
OutofDistribution Detection Using Neural Rendering Generative Models
Outofdistribution (OoD) detection is a natural downstream task for dee...
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InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers
Continuous Normalizing Flows (CNFs) have emerged as promising deep gener...
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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...
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Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors
We study the linear subspace fitting problem in the overparameterized se...
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Deep Learning Techniques for Inverse Problems in Imaging
Recent work in machine learning shows that deep neural networks can be u...
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Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent
Stochastic gradient descent (SGD) with constant momentum and its variant...
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SemiSupervised Learning via New Deep Network Inversion
We exploit a recently derived inversion scheme for arbitrary deep neural...
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SemiSupervised Learning with the Deep Rendering Mixture Model
Semisupervised learning algorithms reduce the high cost of acquiring la...
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A Probabilistic Framework for Deep Learning
We develop a probabilistic framework for deep learning based on the Deep...
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DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks
In this paper we develop a novel computational sensing framework for sen...
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Learned DAMP: Principled Neural Network based Compressive Image Recovery
Compressive image recovery is a challenging problem that requires fast a...
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Learning to Invert: Signal Recovery via Deep Convolutional Networks
The promise of compressive sensing (CS) has been offset by two significa...
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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...
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A Deep Learning Approach to Structured Signal Recovery
In this paper, we develop a new framework for sensing and recovering str...
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oASIS: Adaptive Column Sampling for Kernel Matrix Approximation
Kernel matrices (e.g. Gram or similarity matrices) are essential for man...
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SPRITE: A Response Model For Multiple Choice Testing
Item response theory (IRT) models for categorical response data are wide...
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Quantized Matrix Completion for Personalized Learning
The recently proposed SPARse Factor Analysis (SPARFA) framework for pers...
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TagAware Ordinal Sparse Factor Analysis for Learning and Content Analytics
Machine learning offers novel ways and means to design personalized lear...
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Convex Biclustering
In the biclustering problem, we seek to simultaneously group observation...
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Active Learning for Undirected Graphical Model Selection
This paper studies graphical model selection, i.e., the problem of estim...
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Path Thresholding: Asymptotically TuningFree HighDimensional Sparse Regression
In this paper, we address the challenging problem of selecting tuning pa...
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Timevarying Learning and Content Analytics via Sparse Factor Analysis
We propose SPARFATrace, a new machine learningbased framework for time...
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Swapping Variables for HighDimensional Sparse Regression with Correlated Measurements
We consider the highdimensional sparse linear regression problem of acc...
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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...
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Joint Topic Modeling and Factor Analysis of Textual Information and Graded Response Data
Modern machine learning methods are critical to the development of large...
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Sparse Factor Analysis for Learning and Content Analytics
We develop a new model and algorithms for machine learningbased learnin...
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Greedy Feature Selection for Subspace Clustering
Unions of subspaces provide a powerful generalization to linear subspace...
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SelfExpressive Decompositions for Matrix Approximation and Clustering
Dataaware methods for dimensionality reduction and matrix decomposition...
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A Probabilistic Theory of Deep Learning
A grand challenge in machine learning is the development of computationa...
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Video Compressive Sensing for Spatial Multiplexing Cameras using MotionFlow Models
Spatial multiplexing cameras (SMCs) acquire a (typically static) scene t...
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Signal Recovery on Incoherent Manifolds
Suppose that we observe noisy linear measurements of an unknown signal t...
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Video Compressive Sensing for Dynamic MRI
We present a video compressive sensing framework, termed ktCSLDS, to ac...
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Compressive Acquisition of Dynamic Scenes
Compressive sensing (CS) is a new approach for the acquisition and recov...
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Suboptimality of Nonlocal Means for Images with Sharp Edges
We conduct an asymptotic risk analysis of the nonlocal means image denoi...
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A Theory for Optical flowbased Transport on Image Manifolds
An image articulation manifold (IAM) is the collection of images formed ...
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A Theoretical Analysis of Joint Manifolds
The emergence of lowcost sensor architectures for diverse modalities ha...
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DataMining Textual Responses to Uncover Misconception Patterns
An important, yet largely unstudied, problem in student data analysis is...
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Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions
While computer and communication technologies have provided effective me...
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prDeep: Robust Phase Retrieval with Flexible Deep Neural Networks
Phase retrieval (PR) algorithms have become an important component in ma...
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Unsupervised Learning with Stein's Unbiased Risk Estimator
Learning from unlabeled and noisy data is one of the grand challenges of...
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An ExpectationMaximization Approach to Tuning Generalized Vector Approximate Message Passing
Generalized Vector Approximate Message Passing (GVAMP) is an efficient i...
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MISSION: Ultra LargeScale Feature Selection using CountSketches
Feature selection is an important challenge in machine learning. It play...
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Neural Rendering Model: Joint Generation and Prediction for SemiSupervised Learning
Unsupervised and semisupervised learning are important problems that ar...
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Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks
We investigate the internal representations that a recurrent neural netw...
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RACE: SubLinear Memory Sketches for Approximate NearNeighbor Search on Streaming Data
We demonstrate the first possibility of a sublinear memory sketch for s...
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Adaptive Estimation for Approximate kNearestNeighbor Computations
Algorithms often carry out equally many computations for "easy" and "har...
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Thresholding Graph Bandits with GrAPL
In this paper, we introduce a new online decision making paradigm that w...
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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...
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Drawing earlybird tickets: Towards more efficient training of deep networks
(Frankle & Carbin, 2019) shows that there exist winning tickets (small b...
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Insense: Incoherent Sensor Selection for Sparse Signals
Sensor selection refers to the problem of intelligently selecting a smal...
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Richard G. Baraniuk
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Professor at Rice University, Founder and Director at OpenStax, Founder and Director at Connexions