697 Results
March 29, 2019
The effect of generic headphone compensation on binaural renderings
Audio Engineering Society (AES)
In this study, the effects of generic headphone equalization in binaural rendering are evaluated objectively and subjectively, with respect to unequalized and individually-equalized cases.
By: Isaac Engel, David Lou Alon, Philip Robinson, Ravish Mehra
AR/VR March 28, 2019
Harassment in Social VR: Implications for Design
IEEE Conference on Virtual Reality
We interviewed VR users (n=25) about their experiences with harassment, abuse, and discomfort in social VR.
By: Lindsay Blackwell, Nicole Ellison, Natasha Elliott-Deflo, Raz SchwartzMarch 27, 2019
Evaluation of real-time sound propagation engines in a virtual reality framework
AES International Conference on Immersive and Interactive Audio
Sound propagation in an enclosed space is a combination of several wave phenomena, such as direct sound, specular reflections, scattering, diffraction, or air absorption, among others. Achieving realistic and immersive audio in games and virtual reality (VR) requires real-time modeling of these phenomena.
By: Sebastià V. Amengual Garí, Carl Schissler, Ravish Mehra, Shawn Featherly, Philip W. RobinsonMarch 26, 2019
The Impact of Avatar Tracking Errors on User Experience in VR
IEEE Conference on Virtual Reality
This paper presents a series of experiments employing a sizable subject pool (n=96) that study the impact of motion tracking errors on user experience for activities including social interaction and virtual object manipulation.
By: Nicholas Toothman, Michael Neff
Human Computer Interaction & UX March 23, 2019
The Effect of Hand Size and Interaction Modality on the Virtual Hand Illusion
IEEE Conference on Virtual Reality
In this paper, we consider how concepts related to the virtual hand illusion, user experience, and task efficiency are influenced by variations between the size of a user’s actual hand and their avatar’s hand.
By: Lorraine Lin, Aline Normoyle, Alexandra Adkins, Yu Sun, Andrew Robb, Yuting Ye, Massimiliano Di Luca, Sophie JörgMarch 14, 2019
On the Pitfalls of Measuring Emergent Communication
ArXiv
In this paper, we examine a few intuitive existing metrics for measuring communication, and show that they can be misleading. Specifically, by training deep reinforcement learning agents to play simple matrix games augmented with a communication channel, we find a scenario where agents appear to communicate (their messages provide information about their subsequent action), and yet the messages do not impact the environment or other agent in any way.
By: Ryan Lowe, Jakob Foerster, Y-Lan Boureau, Joelle Pineau, Yann Dauphin
Facebook AI Research 
Machine Learning March 12, 2019
Convolutional neural networks for mesh-based parcellation of the cerebral cortex
Medical Imaging with Deep Learning (MIDL)
We show experimentally on the Human Connectome Project dataset that the proposed graph convolutional models outperform current state-of-the-art and baselines, highlighting the potential and applicability of these methods to tackle neuroimaging challenges, paving the road towards a better characterization of brain diseases.
By: Guillem Cucurull, Konrad Wagstyl, Arantxa Casanova, Petar Velickovic, Estrid Jakobsen, Michal Drozdzal, Adriana Romero, Alan Evans, Yoshua Bengio
Computer Vision March 11, 2019
Stochastic Adaptive Neural Architecture Search for Keyword Spotting
International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
We propose a new method called SANAS (Stochastic Adaptive Neural Architecture Search) which is able to adapt the architecture of the neural network on-the-fly at inference time such that small architectures will be used when the stream is easy to process (silence, low noise, …) and bigger networks will be used when the task becomes more difficult.
By: Tom Véniat, Olivier Schwander, Ludovic DenoyerFebruary 20, 2019
BOLT: A Practical Binary Optimizer for Data Centers and Beyond
International Symposium on Code Generation and Optimization (CGO)
In this paper, we present BOLT, a post-link optimizer built on top of the LLVM framework. Utilizing sample-based profiling, BOLT boosts the performance of real-world applications even for highly optimized binaries built with both feedback-driven optimizations (FDO) and link-time optimizations (LTO).
By: Maksim Panchenko, Rafael Auler, Bill Nell, Guilherme Ottoni
Systems & Networking February 16, 2019
Machine Learning at Facebook: Understanding Inference at the Edge
IEEE International Symposium on High-Performance Computer Architecture (HPCA)
This paper takes a data-driven approach to present the opportunities and design challenges faced by Facebook in order to enable machine learning inference locally on smartphones and other edge platforms.
By: Carole-Jean Wu, David Brooks, Kevin Chen, Douglas Chen, Sy Choudhury, Marat Dukhan, Kim Hazelwood, Eldad Isaac, Yangqing Jia, Bill Jia, Tommer Leyvand, Hao Lu, Yang Lu, Lin Qiao, Brandon Reagen, Joe Spisak, Fei Sun, Andrew Tulloch, Peter Vajda, Xiaodong Wang, Yanghan Wang, Bram Wasti, Yiming Wu, Ran Xian, Sungjoo Yoo, Peizhao Zhang