
Diagnostic Questions:The NeurIPS 2020 Education Challenge
Digital technologies are becoming increasingly prevalent in education, e...
read it

Ensembles of Generative Adversarial Networks for Disconnected Data
Most current computer vision datasets are composed of disconnected sets,...
read it

Analytical Probability Distributions and EMLearning for Deep Generative Networks
Deep Generative Networks (DGNs) with probabilistic modeling of their out...
read it

Interpretable SuperResolution via a Learned TimeSeries Representation
We develop an interpretable and learnable WignerVille distribution that...
read it

LaRVAE: Label Replacement VAE for SemiSupervised Disentanglement Learning
Learning interpretable and disentangled representations is a crucial yet...
read it

Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks
We study the transfer learning process between two linear regression pro...
read it

MomentumRNN: Integrating Momentum into Recurrent Neural Networks
Designing deep neural networks is an art that often involves an expensiv...
read it

Attention Word Embedding
Word embedding models learn semantically rich vector representations of ...
read it

qDKT: Questioncentric Deep Knowledge Tracing
Knowledge tracing (KT) models, e.g., the deep knowledge tracing (DKT) mo...
read it

Deep Learning Techniques for Inverse Problems in Imaging
Recent work in machine learning shows that deep neural networks can be u...
read it

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...
read it

Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent
Stochastic gradient descent (SGD) with constant momentum and its variant...
read it

InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers
Continuous Normalizing Flows (CNFs) have emerged as promising deep gener...
read it

The Implicit Regularization of Ordinary Least Squares Ensembles
Ensemble methods that average over a collection of independent predictor...
read it

Drawing earlybird tickets: Towards more efficient training of deep networks
(Frankle & Carbin, 2019) shows that there exist winning tickets (small b...
read it

OutofDistribution Detection Using Neural Rendering Generative Models
Outofdistribution (OoD) detection is a natural downstream task for dee...
read it

Thresholding Graph Bandits with GrAPL
In this paper, we introduce a new online decision making paradigm that w...
read 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...
read it

Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks
We investigate the internal representations that a recurrent neural netw...
read it

Adaptive Estimation for Approximate kNearestNeighbor Computations
Algorithms often carry out equally many computations for "easy" and "har...
read it

RACE: SubLinear Memory Sketches for Approximate NearNeighbor Search on Streaming Data
We demonstrate the first possibility of a sublinear memory sketch for s...
read it

Neural Rendering Model: Joint Generation and Prediction for SemiSupervised Learning
Unsupervised and semisupervised learning are important problems that ar...
read 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...
read it

An ExpectationMaximization Approach to Tuning Generalized Vector Approximate Message Passing
Generalized Vector Approximate Message Passing (GVAMP) is an efficient i...
read it

MISSION: Ultra LargeScale Feature Selection using CountSketches
Feature selection is an important challenge in machine learning. It play...
read it

Unsupervised Learning with Stein's Unbiased Risk Estimator
Learning from unlabeled and noisy data is one of the grand challenges of...
read it

prDeep: Robust Phase Retrieval with Flexible Deep Neural Networks
Phase retrieval (PR) algorithms have become an important component in ma...
read it

SemiSupervised Learning via New Deep Network Inversion
We exploit a recently derived inversion scheme for arbitrary deep neural...
read it

DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks
In this paper we develop a novel computational sensing framework for sen...
read it

Learned DAMP: Principled Neural Network based Compressive Image Recovery
Compressive image recovery is a challenging problem that requires fast a...
read it

DataMining Textual Responses to Uncover Misconception Patterns
An important, yet largely unstudied, problem in student data analysis is...
read it

Insense: Incoherent Sensor Selection for Sparse Signals
Sensor selection refers to the problem of intelligently selecting a smal...
read it

Learning to Invert: Signal Recovery via Deep Convolutional Networks
The promise of compressive sensing (CS) has been offset by two significa...
read it

SemiSupervised Learning with the Deep Rendering Mixture Model
Semisupervised learning algorithms reduce the high cost of acquiring la...
read it

A Probabilistic Framework for Deep Learning
We develop a probabilistic framework for deep learning based on the Deep...
read 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...
read it

A Deep Learning Approach to Structured Signal Recovery
In this paper, we develop a new framework for sensing and recovering str...
read it

oASIS: Adaptive Column Sampling for Kernel Matrix Approximation
Kernel matrices (e.g. Gram or similarity matrices) are essential for man...
read it

SelfExpressive Decompositions for Matrix Approximation and Clustering
Dataaware methods for dimensionality reduction and matrix decomposition...
read it

A Probabilistic Theory of Deep Learning
A grand challenge in machine learning is the development of computationa...
read it

Video Compressive Sensing for Spatial Multiplexing Cameras using MotionFlow Models
Spatial multiplexing cameras (SMCs) acquire a (typically static) scene t...
read it

Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions
While computer and communication technologies have provided effective me...
read it

SPRITE: A Response Model For Multiple Choice Testing
Item response theory (IRT) models for categorical response data are wide...
read it

Quantized Matrix Completion for Personalized Learning
The recently proposed SPARse Factor Analysis (SPARFA) framework for pers...
read it

TagAware Ordinal Sparse Factor Analysis for Learning and Content Analytics
Machine learning offers novel ways and means to design personalized lear...
read it

Convex Biclustering
In the biclustering problem, we seek to simultaneously group observation...
read it

Active Learning for Undirected Graphical Model Selection
This paper studies graphical model selection, i.e., the problem of estim...
read it

Path Thresholding: Asymptotically TuningFree HighDimensional Sparse Regression
In this paper, we address the challenging problem of selecting tuning pa...
read it

Video Compressive Sensing for Dynamic MRI
We present a video compressive sensing framework, termed ktCSLDS, to ac...
read it

Timevarying Learning and Content Analytics via Sparse Factor Analysis
We propose SPARFATrace, a new machine learningbased framework for time...
read it
Richard G. Baraniuk
is this you? claim profile
Professor at Rice University, Founder and Director at OpenStax, Founder and Director at Connexions