Cindy Trinh

According to our database, Cindy Trinh authored at least 6 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

On the Development of Descriptor-Based Machine Learning Models for Thermodynamic Properties: Part 2 - Applicability Domain and Outliers.

Cindy Trinh, Silvia Lasala, Olivier Herbinet, Dimitrios Meimaroglou

Algorithms, 2023

2022

MLPerf Mobile Inference Benchmark: An Industry-Standard Open-Source Machine Learning Benchmark for On-Device AI.

Vijay Janapa Reddi, David Kanter, Peter Mattson, Jared Duke, Thai Nguyen, Ramesh Chukka, Kenneth Shiring, Koan-Sin Tan, Mark Charlebois, William Chou, Mostafa El-Khamy, Jungwook Hong, Tom St. John, Cindy Trinh, Michael Buch, Mark Mazumder, Relja Markovic, Thomas Atta-fosu, Fatih Çakir, Masoud Charkhabi, Xiaodong Chen, Cheng-Ming Chiang, Dave Dexter, Terry Heo, Guenther Schmuelling, Maryam Shabani, Dylan Zika

Proceedings of Machine Learning and Systems 2022, 2022

Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information.

Wei Huang, Richard Combes, Cindy Trinh

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

A High Performance, Low Complexity Algorithm for Multi-Player Bandits Without Collision Sensing Information.

Cindy Trinh, Richard Combes

CoRR, 2021

2020

MLPerf Mobile Inference Benchmark: Why Mobile AI Benchmarking Is Hard and What to Do About It.

Vijay Janapa Reddi, David Kanter, Peter Mattson, Jared Duke, Thai Nguyen, Ramesh Chukka, Kenneth Shiring, Koan-Sin Tan, Mark Charlebois, William Chou, Mostafa El-Khamy, Jungwook Hong, Michael Buch, Cindy Trinh, Thomas Atta-fosu, Fatih Çakir, Masoud Charkhabi, Xiaodong Chen, Jimmy Chiang, Dave Dexter, Woncheol Heo, Guenther Schmuelling, Maryam Shabani, Dylan Zika

CoRR, 2020

Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling.

Cindy Trinh, Emilie Kaufmann, Claire Vernade, Richard Combes

Proceedings of the Algorithmic Learning Theory, 2020