Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Vassilis Digalakis"'
Autor:
Alessandro Previero, Alexander Jacquillat, Dimitris Bertsimas, Michael Lingzhi Li, Vassilis Digalakis
Publikováno v:
Naval Research Logistics (NRL)
The outbreak of COVID-19 led to a record-breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to mitigate the near-end impact of the pandemic? In the United States in
Autor:
Dimitris Bertsimas, Vassilis Digalakis
Publikováno v:
2022 IEEE 38th International Conference on Data Engineering (ICDE).
Autor:
Alexandre Jacquillat, Bartolomeo Stellato, Michael Li, Ivan Paskov, Kimberly Villalobos Carballo, Hamza Tazi Bouardi, Dimitris Bertsimas, Driss Lahlou Kitane, Arthur Delarue, Omar Skali Lami, Leonard Boussioux, Cynthia Zeng, Agni Orfanoudaki, Jean Pauphilet, Holly Wiberg, Ryan Cory-Wright, Theodore Papalexopoulos, Vassilis Digalakis, Luca Mingardi, Galit Lukin, Omid Nohadani
Publikováno v:
Springer US
Health Care Management Science
Health Care Management Science
The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c08d52eea024fcb82665c37886da49b4
Autor:
Vassilis Digalakis, Dimitris Bertsimas
We present a novel approach for the problem of frequency estimation in data streams that is based on optimization and machine learning. Contrary to state-of-the-art streaming frequency estimation algorithms, which heavily rely on random hashing to ma
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0ddb2ee55590782dd9a1510f8d8d767
Autor:
Dimitris Bertsimas, Vassilis Digalakis
We present the backbone method, a generic framework that enables sparse and interpretable supervised machine learning methods to scale to ultra-high dimensional problems. We solve sparse regression problems with $10^7$ features in minutes and $10^8$
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04c9649ba46bc2f9722ca32b9f8c75f5
Publikováno v:
IEEE Signal Processing Letters. 23:1057-1061
Linear Dynamical Models (LDMs) have been used in speech synthesis recently as an alternative to hidden Markov models (HMMs). Among the advantages of LDMs are the ability to capture the dynamics of speech and the achievement of synthesized speech qual
Publikováno v:
Speech Communication. 30:121-130
This work is part of an effort aimed at developing computer-based systems for language instruction; we address the task of grading the pronunciation quality of the speech of a student of a foreign language. The automatic grading system uses SRI's Dec
Publikováno v:
Speech Communication. 30:83-93
We present a paradigm for the automatic assessment of pronunciation quality by machine. In this scoring paradigm, both native and nonnative speech data is collected and a database of human-expert ratings is created to enable the development of a vari
Publikováno v:
The Journal of VLSI Signal Processing. 24:223-240
An architecture is presented for real-time continuous speech recognition based on a modified hidden Markov model. The algorithm is adapted to the needs of continuous speech recognition by efficient encoding of the state space, and logarithmic encodin
Publikováno v:
WMuNeP
In this work we investigate the effects of lossy data networks on the speech recognition performance, utilizing a stock information corpus.