
OutofDistribution Detection Using Neural Rendering Generative Models
Outofdistribution (OoD) detection is a natural downstream task for dee...
07/10/2019 ∙ by Yujia Huang, et al. ∙ 35 ∙ shareread it

From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference
Nonlinearity is crucial to the performance of a deep (neural) network (D...
10/22/2018 ∙ by Randall Balestriero, et al. ∙ 16 ∙ shareread it

SemiSupervised Learning via New Deep Network Inversion
We exploit a recently derived inversion scheme for arbitrary deep neural...
11/12/2017 ∙ by Randall Balestriero, et al. ∙ 0 ∙ shareread it

SemiSupervised Learning with the Deep Rendering Mixture Model
Semisupervised learning algorithms reduce the high cost of acquiring la...
12/06/2016 ∙ by Tan Nguyen, et al. ∙ 0 ∙ shareread it

A Probabilistic Framework for Deep Learning
We develop a probabilistic framework for deep learning based on the Deep...
12/06/2016 ∙ by Ankit B. Patel, et al. ∙ 0 ∙ shareread it

DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks
In this paper we develop a novel computational sensing framework for sen...
07/11/2017 ∙ by Ali Mousavi, et al. ∙ 0 ∙ shareread it

Learned DAMP: Principled Neural Network based Compressive Image Recovery
Compressive image recovery is a challenging problem that requires fast a...
04/21/2017 ∙ by Christopher A. Metzler, et al. ∙ 0 ∙ shareread it

Learning to Invert: Signal Recovery via Deep Convolutional Networks
The promise of compressive sensing (CS) has been offset by two significa...
01/14/2017 ∙ by Ali Mousavi, et al. ∙ 0 ∙ shareread it

Consistent Parameter Estimation for LASSO and Approximate Message Passing
We consider the problem of recovering a vector β_o ∈R^p from n random an...
11/03/2015 ∙ by Ali Mousavi, et al. ∙ 0 ∙ shareread it

A Deep Learning Approach to Structured Signal Recovery
In this paper, we develop a new framework for sensing and recovering str...
08/17/2015 ∙ by Ali Mousavi, et al. ∙ 0 ∙ shareread it

oASIS: Adaptive Column Sampling for Kernel Matrix Approximation
Kernel matrices (e.g. Gram or similarity matrices) are essential for man...
05/19/2015 ∙ by Raajen Patel, et al. ∙ 0 ∙ shareread it

SPRITE: A Response Model For Multiple Choice Testing
Item response theory (IRT) models for categorical response data are wide...
01/12/2015 ∙ by Ryan Ning, et al. ∙ 0 ∙ shareread it

Quantized Matrix Completion for Personalized Learning
The recently proposed SPARse Factor Analysis (SPARFA) framework for pers...
12/18/2014 ∙ by Andrew S. Lan, et al. ∙ 0 ∙ shareread it

TagAware Ordinal Sparse Factor Analysis for Learning and Content Analytics
Machine learning offers novel ways and means to design personalized lear...
12/18/2014 ∙ by Andrew S. Lan, et al. ∙ 0 ∙ shareread it

Convex Biclustering
In the biclustering problem, we seek to simultaneously group observation...
08/05/2014 ∙ by Eric C. Chi, et al. ∙ 0 ∙ shareread it

Active Learning for Undirected Graphical Model Selection
This paper studies graphical model selection, i.e., the problem of estim...
04/13/2014 ∙ by Divyanshu Vats, et al. ∙ 0 ∙ shareread it

Path Thresholding: Asymptotically TuningFree HighDimensional Sparse Regression
In this paper, we address the challenging problem of selecting tuning pa...
02/23/2014 ∙ by Divyanshu Vats, et al. ∙ 0 ∙ shareread it

Timevarying Learning and Content Analytics via Sparse Factor Analysis
We propose SPARFATrace, a new machine learningbased framework for time...
12/19/2013 ∙ by Andrew S. Lan, et al. ∙ 0 ∙ shareread it

Swapping Variables for HighDimensional Sparse Regression with Correlated Measurements
We consider the highdimensional sparse linear regression problem of acc...
12/05/2013 ∙ by Divyanshu Vats, et al. ∙ 0 ∙ shareread it

Asymptotic Analysis of LASSOs Solution Path with Implications for Approximate Message Passing
This paper concerns the performance of the LASSO (also knows as basis pu...
09/23/2013 ∙ by Ali Mousavi, et al. ∙ 0 ∙ shareread it

Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data
Modern machine learning methods are critical to the development of large...
05/08/2013 ∙ by Andrew S. Lan, et al. ∙ 0 ∙ shareread it

Sparse Factor Analysis for Learning and Content Analytics
We develop a new model and algorithms for machine learningbased learnin...
03/22/2013 ∙ by Andrew S. Lan, et al. ∙ 0 ∙ shareread it

Greedy Feature Selection for Subspace Clustering
Unions of subspaces provide a powerful generalization to linear subspace...
03/19/2013 ∙ by Eva L. Dyer, et al. ∙ 0 ∙ shareread it

SelfExpressive Decompositions for Matrix Approximation and Clustering
Dataaware methods for dimensionality reduction and matrix decomposition...
05/04/2015 ∙ by Eva L. Dyer, et al. ∙ 0 ∙ shareread it

A Probabilistic Theory of Deep Learning
A grand challenge in machine learning is the development of computationa...
04/02/2015 ∙ by Ankit B. Patel, et al. ∙ 0 ∙ shareread it

Video Compressive Sensing for Spatial Multiplexing Cameras using MotionFlow Models
Spatial multiplexing cameras (SMCs) acquire a (typically static) scene t...
03/09/2015 ∙ by Aswin C. Sankaranarayanan, et al. ∙ 0 ∙ shareread it

Signal Recovery on Incoherent Manifolds
Suppose that we observe noisy linear measurements of an unknown signal t...
02/08/2012 ∙ by Chinmay Hegde, et al. ∙ 0 ∙ shareread it

Video Compressive Sensing for Dynamic MRI
We present a video compressive sensing framework, termed ktCSLDS, to ac...
01/30/2014 ∙ by Jianing V. Shi, et al. ∙ 0 ∙ shareread it

Compressive Acquisition of Dynamic Scenes
Compressive sensing (CS) is a new approach for the acquisition and recov...
01/23/2012 ∙ by Aswin C. Sankaranarayanan, et al. ∙ 0 ∙ shareread it

Suboptimality of Nonlocal Means for Images with Sharp Edges
We conduct an asymptotic risk analysis of the nonlocal means image denoi...
11/24/2011 ∙ by Arian Maleki, et al. ∙ 0 ∙ shareread it

A Theory for Optical flowbased Transport on Image Manifolds
An image articulation manifold (IAM) is the collection of images formed ...
11/22/2011 ∙ by Sriram Nagaraj, et al. ∙ 0 ∙ shareread it

A Theoretical Analysis of Joint Manifolds
The emergence of lowcost sensor architectures for diverse modalities ha...
01/07/2009 ∙ by Mark A. Davenport, et al. ∙ 0 ∙ shareread it

DataMining Textual Responses to Uncover Misconception Patterns
An important, yet largely unstudied, problem in student data analysis is...
03/24/2017 ∙ by Joshua J. Michalenko, et al. ∙ 0 ∙ shareread it

Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions
While computer and communication technologies have provided effective me...
01/18/2015 ∙ by Andrew S. Lan, et al. ∙ 0 ∙ shareread it

prDeep: Robust Phase Retrieval with Flexible Deep Neural Networks
Phase retrieval (PR) algorithms have become an important component in ma...
03/01/2018 ∙ by Christopher A. Metzler, et al. ∙ 0 ∙ shareread it

Unsupervised Learning with Stein's Unbiased Risk Estimator
Learning from unlabeled and noisy data is one of the grand challenges of...
05/26/2018 ∙ by Christopher A. Metzler, et al. ∙ 0 ∙ shareread it

An ExpectationMaximization Approach to Tuning Generalized Vector Approximate Message Passing
Generalized Vector Approximate Message Passing (GVAMP) is an efficient i...
06/26/2018 ∙ by Christopher A. Metzler, et al. ∙ 0 ∙ shareread it

MISSION: Ultra LargeScale Feature Selection using CountSketches
Feature selection is an important challenge in machine learning. It play...
06/12/2018 ∙ by Amirali Aghazadeh, et al. ∙ 0 ∙ shareread it

Neural Rendering Model: Joint Generation and Prediction for SemiSupervised Learning
Unsupervised and semisupervised learning are important problems that ar...
11/01/2018 ∙ by Nhat Ho, et al. ∙ 0 ∙ shareread it

Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks
We investigate the internal representations that a recurrent neural netw...
02/27/2019 ∙ by Joshua J. Michalenko, et al. ∙ 0 ∙ shareread it

RACE: SubLinear Memory Sketches for Approximate NearNeighbor Search on Streaming Data
We demonstrate the first possibility of a sublinear memory sketch for s...
02/18/2019 ∙ by Benjamin Coleman, et al. ∙ 0 ∙ shareread it

Adaptive Estimation for Approximate kNearestNeighbor Computations
Algorithms often carry out equally many computations for "easy" and "har...
02/25/2019 ∙ by Daniel LeJeune, et al. ∙ 0 ∙ shareread it

Thresholding Graph Bandits with GrAPL
In this paper, we introduce a new online decision making paradigm that w...
05/22/2019 ∙ by Daniel LeJeune, et al. ∙ 0 ∙ shareread it

IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election
The 2016 United States presidential election has been characterized as a...
05/21/2019 ∙ by Indu Manickam, et al. ∙ 0 ∙ shareread it

Drawing earlybird tickets: Towards more efficient training of deep networks
(Frankle & Carbin, 2019) shows that there exist winning tickets (small b...
09/26/2019 ∙ by Haoran You, et al. ∙ 0 ∙ shareread it

Insense: Incoherent Sensor Selection for Sparse Signals
Sensor selection refers to the problem of intelligently selecting a smal...
02/16/2017 ∙ by Amirali Aghazadeh, et al. ∙ 0 ∙ shareread it

The Implicit Regularization of Ordinary Least Squares Ensembles
Ensemble methods that average over a collection of independent predictor...
10/10/2019 ∙ by Daniel LeJeune, et al. ∙ 0 ∙ shareread it
Richard G. Baraniuk
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Professor at Rice University, Founder and Director at OpenStax, Founder and Director at Connexions