Jonathan Huang

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Intel

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Stanford University

Research Scientist at Google since 2014, Postdoctoral Fellow in Computer Science Department at Stanford University from 2011-2014, Research Assistant at Carnegie Mellon University from 2005-2011, Intern at Microsoft Research 2010, Intern at Intel Research 2006, NSF/CRA CI (Computing Innovations) fellowship, Member of the Geometric Computation Group, Member at Lytics Lab, a multidisciplinary group focused on Learning Analytics, Ph.D. in Robotics from the School of Computer Science at Carnegie Mellon University in 2011.

Featured Co-authors

Li Zhang
180 publications
Ruslan Salakhutdinov
174 publications
Alan Yuille
161 publications
Cordelia Schmid
121 publications
Li Fei-Fei
120 publications
Yiran Chen
84 publications
Leonidas Guibas
79 publications
Jascha Sohl-Dickstein
63 publications
Hai Li
63 publications
Kevin Murphy
59 publications
Dmitry Vetrov
54 publications

∙ 05/27/2021

Doubly robust, machine learning effect estimation in real-world clinical sciences: A practical evaluation of performance in molecular epidemiology cohort settings

Modern efficient estimators such as AIPW and TMLE facilitate the applica...

Xiang Meng, et al. ∙

∙ 04/06/2021

Local Metrics for Multi-Object Tracking

This paper introduces temporally local metrics for Multi-Object Tracking...

Jack Valmadre, et al. ∙

∙ 04/01/2021

The surprising impact of mask-head architecture on novel class segmentation

Instance segmentation models today are very accurate when trained on lar...

Vighnesh Birodkar, et al. ∙

∙ 10/12/2020

The implications of outcome truncation in reproductive medicine RCTs: a simulation platform for trialists and simulation study

Randomised controlled trials in reproductive medicine are often subject ...

Jack Wilkinson, et al. ∙

∙ 09/28/2020

PERF-Net: Pose Empowered RGB-Flow Net

In recent years, many works in the video action recognition literature h...

Yinxiao Li, et al. ∙

∙ 09/17/2020

Utterance-level Intent Recognition from Keywords

This paper focuses on wake on intent (WOI) techniques for platforms with...

Wenda Chen, et al. ∙

∙ 08/11/2020

Compact Speaker Embedding: lrx-vector

Deep neural networks (DNN) have recently been widely used in speaker rec...

Munir Georges, et al. ∙

∙ 03/30/2020

RetinaTrack: Online Single Stage Joint Detection and Tracking

Traditionally multi-object tracking and object detection are performed u...

Zhichao Lu, et al. ∙

∙ 12/20/2019

Exploring Context, Attention and Audio Features for Audio Visual Scene-Aware Dialog

We are witnessing a confluence of vision, speech and dialog system techn...

Shachi H Kumar, et al. ∙

∙ 12/20/2019

Leveraging Topics and Audio Features with Multimodal Attention for Audio Visual Scene-Aware Dialog

With the recent advancements in Artificial Intelligence (AI), Intelligen...

Shachi H Kumar, et al. ∙

∙ 12/07/2019

Long Term Temporal Context for Per-Camera Object Detection

In static monitoring cameras, useful contextual information can stretch ...

Sara Beery, et al. ∙

∙ 10/25/2019

Structural sparsification for Far-field Speaker Recognition with GNA

Recently, deep neural networks (DNN) have been widely used in speaker re...

Jingchi Zhang, et al. ∙

∙ 12/20/2018

Context, Attention and Audio Feature Explorations for Audio Visual Scene-Aware Dialog

With the recent advancements in AI, Intelligent Virtual Assistants (IVA)...

Shachi H Kumar, et al. ∙

∙ 11/27/2018

Uncertainty aware multimodal activity recognition with Bayesian inference

Deep neural networks (DNNs) provide state-of-the-art results for a multi...

Mahesh Subedar, et al. ∙

∙ 06/07/2018

Multimodal Relational Tensor Network for Sentiment and Emotion Classification

Understanding Affect from video segments has brought researchers from th...

Saurav Sahay, et al. ∙

∙ 03/16/2018

Learning to Segment via Cut-and-Paste

This paper presents a weakly-supervised approach to object instance segm...

Tal Remez, et al. ∙

∙ 12/13/2017

Rethinking Spatiotemporal Feature Learning For Video Understanding

In this paper we study 3D convolutional networks for video understanding...

Saining Xie, et al. ∙

∙ 12/02/2017

Progressive Neural Architecture Search

We propose a method for learning CNN structures that is more efficient t...

Chenxi Liu, et al. ∙

∙ 05/30/2017

Generative Models of Visually Grounded Imagination

It is easy for people to imagine what a man with pink hair looks like, e...

Ramakrishna Vedantam, et al. ∙

∙ 05/05/2017

Motion Prediction Under Multimodality with Conditional Stochastic Networks

Given a visual history, multiple future outcomes for a video scene are e...

Katerina Fragkiadaki, et al. ∙

∙ 12/07/2016

Spatially Adaptive Computation Time for Residual Networks

This paper proposes a deep learning architecture based on Residual Netwo...

Michael Figurnov, et al. ∙

∙ 11/30/2016

Speed/accuracy trade-offs for modern convolutional object detectors

The goal of this paper is to serve as a guide for selecting a detection ...

Jonathan Huang, et al. ∙

∙ 11/09/2015

Detecting events and key actors in multi-person videos

Multi-person event recognition is a challenging task, often with many pe...

Vignesh Ramanathan, et al. ∙

∙ 11/07/2015

Generation and Comprehension of Unambiguous Object Descriptions

We propose a method that can generate an unambiguous description (known ...

Junhua Mao, et al. ∙

∙ 06/19/2015

Deep Knowledge Tracing

Knowledge tracing---where a machine models the knowledge of a student as...

Chris Piech, et al. ∙

∙ 05/22/2015

Learning Program Embeddings to Propagate Feedback on Student Code

Providing feedback, both assessing final work and giving hints to stuck ...

Chris Piech, et al. ∙

∙ 03/05/2015

What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision

We present a novel method for aligning a sequence of instructions to a v...

Jonathan Malmaud, et al. ∙

∙ 07/09/2013

Tuned Models of Peer Assessment in MOOCs

In massive open online courses (MOOCs), peer grading serves as a critica...

Chris Piech, et al. ∙

∙ 02/14/2012

Efficient Probabilistic Inference with Partial Ranking Queries

Distributions over rankings are used to model data in various settings s...

Jonathan Huang, et al. ∙

∙ 06/07/2010

Uncovering the Riffled Independence Structure of Rankings

Representing distributions over permutations can be a daunting task due ...

Jonathan Huang, et al. ∙