Maximilian Lam

According to our database1, Maximilian Lam authored at least 21 papers between 2016 and 2023.

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Bibliography

2023
GWAS quality score for evaluating associated regions in GWAS analyses.
Bioinform., January, 2023

GPU-based Private Information Retrieval for On-Device Machine Learning Inference.
CoRR, 2023

2022
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning.
Trans. Mach. Learn. Res., 2022

Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference.
CoRR, 2022

2021
The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage.
CoRR, 2021

Widening Access to Applied Machine Learning with TinyML.
CoRR, 2021

The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix.
Proceedings of the 38th International Conference on Machine Learning, 2021

Precision Batching: Bitserial Decomposition for Efficient Neural Network Inference on GPUs.
Proceedings of the 30th International Conference on Parallel Architectures and Compilation Techniques, 2021

2020
Benchmarking TinyML Systems: Challenges and Direction.
CoRR, 2020

Quantized Neural Network Inference with Precision Batching.
CoRR, 2020

RICOPILI: Rapid Imputation for COnsortias PIpeLIne.
Bioinform., 2020

2019
Cataloging the visible universe through Bayesian inference in Julia at petascale.
J. Parallel Distributed Comput., 2019

Quantized Reinforcement Learning (QUARL).
CoRR, 2019

2018
Speeding Up Distributed Machine Learning Using Codes.
IEEE Trans. Inf. Theory, 2018

Word2Bits - Quantized Word Vectors.
CoRR, 2018

Exploring the Utility of Developer Exhaust.
Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning, 2018

Cataloging the Visible Universe Through Bayesian Inference at Petascale.
Proceedings of the 2018 IEEE International Parallel and Distributed Processing Symposium, 2018

Gradient Diversity: a Key Ingredient for Scalable Distributed Learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Gradient Diversity Empowers Distributed Learning.
CoRR, 2017

2016
CYCLADES: Conflict-free Asynchronous Machine Learning.
CoRR, 2016


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