Search Results for author: Partha P. Mitra

Found 7 papers, 1 papers with code

Understanding overfitting peaks in generalization error: Analytical risk curves for $l_2$ and $l_1$ penalized interpolation

no code implementations9 Jun 2019 Partha P. Mitra

We introduce a generative and fitting model pair ("Misparametrized Sparse Regression" or MiSpaR) and show that the overfitting peak can be dissociated from the point at which the fitting function gains enough dof's to match the data generative model and thus provides good generalization.

SSFN -- Self Size-estimating Feed-forward Network with Low Complexity, Limited Need for Human Intervention, and Consistent Behaviour across Trials

no code implementations17 May 2019 Saikat Chatterjee, Alireza M. Javid, Mostafa Sadeghi, Shumpei Kikuta, Dong Liu, Partha P. Mitra, Mikael Skoglund

We design a self size-estimating feed-forward network (SSFN) using a joint optimization approach for estimation of number of layers, number of nodes and learning of weight matrices.

Image Classification

Locally Convex Sparse Learning over Networks

no code implementations31 Mar 2018 Ahmed Zaki, Saikat Chatterjee, Partha P. Mitra, Lars K. Rasmussen

Our expectation is that local estimates in each node improve fast and converge, resulting in a limited demand for communication of estimates between nodes and reducing the processing time.

Sparse Learning

Fast Convergence for Stochastic and Distributed Gradient Descent in the Interpolation Limit

no code implementations8 Mar 2018 Partha P. Mitra

This analysis is made possible since the SGD algorithm reduces to a stochastic linear system near the interpolating minimum of the loss function.

Progressive Learning for Systematic Design of Large Neural Networks

1 code implementation23 Oct 2017 Saikat Chatterjee, Alireza M. Javid, Mostafa Sadeghi, Partha P. Mitra, Mikael Skoglund

The developed network is expected to show good generalization power due to appropriate regularization and use of random weights in the layers.

Estimate Exchange over Network is Good for Distributed Hard Thresholding Pursuit

no code implementations22 Sep 2017 Ahmed Zaki, Partha P. Mitra, Lars K. Rasmussen, Saikat Chatterjee

The algorithm is iterative and exchanges intermediate estimates of a sparse signal over a network.

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