Machine Learning

Eric Cosatto

Senior Researcher

Machine Learning

NEC Laboratories America, Inc.

4 Independence Way, Suite 200
Princeton, NJ 08540

Telephone: +1 609-951-4811

Fax: +1 609-951-2482

Email: cosatto@nec-labs.com

Publications

Size and Alignment Independent Classification of the High-order Spatial Modes of a Light Beam Using a Convolutional Neural Network
Computing Conference 2019
pp 688-696, 2019
Aashima Singh, Giovanni Milione, Eric Cosatto, Philip N. Ji
07/16/2019
Classification of Mitotic Figures with Convolutional Neural Networks and Seeded Blob Features
Journal of Pathology Informatics
4(9): DOI: 10.4103/2153-3539.112694, 2013
Christopher Malon, Eric Cosatto
10/01/2013
Automatic classification of hepatocellular carcinoma images based on nuclear and structural features
Medical Imaging 2013: Digital Pathology (Eds. M.N. Gurcan, A. Madabhushi)
Proc. of SPIE Vol. 8676 86760Y-1-6, 2013 SPIE
Tomoharu Kiyuna, Akira Saito, Atsushi Marugame, Yoshiko Yamashita, Maki Ogura, Eric Cosatto, Tokiya Abe, Akinori Hashiguchi, Michiiee Sakamoto
02/09/2013

More Publications

Projects

Digital Pathology

Accurate and fast pathological diagnosis is crucial to prevention, early detection and early treatment in defeating cander. NEC is developing a cancer diagnosis assistance system using digital image processing technologies and machine learning algorithms. With this system, NEC aims to help improve the administrative efficiency and diagnostic quality of pathological diagnosis READ MORE

Parallel Computation in Learning

We explore algorithms for implementing large scale learning algorithms as parallel computation. We are currently developing parallelization approaches for increasing the ability of SVM (Support Vector Machines) to solve large-scale problems. As target systems, we consider shared memory processors, clusters of processors, vector processors, and SIMD (Single Instruction Multiple Data) processors. On a given system the speed of an SVM is limited by the computer performance of the processor as well as by the size of the memory. Efficient parallelizations have to overcome both of these limitations while not getting bogged down in communication overhead. READ MORE

Software

MiLDe

MiLDe is an integrated development environment with a suite of machine learning tools including SVMs, Neural Networks (using the Torch package), image analysis, numeric functions and dataset handling. READ MORE

Eric Cosatto

Publications

Size and Alignment Independent Classification of the High-order Spatial Modes of a Light Beam Using a Convolutional Neural Network

Classification of Mitotic Figures with Convolutional Neural Networks and Seeded Blob Features

Automatic classification of hepatocellular carcinoma images based on nuclear and structural features

Princeton Campus

San Jose Campus