1

A Method to Reveal Speaker Identity in Distributed ASR Training, and How to Counter IT
End-to-end Automatic Speech Recognition (ASR) models are commonly trained over spoken utterances using optimization methods like …
Cite DOI
Substitutional Neural Image Compression
We describe Substitutional Neural Image Compression (SNIC), a general approach for enhancing any neural image compression model, that …
Cite DOI
Training Robust Zero-Shot Voice Conversion Models with Self-Supervised Features
Unsupervised Zero-Shot Voice Conversion (VC) aims to modify the speaker characteristic of an utterance to match an unseen target …
Cite DOI
Biologically Plausible Complex-Valued Neural Networks and Model Optimization
Artificial Neural Networks (ANNs) are thinly based on biological neural pathways. In an ANN, each node computes its activation by …
Cite
Deep Reinforcement Learning for FlipIt Security Game
Reinforcement learning has shown much success in games such as chess, backgammon and Go [21, 22, 24]. However, in most of these games, …
Cite
Drug Repurposing Using Link Prediction on Knowledge Graphs with Applications to Non-volatile Memory
The active global SARS-CoV-2 pandemic caused more than 167 million cases and 3.4 million deaths worldwide. The development of …
Cite
Neural-Guided, Bidirectional Program Search for Abstraction and Reasoning
One of the challenges facing artificial intelligence research today is designing systems capable of utilizing systematic reasoning to …
Cite
Towards Fast Crash-Consistent Cluster Checkpointing
Cite DOI
What Is Learned in Knowledge Graph Embeddings?
A knowledge graph (KG) is a data structure which represents entities and relations as the vertices and edges of a directed graph with …
Cite
Corrupting Data to Remove Deceptive Perturbation: Using Preprocessing Method to Improve System Robustness
Although deep neural networks have achieved great performance on classification tasks, recent studies showed that well trained networks …
Cite DOI