Search Results for author: Erik B. Sudderth

Found 31 papers, 4 papers with code

Learning Consistent Deep Generative Models from Sparsely Labeled Data

no code implementations pproximateinference AABI Symposium 2022 Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C Hughes, Erik B. Sudderth

We consider training deep generative models toward two simultaneous goals: discriminative classification and generative modeling using an explicit likelihood.

Image Classification

Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints

no code implementations12 Dec 2020 Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C. Hughes, Erik B. Sudderth

We develop a new framework for learning variational autoencoders and other deep generative models that balances generative and discriminative goals.

General Classification Image Classification

Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts

no code implementations11 Jun 2019 Zhile Ren, Erik B. Sudderth

We develop new representations and algorithms for three-dimensional (3D) object detection and spatial layout prediction in cluttered indoor scenes.

3D Object Detection General Classification +2

A Fusion Approach for Multi-Frame Optical Flow Estimation

2 code implementations23 Oct 2018 Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B. Sudderth, Jan Kautz

To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account.

Optical Flow Estimation

3D Object Detection With Latent Support Surfaces

no code implementations CVPR 2018 Zhile Ren, Erik B. Sudderth

We develop a 3D object detection algorithm that uses latent support surfaces to capture contextual relationships in indoor scenes.

3D Object Detection object-detection

Prediction-Constrained Topic Models for Antidepressant Recommendation

no code implementations1 Dec 2017 Michael C. Hughes, Gabriel Hope, Leah Weiner, Thomas H. McCoy, Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez

Supervisory signals can help topic models discover low-dimensional data representations that are more interpretable for clinical tasks.

Topic Models

Bayesian Paragraph Vectors

no code implementations10 Nov 2017 Geng Ji, Robert Bamler, Erik B. Sudderth, Stephan Mandt

Word2vec (Mikolov et al., 2013) has proven to be successful in natural language processing by capturing the semantic relationships between different words.

Natural Language Processing Sentiment Analysis +1

From Patches to Images: A Nonparametric Generative Model

1 code implementation ICML 2017 Geng Ji, Michael C. Hughes, Erik B. Sudderth

Our model is based on a novel, variational interpretation of the popular expected patch log-likelihood (EPLL) method as a model for randomly positioned grids of image patches.

Denoising Image Inpainting +1

Cascaded Scene Flow Prediction using Semantic Segmentation

no code implementations26 Jul 2017 Zhile Ren, Deqing Sun, Jan Kautz, Erik B. Sudderth

Given two consecutive frames from a pair of stereo cameras, 3D scene flow methods simultaneously estimate the 3D geometry and motion of the observed scene.

Autonomous Driving General Classification +3

Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models

no code implementations23 Jul 2017 Michael C. Hughes, Leah Weiner, Gabriel Hope, Thomas H. McCoy Jr., Roy H. Perlis, Erik B. Sudderth, Finale Doshi-Velez

Supervisory signals have the potential to make low-dimensional data representations, like those learned by mixture and topic models, more interpretable and useful.

Sentiment Analysis Topic Models

Fast Learning of Clusters and Topics via Sparse Posteriors

no code implementations23 Sep 2016 Michael C. Hughes, Erik B. Sudderth

Mixture models and topic models generate each observation from a single cluster, but standard variational posteriors for each observation assign positive probability to all possible clusters.

Topic Models

Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients

no code implementations CVPR 2016 Zhile Ren, Erik B. Sudderth

We develop new representations and algorithms for three-dimensional (3D) object detection and spatial layout prediction in cluttered indoor scenes.

3D Object Detection object-detection +1

Layered RGBD Scene Flow Estimation

no code implementations CVPR 2015 Deqing Sun, Erik B. Sudderth, Hanspeter Pfister

As consumer depth sensors become widely available, estimating scene flow from RGBD sequences has received increasing attention.

Optical Flow Estimation Scene Flow Estimation +1

From Deformations to Parts: Motion-based Segmentation of 3D Objects

2 code implementations NeurIPS 2012 Soumya Ghosh, Matthew Loper, Erik B. Sudderth, Michael J. Black

We develop a method for discovering the parts of an articulated object from aligned meshes capturing various three-dimensional (3D) poses.

Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes

no code implementations NeurIPS 2012 Michael Bryant, Erik B. Sudderth

Variational methods provide a computationally scalable alternative to Monte Carlo methods for large-scale, Bayesian nonparametric learning.

Continual Learning Variational Inference

Minimization of Continuous Bethe Approximations: A Positive Variation

no code implementations NeurIPS 2012 Jason Pacheco, Erik B. Sudderth

We develop convergent minimization algorithms for Bethe variational approximations which explicitly constrain marginal estimates to families of valid distributions.

Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data

no code implementations NeurIPS 2012 Michael C. Hughes, Emily Fox, Erik B. Sudderth

Applications of Bayesian nonparametric methods require learning and inference algorithms which efficiently explore models of unbounded complexity.

Time Series

The Doubly Correlated Nonparametric Topic Model

no code implementations NeurIPS 2011 Dae I. Kim, Erik B. Sudderth

Topic models are learned via a statistical model of variation within document collections, but designed to extract meaningful semantic structure.

Topic Models

Global seismic monitoring as probabilistic inference

no code implementations NeurIPS 2010 Nimar Arora, Stuart J. Russell, Paul Kidwell, Erik B. Sudderth

The International Monitoring System (IMS) is a global network of sensors whose purpose is to identify potential violations of the Comprehensive Nuclear-Test-Ban Treaty (CTBT), primarily through detection and localization of seismic events.

Bayesian Inference

Bayesian Nonparametric Inference of Switching Linear Dynamical Systems

1 code implementation19 Mar 2010 Emily B. Fox, Erik B. Sudderth, Michael I. Jordan, Alan S. Willsky

Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes.

Sharing Features among Dynamical Systems with Beta Processes

no code implementations NeurIPS 2009 Emily Fox, Michael. I. Jordan, Erik B. Sudderth, Alan S. Willsky

We propose a Bayesian nonparametric approach to relating multiple time series via a set of latent, dynamical behaviors.

Time Series

A sticky HDP-HMM with application to speaker diarization

no code implementations15 May 2009 Emily B. Fox, Erik B. Sudderth, Michael. I. Jordan, Alan S. Willsky

To address this problem, we take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of Teh et al. [J. Amer.

Speaker Diarization

Nonparametric Bayesian Learning of Switching Linear Dynamical Systems

no code implementations NeurIPS 2008 Emily Fox, Erik B. Sudderth, Michael. I. Jordan, Alan S. Willsky

Many nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes.

Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes

no code implementations NeurIPS 2008 Erik B. Sudderth, Michael. I. Jordan

We develop a statistical framework for the simultaneous, unsupervised segmentation and discovery of visual object categories from image databases.

Gaussian Processes

Loop Series and Bethe Variational Bounds in Attractive Graphical Models

no code implementations NeurIPS 2007 Alan S. Willsky, Erik B. Sudderth, Martin J. Wainwright

Variational methods are frequently used to approximate or bound the partition or likelihood function of a Markov random field.

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