DeepAI

Howard Hughes Medical Institute

∙ Chevy Chase , United States ∙ 0 followers

The Howard Hughes Medical Institute is an American non-profit medical research organization based in Chevy Chase, Maryland.

Featured Researchers

Toufiq Parag
17 publications
Daniel Jiwoong Im
12 publications
Nakul Verma
12 publications
Kristin Branson
11 publications
Srinivas C. Turaga
10 publications
Viren Jain
7 publications
Gary B. Huang
7 publications
Juan Nunez-Iglesias
4 publications
John A. Bogovic
3 publications
Srinivas Turaga
3 publications
Stephen Plaza
3 publications

∙ 06/05/2014

A Context-aware Delayed Agglomeration Framework for Electron Microscopy Segmentation

Electron Microscopy (EM) image (or volume) segmentation has become signi...

Toufiq Parag, et al. ∙

∙ 06/07/2019

Importance Weighted Adversarial Variational Autoencoders for Spike Inference from Calcium Imaging Data

The Importance Weighted Auto Encoder (IWAE) objective has been shown to ...

Daniel Jiwoong Im, et al. ∙

∙ 11/06/2018

A Novel Variational Family for Hidden Nonlinear Markov Models

Latent variable models have been widely applied for the analysis and vis...

Daniel Hernández, et al. ∙

∙ 06/12/2018

A Connectome Based Hexagonal Lattice Convolutional Network Model of the Drosophila Visual System

What can we learn from a connectome? We constructed a simplified model o...

Fabian David Tschopp, et al. ∙

∙ 03/02/2018

Quantitatively Evaluating GANs With Divergences Proposed for Training

Generative adversarial networks (GANs) have been extremely effective in ...

Daniel Jiwoong Im, et al. ∙

∙ 03/25/2013

Machine learning of hierarchical clustering to segment 2D and 3D images

We aim to improve segmentation through the use of machine learning tools...

Juan Nunez-Iglesias, et al. ∙

∙ 07/02/2013

Submodularity of a Set Label Disagreement Function

A set label disagreement function is defined over the number of variable...

Toufiq Parag, et al. ∙

∙ 10/01/2013

Deep and Wide Multiscale Recursive Networks for Robust Image Labeling

Feedforward multilayer networks trained by supervised learning have rece...

Gary B. Huang, et al. ∙

∙ 12/20/2013

Learned versus Hand-Designed Feature Representations for 3d Agglomeration

For image recognition and labeling tasks, recent results suggest that ma...

John A. Bogovic, et al. ∙

∙ 11/06/2017

Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations

A powerful approach for understanding neural population dynamics is to e...

Marcel Nonnenmacher, et al. ∙

∙ 06/06/2014

Small Sample Learning of Superpixel Classifiers for EM Segmentation- Extended Version

Pixel and superpixel classifiers have become essential tools for EM segm...

Toufiq Parag, et al. ∙

∙ 09/05/2014

Identifying Synapses Using Deep and Wide Multiscale Recursive Networks

In this work, we propose a learning framework for identifying synapses u...

Gary B. Huang, et al. ∙

∙ 05/26/2015

MLlib: Machine Learning in Apache Spark

Apache Spark is a popular open-source platform for large-scale data proc...

Xiangrui Meng, et al. ∙

∙ 02/04/2016

Discovering Neuronal Cell Types and Their Gene Expression Profiles Using a Spatial Point Process Mixture Model

Cataloging the neuronal cell types that comprise circuitry of individual...

Furong Huang, et al. ∙

∙ 05/11/2015

Sample complexity of learning Mahalanobis distance metrics

Metric learning seeks a transformation of the feature space that enhance...

Nakul Verma, et al. ∙

∙ 07/11/2016

Learning a metric for class-conditional KNN

Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework...

Daniel Jiwoong Im, et al. ∙

∙ 11/06/2017

Fast amortized inference of neural activity from calcium imaging data with variational autoencoders

Calcium imaging permits optical measurement of neural activity. Since in...

Artur Speiser, et al. ∙