activity
20182020
most citedFinding Experts in Transformer Models

15 citations · 15 across the 2 of their papers we have counts for

collaborators

5 papers

eess.AS2020

Modality Dropout for Improved Performance-driven Talking Faces

Ahmed Hussen Abdelaziz, Barry-John Theobald, Paul Dixon +3

We describe our novel deep learning approach for driving animated faces using both acoustic and visual information. In particular, speech-related facial movements are generated usi…

cs.AI202015 cited

Finding Experts in Transformer Models

Xavier Suau, Luca Zappella, Nicholas Apostoloff

In this work we study the presence of expert units in pre-trained Transformer Models (TM), and how they impact a model's performance. We define expert units to be neurons that are…

eess.AS2019

Speaker-Independent Speech-Driven Visual Speech Synthesis using Domain-Adapted Acoustic Models

Ahmed Hussen Abdelaziz, Barry-John Theobald, Justin Binder +5

Speech-driven visual speech synthesis involves mapping features extracted from acoustic speech to the corresponding lip animation controls for a face model. This mapping can take m…

cs.HC2019

Mirroring to Build Trust in Digital Assistants

Katherine Metcalf, Barry-John Theobald, Garrett Weinberg +4

We describe experiments towards building a conversational digital assistant that considers the preferred conversational style of the user. In particular, these experiments are desi…

cs.LG2018

Learning Sharing Behaviors with Arbitrary Numbers of Agents

Katherine Metcalf, Barry-John Theobald, Nicholas Apostoloff

We propose a method for modeling and learning turn-taking behaviors for accessing a shared resource. We model the individual behavior for each agent in an interaction and then use…