantic space using linear transformations and averaging. With our me...


Tree-Structured Policy based Progressive Reinforcement Learning for Temporally Language Grounding in Video

, , ,  - 2020

Temporally language grounding in untrimmed videos is a newly-raised task in video understanding. Most of the existing methods suffer from inferior efficiency, lacking interpretability, and deviating from the human perception mechanism. Inspired by human's coarse-to-fine decision-making paradigm, we formulate a novel Tree-Structured Policy based P...