Jonathan Huang

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Google ∙ Intel ∙ 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

Ruslan Salakhutdinov
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Li Zhang
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Leonidas Guibas
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Kevin Murphy
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Yiran Chen
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Yang Song
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Jascha Sohl-Dickstein
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Dmitry Vetrov
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Jonathon Shlens
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RetinaTrack: Online Single Stage Joint Detection and Tracking

Traditionally multi-object tracking and object detection are performed u...
03/30/2020 ∙ by Zhichao Lu, et al. ∙ 13 ∙ share

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Long Term Temporal Context for Per-Camera Object Detection

In static monitoring cameras, useful contextual information can stretch ...
12/07/2019 ∙ by Sara Beery, et al. ∙ 1 ∙ share

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Learning Program Embeddings to Propagate Feedback on Student Code

Providing feedback, both assessing final work and giving hints to stuck ...
05/22/2015 ∙ by Chris Piech, et al. ∙ 0 ∙ share

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Generative Models of Visually Grounded Imagination

It is easy for people to imagine what a man with pink hair looks like, e...
05/30/2017 ∙ by Ramakrishna Vedantam, et al. ∙ 0 ∙ share

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Motion Prediction Under Multimodality with Conditional Stochastic Networks

Given a visual history, multiple future outcomes for a video scene are e...
05/05/2017 ∙ by Katerina Fragkiadaki, et al. ∙ 0 ∙ share

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Progressive Neural Architecture Search

We propose a method for learning CNN structures that is more efficient t...
12/02/2017 ∙ by Chenxi Liu, et al. ∙ 0 ∙ share

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Deep Knowledge Tracing

Knowledge tracing---where a machine models the knowledge of a student as...
06/19/2015 ∙ by Chris Piech, et al. ∙ 0 ∙ share

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Spatially Adaptive Computation Time for Residual Networks

This paper proposes a deep learning architecture based on Residual Netwo...
12/07/2016 ∙ by Michael Figurnov, et al. ∙ 0 ∙ share

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Speed/accuracy trade-offs for modern convolutional object detectors

The goal of this paper is to serve as a guide for selecting a detection ...
11/30/2016 ∙ by Jonathan Huang, et al. ∙ 0 ∙ share

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Detecting events and key actors in multi-person videos

Multi-person event recognition is a challenging task, often with many pe...
11/09/2015 ∙ by Vignesh Ramanathan, et al. ∙ 0 ∙ share

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Tuned Models of Peer Assessment in MOOCs

In massive open online courses (MOOCs), peer grading serves as a critica...
07/09/2013 ∙ by Chris Piech, et al. ∙ 0 ∙ share

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Uncovering the Riffled Independence Structure of Rankings

Representing distributions over permutations can be a daunting task due ...
06/07/2010 ∙ by Jonathan Huang, et al. ∙ 0 ∙ share

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Efficient Probabilistic Inference with Partial Ranking Queries

Distributions over rankings are used to model data in various settings s...
02/14/2012 ∙ by Jonathan Huang, et al. ∙ 0 ∙ share

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Generation and Comprehension of Unambiguous Object Descriptions

We propose a method that can generate an unambiguous description (known ...
11/07/2015 ∙ by Junhua Mao, et al. ∙ 0 ∙ share

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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...
03/05/2015 ∙ by Jonathan Malmaud, et al. ∙ 0 ∙ share

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Rethinking Spatiotemporal Feature Learning For Video Understanding

In this paper we study 3D convolutional networks for video understanding...
12/13/2017 ∙ by Saining Xie, et al. ∙ 0 ∙ share

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Learning to Segment via Cut-and-Paste

This paper presents a weakly-supervised approach to object instance segm...
03/16/2018 ∙ by Tal Remez, et al. ∙ 0 ∙ share

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Multimodal Relational Tensor Network for Sentiment and Emotion Classification

Understanding Affect from video segments has brought researchers from th...
06/07/2018 ∙ by Saurav Sahay, et al. ∙ 0 ∙ share

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

With the recent advancements in AI, Intelligent Virtual Assistants (IVA)...
12/20/2018 ∙ by Shachi H Kumar, et al. ∙ 0 ∙ share

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Uncertainty aware multimodal activity recognition with Bayesian inference

Deep neural networks (DNNs) provide state-of-the-art results for a multi...
11/27/2018 ∙ by Mahesh Subedar, et al. ∙ 0 ∙ share

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Structural sparsification for Far-field Speaker Recognition with GNA

Recently, deep neural networks (DNN) have been widely used in speaker re...
10/25/2019 ∙ by Jingchi Zhang, et al. ∙ 0 ∙ share

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

We are witnessing a confluence of vision, speech and dialog system techn...
12/20/2019 ∙ by Shachi H Kumar, et al. ∙ 0 ∙ share

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

With the recent advancements in Artificial Intelligence (AI), Intelligen...
12/20/2019 ∙ by Shachi H Kumar, et al. ∙ 0 ∙ share

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