Search Results for author:
Found 6 papers, 2 papers with code
Learning to Influence Vehicles' Routing in Mixed-Autonomy Networks by Dynamically Controlling the Headway of Autonomous Cars
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
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
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
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.
Mitigating Edge Machine Learning Inference Bottlenecks: An Empirical Study on Accelerating Google Edge Models
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.
A General Framework of Online Updating Variable Selection for Generalized Linear Models with Streaming Datasets
The paper presents a general framework for online updating variable selection and parameter estimation in generalized linear models with streaming datasets.
Variable Selection
Methodology
