Publications

Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey.
Emily Evangelista
Rohan Kale
Desiree McCutcheon
Anais Rameau
Alexander Gelbard
Maria Powell
Michael Johns
Anthony Law
Phillip Song
Matthew Naunheim
Stephanie Watts
Paul C. Bryson
Matthew G. Crowson
Jeremy Pinto
Yael Bensoussan
INTRODUCTION Accuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice da… (voir plus)ta are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research. OBJECTIVE The aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research. METHODS A 30-question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research. RESULTS Seventy-two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%-60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi-institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%). CONCLUSION To conduct large-scale multi-institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing. LEVEL OF EVIDENCE Level 5 Laryngoscope, 2023.
2023-12-13
The Laryngoscope (publié)
doi.org
Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey.
Emily Evangelista
Rohan Kale
Desiree McCutcheon
Anais Rameau
Alexander H. Gelbard
Maria Powell
Michael Johns
Anthony Law
Phillip C Song
M. Naunheim
Stephanie Watts
Paul C. Bryson
Matthew G. Crowson
Jeremy M. Pinto
Yael Bensoussan
INTRODUCTION Accuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice da… (voir plus)ta are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research. OBJECTIVE The aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research. METHODS A 30-question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research. RESULTS Seventy-two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%-60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi-institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%). CONCLUSION To conduct large-scale multi-institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing. LEVEL OF EVIDENCE Level 5 Laryngoscope, 2023.
2023-12-13
The Laryngoscope (published)
doi.org
A deep learning benchmark for first break detection from hardrock seismic reflection data
Pierre-Luc St-Charles
Bruno Rousseau
Joumana Ghosn
Gilles Bellefleur
Ernst Schetselaar
2023-12-13
GEOPHYSICS (publié)
doi.org
Privacy-preserving analysis of time-to-event data under nested case-control sampling
Lamin Juwara
Archer Yang
Ana M Velly
Paramita Saha-Chaudhuri
2023-12-13
Statistical Methods in Medical Research (publié)
doi.org
Q-learners Can Provably Collude in the Iterated Prisoner's Dilemma
Quentin Bertrand
Juan Duque
Emilio Calvano
Gauthier Gidel
The deployment of machine learning systems in the market economy has triggered academic and institutional fears over potential tacit collusi… (voir plus)on between fully automated agents. Multiple recent economics studies have empirically shown the emergence of collusive strategies from agents guided by machine learning algorithms. In this work, we prove that multi-agent Q-learners playing the iterated prisoner's dilemma can learn to collude. The complexity of the cooperative multi-agent setting yields multiple fixed-point policies for
2023-12-13
ArXiv (prépublication)
doi.org
arxiv.org
Relative Almost Sure Regret Bounds for Certainty Equivalence Control of Markov Jump Systems
Borna Sayedana
Mohammad Afshari
Peter E. Caines
Aditya Mahajan
In this paper, we consider learning and control problem in an unknown Markov jump linear system (MJLS) with perfect state observations. We f… (voir plus)irst establish a generic upper bound on regret for any learning based algorithm. We then propose a certainty equivalence-based learning alagrithm and show that this algorithm achieves a regret of
2023-12-13
2023 62nd IEEE Conference on Decision and Control (CDC) (publié)
doi.org
Weighted-Norm Bounds on Model Approximation in MDPs with Unbounded Per-Step Cost
Berk Bozkurt
Aditya Mahajan
Ashutosh Nayyar
Yi Ouyang
We consider the problem of designing a control policy for an infinite-horizon discounted cost Markov Decision Process (MDP) …
2023-12-13
IEEE Conference on Decision and Control (publié)
doi.org
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems
Alexandre AGM Duval
Simon V. Mathis
Chaitanya K. Joshi
Victor Schmidt
Santiago Miret
Fragkiskos D. Malliaros
Taco Cohen
Pietro Lio’
Yoshua Bengio
Michael M. Bronstein
2023-12-12
ArXiv (prépublication)
doi.org
arxiv.org
Efficient Graphics Representation with Differentiable Indirection
Sayantan Datta
Carl Marshall
Zhao Dong
Zhengqin Li
Derek Nowrouzezahrai
We introduce differentiable indirection – a novel learned primitive that employs differentiable multi-scale lookup tables as an effective … (voir plus)substitute for traditional compute and data operations across the graphics pipeline. We demonstrate its flexibility on a number of graphics tasks, i.e., geometric and image representation, texture mapping, shading, and radiance field representation. In all cases, differentiable indirection seamlessly integrates into existing architectures, trains rapidly, and yields both versatile and efficient results.
2023-12-11
SIGGRAPH Asia 2023 Conference Papers (publié)
doi.org
arxiv.org
Explorable Mesh Deformation Subspaces from Unstructured 3D Generative Models
Arman Maesumi
Paul Guerrero
Noam Aigerman
Vladimir Kim
Matthew Fisher
Siddhartha Chaudhuri
Daniel Ritchie
2023-12-11
SIGGRAPH Asia 2023 Conference Papers (publié)
doi.org
arxiv.org
Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks
MohammadReza Davari
Eugene Belilovsky
2023-12-11
ArXiv (prépublication)
doi.org
arxiv.org
Lagrangian Properties and Control of Soft Robots Modeled with Discrete Cosserat Rods
Lekan Molu
Shaoru Chen
Audrey Sedal
The characteristic ``in-plane"bending associated with soft robots' deformation make them preferred over rigid robots in sophisticated manipu… (voir plus)lation and movement tasks. Executing such motion strategies to precision in soft deformable robots and structures is however fraught with modeling and control challenges given their infinite degrees-of-freedom. Imposing \textit{piecewise constant strains} (PCS) across (discretized) Cosserat microsolids on the continuum material however, their dynamics become amenable to tractable mathematical analysis. While this PCS model handles the characteristic difficult-to-model ``in-plane"bending well, its Lagrangian properties are not exploited for control in literature neither is there a rigorous study on the dynamic performance of multisection deformable materials for ``in-plane"bending that guarantees steady-state convergence. In this sentiment, we first establish the PCS model's structural Lagrangian properties. Second, we exploit these for control on various strain goal states. Third, we benchmark our hypotheses against an Octopus-inspired robot arm under different constant tip loads. These induce non-constant ``in-plane"deformation and we regulate strain states throughout the continuum in these configurations. Our numerical results establish convergence to desired equilibrium throughout the continuum in all of our tests. Within the bounds here set, we conjecture that our methods can find wide adoption in the control of cable- and fluid-driven multisection soft robotic arms; and may be extensible to the (learning-based) control of deformable agents employed in simulated, mixed, or augmented reality.
2023-12-10
ArXiv (prépublication)
doi.org
arxiv.org