Search Results for author: Xiaoyu Ma

Found 6 papers, 2 papers with code

Learning to Influence Vehicles' Routing in Mixed-Autonomy Networks by Dynamically Controlling the Headway of Autonomous Cars

1 code implementation7 Mar 2023 Xiaoyu Ma, Negar Mehr

We train an RL policy that learns to regulate the headway of autonomous cars such that the total travel time in the network is minimized.

Testing for context-dependent changes in neural encoding in naturalistic experiments

no code implementations17 Nov 2022 Yenho Chen, Carl W. Harris, Xiaoyu Ma, Zheng Li, Francisco Pereira, Charles Y. Zheng

We propose a decoding-based approach to detect context effects on neural codes in longitudinal neural recording data.

A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks

1 code implementation21 Jan 2022 Xiaoyu Ma, Sylvain Sardy, Nick Hengartner, Nikolai Bobenko, Yen Ting Lin

To fit sparse linear associations, a LASSO sparsity inducing penalty with a single hyperparameter provably allows to recover the important features (needles) with high probability in certain regimes even if the sample size is smaller than the dimension of the input vector (haystack).

Google Neural Network Models for Edge Devices: Analyzing and Mitigating Machine Learning Inference Bottlenecks

no code implementations29 Sep 2021 Amirali Boroumand, Saugata Ghose, Berkin Akin, Ravi Narayanaswami, Geraldo F. Oliveira, Xiaoyu Ma, Eric Shiu, Onur Mutlu

To understand how edge ML accelerators perform, we characterize the performance of a commercial Google Edge TPU, using 24 Google edge NN models (which span a wide range of NN model types) and analyzing each NN layer within each model.

Edge-computing Face Detection +3

Mitigating Edge Machine Learning Inference Bottlenecks: An Empirical Study on Accelerating Google Edge Models

no code implementations1 Mar 2021 Amirali Boroumand, Saugata Ghose, Berkin Akin, Ravi Narayanaswami, Geraldo F. Oliveira, Xiaoyu Ma, Eric Shiu, Onur Mutlu

We comprehensively study the characteristics of each NN layer in all of the Google edge models, and find that these shortcomings arise from the one-size-fits-all approach of the accelerator, as there is a high amount of heterogeneity in key layer characteristics both across different models and across different layers in the same model.

BIG-bench Machine Learning Edge-computing

A General Framework of Online Updating Variable Selection for Generalized Linear Models with Streaming Datasets

no code implementations21 Jan 2021 Xiaoyu Ma, Lu Lin, Yujie Gai

The paper presents a general framework for online updating variable selection and parameter estimation in generalized linear models with streaming datasets.

Variable Selection Methodology

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