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no code implementations • 15 Oct 2021 • CJ Barberan, Randall Balestriero, Richard G. Baraniuk

Each member of the family is derived from a standard DN architecture by vector quantizing the unit output values and feeding them into a global linear classifier.

1 code implementation • 11 Oct 2021 • Sina AlEMohammad, Hossein Babaei, CJ Barberan, Naiming Liu, Lorenzo Luzi, Blake Mason, Richard G. Baraniuk

To further contribute interpretability with respect to classification and the layers, we develop a new network as a combination of multiple neural tangent kernels, one to model each layer of the deep neural network individually as opposed to past work which attempts to represent the entire network via a single neural tangent kernel.

no code implementations • 8 Oct 2021 • Lorenzo Luzi, Carlos Ortiz Marrero, Nile Wynar, Richard G. Baraniuk, Michael J. Henry

We define a performance measure, which we call WaM, on two sets of images by using Inception-v3 (or another classifier) to featurize the images, estimate two GMMs, and use the restricted 2-Wasserstein distance to compare the GMMs.

1 code implementation • 6 Oct 2021 • Fernando Gama, Nicolas Zilberstein, Richard G. Baraniuk, Santiago Segarra

Particle filtering is used to compute good nonlinear estimates of complex systems.

no code implementations • 9 Sep 2021 • Zichao Wang, Andrew S. Lan, Richard G. Baraniuk

We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario.

no code implementations • 6 Sep 2021 • Yehuda Dar, Vidya Muthukumar, Richard G. Baraniuk

The rapid recent progress in machine learning (ML) has raised a number of scientific questions that challenge the longstanding dogma of the field.

1 code implementation • 14 Jun 2021 • Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk

Among the most successful methods for sparsifying deep (neural) networks are those that adaptively mask the network weights throughout training.

1 code implementation • 15 Apr 2021 • Shashank Sonkar, Arzoo Katiyar, Richard G. Baraniuk

Knowledge graphs link entities through relations to provide a structured representation of real world facts.

Ranked #9 on Link Prediction on FB15k-237

1 code implementation • 15 Mar 2021 • Pavan K. Kota, Daniel LeJeune, Rebekah A. Drezek, Richard G. Baraniuk

Here, we present the first exploration of the MMV problem where signals are independently drawn from a sparse, multivariate Poisson distribution.

no code implementations • 9 Mar 2021 • Yehuda Dar, Richard G. Baraniuk

We study a fundamental transfer learning process from source to target linear regression tasks, including overparameterized settings where there are more learned parameters than data samples.

1 code implementation • 27 Oct 2020 • Sina AlEMohammad, Hossein Babaei, Randall Balestriero, Matt Y. Cheung, Ahmed Imtiaz Humayun, Daniel LeJeune, Naiming Liu, Lorenzo Luzi, Jasper Tan, Zichao Wang, Richard G. Baraniuk

High dimensionality poses many challenges to the use of data, from visualization and interpretation, to prediction and storage for historical preservation.

no code implementations • 23 Jul 2020 • Zichao Wang, Angus Lamb, Evgeny Saveliev, Pashmina Cameron, Yordan Zaykov, José Miguel Hernández-Lobato, Richard E. Turner, Richard G. Baraniuk, Craig Barton, Simon Peyton Jones, Simon Woodhead, Cheng Zhang

In this competition, participants will focus on the students' answer records to these multiple-choice diagnostic questions, with the aim of 1) accurately predicting which answers the students provide; 2) accurately predicting which questions have high quality; and 3) determining a personalized sequence of questions for each student that best predicts the student's answers.

no code implementations • 25 Jun 2020 • Lorenzo Luzi, Randall Balestriero, Richard G. Baraniuk

They can be represented in two ways: With an ensemble of networks or with a single network with truncated latent space.

no code implementations • NeurIPS 2020 • Randall Balestriero, Sebastien Paris, Richard G. Baraniuk

Deep Generative Networks (DGNs) with probabilistic modeling of their output and latent space are currently trained via Variational Autoencoders (VAEs).

no code implementations • 13 Jun 2020 • Randall Balestriero, Herve Glotin, Richard G. Baraniuk

We develop an interpretable and learnable Wigner-Ville distribution that produces a super-resolved quadratic signal representation for time-series analysis.

no code implementations • 12 Jun 2020 • Yehuda Dar, Richard G. Baraniuk

Our non-asymptotic analysis shows that the generalization error of the target task follows a two-dimensional double descent trend (with respect to the number of free parameters in each of the tasks) that is controlled by the transfer learning factors.

no code implementations • 12 Jun 2020 • Weili Nie, Zichao Wang, Ankit B. Patel, Richard G. Baraniuk

Learning interpretable and disentangled representations is a crucial yet challenging task in representation learning.

2 code implementations • NeurIPS 2020 • Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang

Designing deep neural networks is an art that often involves an expensive search over candidate architectures.

no code implementations • COLING 2020 • Shashank Sonkar, Andrew E. Waters, Richard G. Baraniuk

Word embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) models.

no code implementations • 25 May 2020 • Shashank Sonkar, Andrew E. Waters, Andrew S. Lan, Phillip J. Grimaldi, Richard G. Baraniuk

Knowledge tracing (KT) models, e. g., the deep knowledge tracing (DKT) model, track an individual learner's acquisition of skills over time by examining the learner's performance on questions related to those skills.

no code implementations • 12 May 2020 • Gregory Ongie, Ajil Jalal, Christopher A. Metzler, Richard G. Baraniuk, Alexandros G. Dimakis, Rebecca Willett

Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging.

1 code implementation • ICLR 2020 • Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Richard G. Baraniuk, Zhangyang Wang, Yingyan Lin

Finally, we leverage the existence of EB tickets and the proposed mask distance to develop efficient training methods, which are achieved by first identifying EB tickets via low-cost schemes, and then continuing to train merely the EB tickets towards the target accuracy.

no code implementations • 12 Mar 2020 • Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, Jose Miguel Hernandez-Lobato, Simon Peyton Jones, Richard G. Baraniuk, Cheng Zhang

Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students.

no code implementations • ICML 2020 • Yehuda Dar, Paul Mayer, Lorenzo Luzi, Richard G. Baraniuk

We study the linear subspace fitting problem in the overparameterized setting, where the estimated subspace can perfectly interpolate the training examples.

1 code implementation • 24 Feb 2020 • Bao Wang, Tan M. Nguyen, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher

Nesterov accelerated gradient (NAG) improves the convergence rate of gradient descent (GD) for convex optimization using a specially designed momentum; however, it accumulates error when an inexact gradient is used (such as in SGD), slowing convergence at best and diverging at worst.

no code implementations • 9 Dec 2019 • Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk, Anima Anandkumar

Continuous Normalizing Flows (CNFs) have emerged as promising deep generative models for a wide range of tasks thanks to their invertibility and exact likelihood estimation.

1 code implementation • 10 Oct 2019 • Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk

Ensemble methods that average over a collection of independent predictors that are each limited to a subsampling of both the examples and features of the training data command a significant presence in machine learning, such as the ever-popular random forest, yet the nature of the subsampling effect, particularly of the features, is not well understood.

1 code implementation • 26 Sep 2019 • Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Richard G. Baraniuk, Zhangyang Wang, Yingyan Lin

In this paper, we discover for the first time that the winning tickets can be identified at the very early training stage, which we term as early-bird (EB) tickets, via low-cost training schemes (e. g., early stopping and low-precision training) at large learning rates.

no code implementations • NeurIPS 2019 Workshop Neuro AI 2019 • Yujia Huang, Sihui Dai, Tan Nguyen, Pinglei Bao, Doris Y. Tsao, Richard G. Baraniuk, Anima Anandkumar

Primates have a remarkable ability to correctly classify images even in the presence of significant noise and degradation.

no code implementations • 10 Jul 2019 • Yujia Huang, Sihui Dai, Tan Nguyen, Richard G. Baraniuk, Anima Anandkumar

Our results show that when trained on CIFAR-10, lower likelihood (of latent variables) is assigned to SVHN images.

no code implementations • 28 May 2019 • Daniel LeJeune, Randall Balestriero, Hamid Javadi, Richard G. Baraniuk

Deep (neural) networks have been applied productively in a wide range of supervised and unsupervised learning tasks.

1 code implementation • 22 May 2019 • Daniel LeJeune, Gautam Dasarathy, Richard G. Baraniuk

The main goal is to efficiently identify a subset of arms in a multi-armed bandit problem whose means are above a specified threshold.

no code implementations • 21 May 2019 • Indu Manickam, Andrew S. Lan, Gautam Dasarathy, Richard G. Baraniuk

We apply this framework to the last two months of the election period for a group of 47508 Twitter users and demonstrate that both liberal and conservative users became more polarized over time.

no code implementations • ICLR 2019 • Nhat Ho, Tan Nguyen, Ankit B. Patel, Anima Anandkumar, Michael. I. Jordan, Richard G. Baraniuk

The conjugate prior yields a new regularizer for learning based on the paths rendered in the generative model for training CNNs–the Rendering Path Normalization (RPN).

no code implementations • ICLR 2019 • Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk

In this paper, we focus on two challenges which offset the promise of sparse signal representation, sensing, and recovery.

no code implementations • 27 Feb 2019 • Joshua J. Michalenko, Ameesh Shah, Abhinav Verma, Richard G. Baraniuk, Swarat Chaudhuri, Ankit B. Patel

We investigate the internal representations that a recurrent neural network (RNN) uses while learning to recognize a regular formal language.

1 code implementation • 25 Feb 2019 • Daniel LeJeune, Richard G. Baraniuk, Reinhard Heckel

Algorithms often carry out equally many computations for "easy" and "hard" problem instances.

no code implementations • 18 Feb 2019 • Benjamin Coleman, Richard G. Baraniuk, Anshumali Shrivastava

We present the first sublinear memory sketch that can be queried to find the nearest neighbors in a dataset.

no code implementations • 1 Nov 2018 • Tan Nguyen, Nhat Ho, Ankit Patel, Anima Anandkumar, Michael. I. Jordan, Richard G. Baraniuk

This conjugate prior yields a new regularizer based on paths rendered in the generative model for training CNNs-the Rendering Path Normalization (RPN).

no code implementations • ICLR 2019 • Randall Balestriero, Richard G. Baraniuk

We show that, under a GMM, piecewise affine, convex nonlinearities like ReLU, absolute value, and max-pooling can be interpreted as solutions to certain natural "hard" VQ inference problems, while sigmoid, hyperbolic tangent, and softmax can be interpreted as solutions to corresponding "soft" VQ inference problems.

1 code implementation • 12 Jun 2018 • Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk

We demonstrate that MISSION accurately and efficiently performs feature selection on real-world, large-scale datasets with billions of dimensions.

1 code implementation • 26 May 2018 • Christopher A. Metzler, Ali Mousavi, Reinhard Heckel, Richard G. Baraniuk

We show that, in the context of image recovery, SURE and its generalizations can be used to train convolutional neural networks (CNNs) for a range of image denoising and recovery problems without any ground truth data.

1 code implementation • ICML 2018 • Christopher A. Metzler, Philip Schniter, Ashok Veeraraghavan, Richard G. Baraniuk

Phase retrieval algorithms have become an important component in many modern computational imaging systems.

no code implementations • 12 Nov 2017 • Randall Balestriero, Vincent Roger, Herve G. Glotin, Richard G. Baraniuk

We exploit a recently derived inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework that applies to a wide range of systems and problems.

no code implementations • 11 Jul 2017 • Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk

In this paper we develop a novel computational sensing framework for sensing and recovering structured signals.

1 code implementation • NeurIPS 2017 • Christopher A. Metzler, Ali Mousavi, Richard G. Baraniuk

The LDAMP network is easy to train, can be applied to a variety of different measurement matrices, and comes with a state-evolution heuristic that accurately predicts its performance.

no code implementations • 24 Mar 2017 • Joshua J. Michalenko, Andrew S. Lan, Richard G. Baraniuk

An important, yet largely unstudied, problem in student data analysis is to detect misconceptions from students' responses to open-response questions.

2 code implementations • 14 Jan 2017 • Ali Mousavi, Richard G. Baraniuk

The promise of compressive sensing (CS) has been offset by two significant challenges.

no code implementations • 6 Dec 2016 • Tan Nguyen, Wanjia Liu, Ethan Perez, Richard G. Baraniuk, Ankit B. Patel

Semi-supervised learning algorithms reduce the high cost of acquiring labeled training data by using both labeled and unlabeled data during learning.

no code implementations • NeurIPS 2016 • Ankit B. Patel, Tan Nguyen, Richard G. Baraniuk

We develop a probabilistic framework for deep learning based on the Deep Rendering Mixture Model (DRMM), a new generative probabilistic model that explicitly capture variations in data due to latent task nuisance variables.

no code implementations • 3 Nov 2015 • Ali Mousavi, Arian Maleki, Richard G. Baraniuk

For instance the following basic questions have not yet been studied in the literature: (i) How does the size of the active set $\|\hat{\beta}^\lambda\|_0/p$ behave as a function of $\lambda$?

no code implementations • 17 Aug 2015 • Ali Mousavi, Ankit B. Patel, Richard G. Baraniuk

In this paper, we develop a new framework for sensing and recovering structured signals.

no code implementations • 19 May 2015 • Raajen Patel, Thomas A. Goldstein, Eva L. Dyer, Azalia Mirhoseini, Richard G. Baraniuk

Kernel matrices (e. g. Gram or similarity matrices) are essential for many state-of-the-art approaches to classification, clustering, and dimensionality reduction.

no code implementations • 4 May 2015 • Eva L. Dyer, Tom A. Goldstein, Raajen Patel, Konrad P. Kording, Richard G. Baraniuk

Classical approaches discover such structure by learning a basis that can efficiently express the collection.

no code implementations • 2 Apr 2015 • Ankit B. Patel, Tan Nguyen, Richard G. Baraniuk

A grand challenge in machine learning is the development of computational algorithms that match or outperform humans in perceptual inference tasks that are complicated by nuisance variation.

1 code implementation • 27 Mar 2015 • Azalia Mirhoseini, Eva L. Dyer, Ebrahim. M. Songhori, Richard G. Baraniuk, Farinaz Koushanfar

This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense datasets.

no code implementations • 9 Mar 2015 • Aswin C. Sankaranarayanan, Lina Xu, Christoph Studer, Yun Li, Kevin Kelly, Richard G. Baraniuk

In this paper, we propose the CS multi-scale video (CS-MUVI) sensing and recovery framework for high-quality video acquisition and recovery using SMCs.

no code implementations • 18 Jan 2015 • Andrew S. Lan, Divyanshu Vats, Andrew E. Waters, Richard G. Baraniuk

Our data-driven framework for mathematical language processing (MLP) leverages solution data from a large number of learners to evaluate the correctness of their solutions, assign partial-credit scores, and provide feedback to each learner on the likely locations of any errors.

no code implementations • 12 Jan 2015 • Ryan Ning, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk

In this work, we propose a novel methodology for unordered categorical IRT that we call SPRITE (short for stochastic polytomous response item model) that: (i) analyzes both ordered and unordered categories, (ii) offers interpretable outputs, and (iii) provides improved data fitting compared to existing models.

no code implementations • 18 Dec 2014 • Andrew S. Lan, Christoph Studer, Richard G. Baraniuk

The recently proposed SPARse Factor Analysis (SPARFA) framework for personalized learning performs factor analysis on ordinal or binary-valued (e. g., correct/incorrect) graded learner responses to questions.

no code implementations • 18 Dec 2014 • Andrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk

SPARse Factor Analysis (SPARFA) is a novel framework for machine learning-based learning analytics, which estimates a learner's knowledge of the concepts underlying a domain, and content analytics, which estimates the relationships among a collection of questions and those concepts.

no code implementations • 5 Aug 2014 • Eric C. Chi, Genevera I. Allen, Richard G. Baraniuk

In the biclustering problem, we seek to simultaneously group observations and features.

2 code implementations • 16 Jun 2014 • Christopher A. Metzler, Arian Maleki, Richard G. Baraniuk

A key element in D-AMP is the use of an appropriate Onsager correction term in its iterations, which coerces the signal perturbation at each iteration to be very close to the white Gaussian noise that denoisers are typically designed to remove.

no code implementations • 15 Apr 2014 • Jianing V. Shi, Yangyang Xu, Richard G. Baraniuk

In this paper, we introduce the concept of sparse bilinear logistic regression for decision problems involving explanatory variables that are two-dimensional matrices.

no code implementations • 13 Apr 2014 • Divyanshu Vats, Robert D. Nowak, Richard G. Baraniuk

This paper studies graphical model selection, i. e., the problem of estimating a graph of statistical relationships among a collection of random variables.

no code implementations • 23 Feb 2014 • Divyanshu Vats, Richard G. Baraniuk

In this paper, we address the challenging problem of selecting tuning parameters for high-dimensional sparse regression.

no code implementations • 30 Jan 2014 • Jianing V. Shi, Wotao Yin, Aswin C. Sankaranarayanan, Richard G. Baraniuk

We apply this framework to accelerate the acquisition process of dynamic MRI and show it achieves the best reconstruction accuracy with the least computational time compared with existing algorithms in the literature.

no code implementations • 19 Dec 2013 • Andrew S. Lan, Christoph Studer, Richard G. Baraniuk

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications.

no code implementations • 5 Dec 2013 • Divyanshu Vats, Richard G. Baraniuk

We consider the high-dimensional sparse linear regression problem of accurately estimating a sparse vector using a small number of linear measurements that are contaminated by noise.

no code implementations • 31 Oct 2013 • Ali Mousavi, Arian Maleki, Richard G. Baraniuk

In particular, both the final reconstruction error and the convergence rate of the algorithm crucially rely on how the threshold parameter is set at each step of the algorithm.

no code implementations • 23 Sep 2013 • Ali Mousavi, Arian Maleki, Richard G. Baraniuk

This paper concerns the performance of the LASSO (also knows as basis pursuit denoising) for recovering sparse signals from undersampled, randomized, noisy measurements.

no code implementations • 8 May 2013 • Andrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk

In order to better interpret the estimated latent concepts, SPARFA relies on a post-processing step that utilizes user-defined tags (e. g., topics or keywords) available for each question.

no code implementations • 22 Mar 2013 • Andrew S. Lan, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk

We estimate these factors given the graded responses to a collection of questions.

no code implementations • 19 Mar 2013 • Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk

To learn a union of subspaces from a collection of data, sets of signals in the collection that belong to the same subspace must be identified in order to obtain accurate estimates of the subspace structures present in the data.

no code implementations • 23 Jan 2012 • Aswin C. Sankaranarayanan, Pavan K Turaga, Rama Chellappa, Richard G. Baraniuk

Compressive sensing (CS) is a new approach for the acquisition and recovery of sparse signals and images that enables sampling rates significantly below the classical Nyquist rate.

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