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ORCIDRichard G. Baraniuk
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- affiliation: Rice University, Houston, TX, USA
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2020 – today
- 2021
[j108]Nathan Dunkelberger, Jennifer L. Sullivan, Joshua Bradley, Indu Manickam, Gautam Dasarathy, Richard G. Baraniuk, Marcia K. O'Malley:
A Multisensory Approach to Present Phonemes as Language Through a Wearable Haptic Device. IEEE Trans. Haptics 14(1): 188-199 (2021)- [c195]Tianyi Yao, Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk, Genevera I. Allen:
Minipatch Learning as Implicit Ridge-Like Regularization. BigComp 2021: 65-68 - [i110]Randall Balestriero, Haoran You, Zhihan Lu, Yutong Kou, Yingyan Lin, Richard G. Baraniuk:
Max-Affine Spline Insights Into Deep Network Pruning. CoRR abs/2101.02338 (2021) - [i109]Yehuda Dar, Richard G. Baraniuk:
Transfer Learning Can Outperform the True Prior in Double Descent Regularization. CoRR abs/2103.05621 (2021) - [i108]Randall Balestriero, Richard G. Baraniuk:
Fast Jacobian-Vector Product for Deep Networks. CoRR abs/2104.00219 (2021) - [i107]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:
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR abs/2104.04034 (2021) - [i106]Shashank Sonkar, Arzoo Katiyar, Richard G. Baraniuk:
NePTuNe: Neural Powered Tucker Network for Knowledge Graph Completion. CoRR abs/2104.07824 (2021) - 2020
- [j107]Richard G. Baraniuk, Alex Dimakis
, Negar Kiyavash, Sewoong Oh, Rebecca Willett:
Guest Editorial. IEEE J. Sel. Areas Inf. Theory 1(1): 4 (2020) - [j106]Gregory Ongie, Ajil Jalal, Christopher A. Metzler, Richard G. Baraniuk, Alexandros G. Dimakis, Rebecca Willett:
Deep Learning Techniques for Inverse Problems in Imaging. IEEE J. Sel. Areas Inf. Theory 1(1): 39-56 (2020) - [j105]Yue Wang, Jianghao Shen, Ting-Kuei Hu, Pengfei Xu, Tan M. Nguyen, Richard G. Baraniuk, Zhangyang Wang, Yingyan Lin:
Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference. IEEE J. Sel. Top. Signal Process. 14(4): 623-633 (2020) - [j104]Richard G. Baraniuk, David L. Donoho, Matan Gavish:
The science of deep learning. Proc. Natl. Acad. Sci. USA 117(48): 30029-30032 (2020) - [j103]Romain Cosentino, Randall Balestriero, Richard G. Baraniuk, Behnaam Aazhang:
Universal Frame Thresholding. IEEE Signal Process. Lett. 27: 1115-1119 (2020) - [j102]Manoj Kumar Sharma, Christopher A. Metzler, Sudarshan Nagesh, Richard G. Baraniuk, Oliver Cossairt, Ashok Veeraraghavan:
Inverse Scattering via Transmission Matrices: Broadband Illumination and Fast Phase Retrieval Algorithms. IEEE Trans. Computational Imaging 6: 95-108 (2020) - [c194]David Kim, Adam Winchell, Andrew E. Waters, Phillip Grimaldi, Richard G. Baraniuk, Michael Mozer:
Inferring Student Comprehension from Highlighting Patterns in Digital Textbooks: An Exploration in an Authentic Learning Platform. iTextbooks@AIED 2020: 67-79 - [c193]Daniel LeJeune, Gautam Dasarathy, Richard G. Baraniuk:
Thresholding Graph Bandits with GrAPL. AISTATS 2020: 2476-2485 - [c192]Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk:
The Implicit Regularization of Ordinary Least Squares Ensembles. AISTATS 2020: 3525-3535 - [c191]Shashank Sonkar, Andrew E. Waters, Richard G. Baraniuk:
Attention Word Embedding. COLING 2020: 6894-6902 - [c190]Zichao Wang, Yi Gu, Andrew S. Lan, Richard G. Baraniuk:
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics. EDM 2020 - [c189]Shashank Sonkar, Andrew S. Lan, Andrew E. Waters, Phillip Grimaldi, Richard G. Baraniuk:
qDKT: Question-centric Deep Knowledge Tracing. EDM 2020 - [c188]Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Richard G. Baraniuk, Zhangyang Wang, Yingyan Lin:
Drawing Early-Bird Tickets: Toward More Efficient Training of Deep Networks. ICLR 2020 - [c187]Jasper Tan, Salman Siddique Khan, Vivek Boominathan, Jeffrey Byrne, Richard G. Baraniuk, Kaushik Mitra, Ashok Veeraraghavan:
CANOPIC: Pre-Digital Privacy-Enhancing Encodings for Computer Vision. ICME 2020: 1-6 - [c186]Benjamin Coleman, Richard G. Baraniuk, Anshumali Shrivastava:
Sub-linear Memory Sketches for Near Neighbor Search on Streaming Data. ICML 2020: 2089-2099 - [c185]Yehuda Dar, Paul Mayer, Lorenzo Luzi, Richard G. Baraniuk:
Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors. ICML 2020: 2366-2375 - [c184]Randall Balestriero, Sebastien Paris, Richard G. Baraniuk:
Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks. NeurIPS 2020 - [c183]Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
MomentumRNN: Integrating Momentum into Recurrent Neural Networks. NeurIPS 2020 - [e2]Sergey A. Sosnovsky, Peter Brusilovsky, Richard G. Baraniuk, Andrew S. Lan:
Proceedings of the Second International Workshop on Intelligent Textbooks 2020 co-located with 21st International Conference on Artificial Intelligence in Education (AIED 2020), Online, July 06, 2020. CEUR Workshop Proceedings 2674, CEUR-WS.org 2020 [contents] - [i105]Bao Wang, Tan M. Nguyen, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher:
Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent. CoRR abs/2002.10583 (2020) - [i104]Yehuda Dar, Paul Mayer, Lorenzo Luzi, Richard G. Baraniuk:
Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors. CoRR abs/2002.10614 (2020) - [i103]Randall Balestriero, Sebastien Paris, Richard G. Baraniuk:
Max-Affine Spline Insights into Deep Generative Networks. CoRR abs/2002.11912 (2020) - [i102]Gregory Ongie, Ajil Jalal, Christopher A. Metzler, Richard G. Baraniuk, Alexandros G. Dimakis, Rebecca Willett:
Deep Learning Techniques for Inverse Problems in Imaging. CoRR abs/2005.06001 (2020) - [i101]Shashank Sonkar, Andrew E. Waters, Andrew S. Lan, Phillip J. Grimaldi, Richard G. Baraniuk:
qDKT: Question-centric Deep Knowledge Tracing. CoRR abs/2005.12442 (2020) - [i100]Jack Zichao Wang, Yi Gu, Andrew S. Lan, Richard G. Baraniuk:
VarFA: A Variational Factor Analysis Framework For Efficient Bayesian Learning Analytics. CoRR abs/2005.13107 (2020) - [i99]Shashank Sonkar, Andrew E. Waters, Richard G. Baraniuk:
Attention Word Embedding. CoRR abs/2006.00988 (2020) - [i98]Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang:
MomentumRNN: Integrating Momentum into Recurrent Neural Networks. CoRR abs/2006.06919 (2020) - [i97]Yehuda Dar, Richard G. Baraniuk:
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks. CoRR abs/2006.07002 (2020) - [i96]Weili Nie, Zichao Wang, Ankit B. Patel, Richard G. Baraniuk:
An Improved Semi-Supervised VAE for Learning Disentangled Representations. CoRR abs/2006.07460 (2020) - [i95]Randall Balestriero, Hervé Glotin, Richard G. Baraniuk:
Interpretable Super-Resolution via a Learned Time-Series Representation. CoRR abs/2006.07713 (2020) - [i94]Randall Balestriero, Sebastien Paris, Richard G. Baraniuk:
Analytical Probability Distributions and EM-Learning for Deep Generative Networks. CoRR abs/2006.10023 (2020) - [i93]Sina Alemohammad, Zichao Wang, Randall Balestriero, Richard G. Baraniuk:
The Recurrent Neural Tangent Kernel. CoRR abs/2006.10246 (2020) - [i92]Lorenzo Luzi, Randall Balestriero, Richard G. Baraniuk:
Ensembles of Generative Adversarial Networks for Disconnected Data. CoRR abs/2006.14600 (2020) - [i91]Rajeev Alur, Richard G. Baraniuk, Rastislav Bodík, Ann W. Drobnis, Sumit Gulwani, Bjoern Hartmann, Yasmin B. Kafai, Jeff Karpicke, Ran Libeskind-Hadas, Debra J. Richardson, Armando Solar-Lezama, Candace Thille, Moshe Y. Vardi:
Computer-Aided Personalized Education. CoRR abs/2007.03704 (2020) - [i90]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:
Diagnostic Questions: The NeurIPS 2020 Education Challenge. CoRR abs/2007.12061 (2020) - [i89]Romain Cosentino, Randall Balestriero, Richard G. Baraniuk, Behnaam Aazhang:
Provable Finite Data Generalization with Group Autoencoder. CoRR abs/2009.09525 (2020) - [i88]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:
Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels. CoRR abs/2010.13975 (2020) - [i87]Sina Alemohammad, Randall Balestriero, Zichao Wang, Richard G. Baraniuk:
Scalable Neural Tangent Kernel of Recurrent Architectures. CoRR abs/2012.04859 (2020) - [i86]Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan M. Sengupta, Richard G. Baraniuk, Behnaam Aazhang:
Interpretable Image Clustering via Diffeomorphism-Aware K-Means. CoRR abs/2012.09743 (2020) - [i85]Vishwanath Saragadam, Michael DeZeeuw, Richard G. Baraniuk, Ashok Veeraraghavan, Aswin C. Sankaranarayanan:
SASSI - Super-Pixelated Adaptive Spatio-Spectral Imaging. CoRR abs/2012.14495 (2020)
2010 – 2019
- 2019
- [j101]Jasper Tan, Li Niu, Jesse K. Adams, Vivek Boominathan, Jacob T. Robinson, Richard G. Baraniuk, Ashok Veeraraghavan:
Face Detection and Verification Using Lensless Cameras. IEEE Trans. Computational Imaging 5(2): 180-194 (2019) - [c182]Daniel LeJeune, Reinhard Heckel, Richard G. Baraniuk:
Adaptive Estimation for Approximate $k$-Nearest-Neighbor Computations. AISTATS 2019: 3099-3107 - [c181]Indu Manickam, Andrew S. Lan, Gautam Dasarathy, Richard G. Baraniuk:
IdeoTrace: a framework for ideology tracing with a case study on the 2016 U.S. presidential election. ASONAM 2019: 274-281 - [c180]Zichao Wang, Andrew S. Lan, Andrew E. Waters, Phillip Grimaldi, Richard G. Baraniuk:
A Meta-Learning Augmented Bidirectional Transformer Model for Automatic Short Answer Grading. EDM 2019 - [c179]Randall Balestriero, Richard G. Baraniuk:
From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference. ICLR (Poster) 2019 - [c178]Joshua J. Michalenko, Ameesh Shah, Abhinav Verma, Richard G. Baraniuk, Swarat Chaudhuri, Ankit B. Patel:
Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks. ICLR (Poster) 2019 - [c177]Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk:
A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery. ICLR (Poster) 2019 - [c176]Zichao Wang, Randall Balestriero, Richard G. Baraniuk:
A Max-Affine Spline Perspective of Recurrent Neural Networks. ICLR (Poster) 2019 - [c175]Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard G. Baraniuk:
The Geometry of Deep Networks: Power Diagram Subdivision. NeurIPS 2019: 15806-15815 - [e1]Sergey A. Sosnovsky, Peter Brusilovsky, Richard G. Baraniuk, Rakesh Agrawal, Andrew S. Lan:
Proceedings of the First Workshop on Intelligent Textbooks co-located with 20th International Conference on Artificial Intelligence in Education (AIED 2019), Chicago, IL, USA, June 25, 2019. CEUR Workshop Proceedings 2384, CEUR-WS.org 2019 [contents] - [i84]Benjamin Coleman, Anshumali Shrivastava, Richard G. Baraniuk:
RACE: Sub-Linear Memory Sketches for Approximate Near-Neighbor Search on Streaming Data. CoRR abs/1902.06687 (2019) - [i83]Daniel LeJeune, Richard G. Baraniuk, Reinhard Heckel:
Adaptive Estimation for Approximate k-Nearest-Neighbor Computations. CoRR abs/1902.09465 (2019) - [i82]Joshua J. Michalenko, Ameesh Shah, Abhinav Verma, Richard G. Baraniuk, Swarat Chaudhuri, Ankit B. Patel:
Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks. CoRR abs/1902.10297 (2019) - [i81]Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard G. Baraniuk:
The Geometry of Deep Networks: Power Diagram Subdivision. CoRR abs/1905.08443 (2019) - [i80]Indu Manickam, Andrew S. Lan, Gautam Dasarathy, Richard G. Baraniuk:
IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election. CoRR abs/1905.08831 (2019) - [i79]Daniel LeJeune, Gautam Dasarathy, Richard G. Baraniuk:
Thresholding Graph Bandits with GrAPL. CoRR abs/1905.09190 (2019) - [i78]Hamid Javadi, Randall Balestriero, Richard G. Baraniuk:
A Hessian Based Complexity Measure for Deep Networks. CoRR abs/1905.11639 (2019) - [i77]Yue Wang, Jianghao Shen, Ting-Kuei Hu, Pengfei Xu, Tan M. Nguyen, Richard G. Baraniuk, Zhangyang Wang, Yingyan Lin:
Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference. CoRR abs/1907.04523 (2019) - [i76]Yujia Huang, Sihui Dai, Tan M. Nguyen, Richard G. Baraniuk, Anima Anandkumar:
Out-of-Distribution Detection Using Neural Rendering Generative Models. CoRR abs/1907.04572 (2019) - [i75]Haoran You, Chaojian Li, Pengfei Xu, Yonggan Fu, Yue Wang, Xiaohan Chen, Yingyan Lin, Zhangyang Wang, Richard G. Baraniuk:
Drawing early-bird tickets: Towards more efficient training of deep networks. CoRR abs/1909.11957 (2019) - [i74]Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk:
The Implicit Regularization of Ordinary Least Squares Ensembles. CoRR abs/1910.04743 (2019) - [i73]Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk, Anima Anandkumar:
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers. CoRR abs/1912.03978 (2019) - 2018
- [j100]Amirali Aghazadeh, Mohammad Golbabaee, Andrew S. Lan, Richard G. Baraniuk:
Insense: Incoherent sensor selection for sparse signals. Signal Process. 150: 57-65 (2018) - [j99]Azalia Mirhoseini, Eva L. Dyer, Ebrahim M. Songhori, Richard G. Baraniuk, Farinaz Koushanfar:
RankMap: A Framework for Distributed Learning From Dense Data Sets. IEEE Trans. Neural Networks Learn. Syst. 29(7): 2717-2730 (2018) - [c174]Nathan Dunkelberger, Jenny Sullivan, Joshua Bradley, Nickolas P. Walling, Indu Manickam, Gautam Dasarathy, Ali Israr, Frances W. Y. Lau, Keith Klumb, Brian Knott, Freddy Abnousi, Richard G. Baraniuk, Marcia K. O'Malley:
Conveying language through haptics: a multi-sensory approach. UbiComp 2018: 25-32 - [c173]Christopher A. Metzler, Philip Schniter, Richard G. Baraniuk:
An Expectation-Maximization Approach to Tuning Generalized Vector Approximate Message Passing. LVA/ICA 2018: 395-406 - [c172]Amirali Aghazadeh, Mohammad Golbabaee, Andrew S. Lan, Richard G. Baraniuk:
Insense: Incoherent Sensor Selection for Sparse Signals. ICASSP 2018: 4689-4693 - [c171]Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk:
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches. ICML 2018: 80-88 - [c170]Randall Balestriero, Romain Cosentino, Hervé Glotin, Richard G. Baraniuk:
Spline Filters For End-to-End Deep Learning. ICML 2018: 373-382 - [c169]Randall Balestriero, Richard G. Baraniuk:
A Spline Theory of Deep Networks. ICML 2018: 383-392 - [c168]Christopher A. Metzler, Philip Schniter, Ashok Veeraraghavan, Richard G. Baraniuk:
prDeep: Robust Phase Retrieval with a Flexible Deep Network. ICML 2018: 3498-3507 - [c167]Zichao Wang, Andrew S. Lan, Weili Nie, Andrew E. Waters, Phillip J. Grimaldi, Richard G. Baraniuk:
QG-net: a data-driven question generation model for educational content. L@S 2018: 7:1-7:10 - [i72]Randall Balestriero, Hervé Glotin, Richard G. Baraniuk:
Semi-Supervised Learning Enabled by Multiscale Deep Neural Network Inversion. CoRR abs/1802.10172 (2018) - [i71]Christopher A. Metzler, Philip Schniter, Ashok Veeraraghavan, Richard G. Baraniuk:
prDeep: Robust Phase Retrieval with Flexible Deep Neural Networks. CoRR abs/1803.00212 (2018) - [i70]Randall Balestriero, Richard G. Baraniuk:
A Spline Theory of Deep Networks (Extended Version). CoRR abs/1805.06576 (2018) - [i69]Christopher A. Metzler, Ali Mousavi, Reinhard Heckel, Richard G. Baraniuk:
Unsupervised Learning with Stein's Unbiased Risk Estimator. CoRR abs/1805.10531 (2018) - [i68]Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk:
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches. CoRR abs/1806.04310 (2018) - [i67]Christopher A. Metzler, Philip Schniter, Richard G. Baraniuk:
An Expectation-Maximization Approach to Tuning Generalized Vector Approximate Message Passing. CoRR abs/1806.10079 (2018) - [i66]Randall Balestriero, Richard G. Baraniuk:
From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference. CoRR abs/1810.09274 (2018) - [i65]Nhat Ho, Tan M. Nguyen, Ankit B. Patel, Anima Anandkumar, Michael I. Jordan, Richard G. Baraniuk:
Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning. CoRR abs/1811.02657 (2018) - 2017
- [j98]Mihaela van der Schaar, Richard G. Baraniuk, Mung Chiang, Jonathan Huang, Shengdong Zhao:
Introduction to the Issue on Signal Processing and Machine Learning. IEEE J. Sel. Top. Signal Process. 11(5): 713-715 (2017) - [j97]Andrew S. Lan, Andrew E. Waters, Christoph Studer, Richard G. Baraniuk:
BLAh: Boolean Logic Analysis for Graded Student Response Data. IEEE J. Sel. Top. Signal Process. 11(5): 754-764 (2017) - [j96]Richard G. Baraniuk, Thomas Goldstein, Aswin C. Sankaranarayanan, Christoph Studer, Ashok Veeraraghavan, Michael B. Wakin:
Compressive Video Sensing: Algorithms, architectures, and applications. IEEE Signal Process. Mag. 34(1): 52-66 (2017) - [j95]M. Salman Asif, Ali Ayremlou, Aswin C. Sankaranarayanan, Ashok Veeraraghavan, Richard G. Baraniuk:
FlatCam: Thin, Lensless Cameras Using Coded Aperture and Computation. IEEE Trans. Computational Imaging 3(3): 384-397 (2017) - [j94]Richard G. Baraniuk, Simon Foucart, Deanna Needell, Yaniv Plan, Mary Wootters:
Exponential Decay of Reconstruction Error From Binary Measurements of Sparse Signals. IEEE Trans. Inf. Theory 63(6): 3368-3385 (2017) - [c166]Gautam Dasarathy, Parikshit Shah, Richard G. Baraniuk:
Sketched covariance testing: A compression-statistics tradeoff. ACSSC 2017: 676-680 - [c165]Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk:
DeepCodec: Adaptive sensing and recovery via deep convolutional neural networks. Allerton 2017: 744 - [c164]Joshua J. Michalenko, Andrew S. Lan, Richard G. Baraniuk:
Personalized Feedback for Open-Response Mathematical Questions using Long Short-Term Memory Networks. EDM 2017 - [c163]Joshua J. Michalenko, Andrew S. Lan, Andrew E. Waters, Phillip Grimaldi, Richard G. Baraniuk:
Data-Mining Textual Responses to Uncover Misconception Patterns. EDM 2017 - [c162]Jack Z. Wang, Andrew S. Lan, Phillip Grimaldi, Richard G. Baraniuk:
A Latent Factor Model For Instructor Content Preference Analysis. EDM 2017 - [c161]Andrew E. Waters, Phillip Grimaldi, Andrew S. Lan, Richard G. Baraniuk:
Short-Answer Responses to STEM Exercises: Measuring Response Validity and Its Impact on Learning. EDM 2017 - [c160]Ali Mousavi, Richard G. Baraniuk:
Learning to invert: Signal recovery via Deep Convolutional Networks. ICASSP 2017: 2272-2276 - [c159]Indu Manickam, Andrew S. Lan, Richard G. Baraniuk:
Contextual multi-armed bandit algorithms for personalized learning action selection. ICASSP 2017: 6344-6348 - [c158]Jasper Tan, Vivek Boominathan, Ashok Veeraraghavan, Richard G. Baraniuk:
Flat focus: depth of field analysis for the FlatCam lensless imaging system. ICASSP 2017: 6473-6477 - [c157]Christopher A. Metzler, Manoj Kumar Sharma, Sudarshan Nagesh, Richard G. Baraniuk, Oliver Cossairt, Ashok Veeraraghavan:
Coherent inverse scattering via transmission matrices: Efficient phase retrieval algorithms and a public dataset. ICCP 2017: 51-66 - [c156]Amirali Aghazadeh, Andrew S. Lan, Anshumali Shrivastava, Richard G. Baraniuk:
RHash: Robust Hashing via L_infinity-norm Distortion. IJCAI 2017: 1386-1394 - [c155]Gautam Dasarathy, Parikshit Shah, Richard G. Baraniuk:
Sketched covariance testing: A compression-statistics tradeoff. ISIT 2017: 2268-2272 - [c154]Joshua J. Michalenko, Andrew S. Lan, Richard G. Baraniuk:
D.TRUMP: Data-mining Textual Responses to Uncover Misconception Patterns. L@S 2017: 245-248 - [c153]Christopher A. Metzler, Ali Mousavi, Richard G. Baraniuk:
Learned D-AMP: Principled Neural Network based Compressive Image Recovery. NIPS 2017: 1772-1783 - [i64]Ali Mousavi, Richard G. Baraniuk:
Learning to Invert: Signal Recovery via Deep Convolutional Networks. CoRR abs/1701.03891 (2017) - [i63]Amirali Aghazadeh, Mohammad Golbabaee, Andrew S. Lan, Richard G. Baraniuk:
Insense: Incoherent Sensor Selection for Sparse Signals. CoRR abs/1702.07670 (2017) - [i62]Joshua J. Michalenko, Andrew S. Lan, Richard G. Baraniuk:
Data-Mining Textual Responses to Uncover Misconception Patterns. CoRR abs/1703.08544 (2017) - [i61]Christopher A. Metzler, Ali Mousavi, Richard G. Baraniuk:
Learned D-AMP: A Principled CNN-based Compressive Image Recovery Algorithm. CoRR abs/1704.06625 (2017) - [i60]Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk:
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks. CoRR abs/1707.03386 (2017) - [i59]Randall Balestriero, Richard G. Baraniuk:
Adaptive Partitioning Spline Neural Networks: Template Matching, Memorization, Inhibitor Connections, Inversion, Semi-Sup, Topology Search. CoRR abs/1710.09302 (2017) - [i58]Randall Balestriero, Vincent Roger, Hervé Glotin, Richard G. Baraniuk:
Semi-Supervised Learning via New Deep Network Inversion. CoRR abs/1711.04313 (2017) - [i57]Romain Cosentino, Randall Balestriero, Richard G. Baraniuk, Ankit B. Patel:
Overcomplete Frame Thresholding for Acoustic Scene Analysis. CoRR abs/1712.09117 (2017) - 2016
- [j93]Thomas A. Baran, Richard G. Baraniuk, Alan V. Oppenheim, Paolo Prandoni, Martin Vetterli:
MOOC Adventures in Signal Processing: Bringing DSP to the era of massive open online courses. IEEE Signal Process. Mag. 33(4): 62-83 (2016) - [j92]Amit K. Agrawal, Richard G. Baraniuk, Pablo Favaro, Ashok Veeraraghavan:
Signal Processing for Computational Photography and Displays [From the Guest Editors]. IEEE Signal Process. Mag. 33(5): 12-15 (2016) - [j91]Vivek Boominathan, Jesse K. Adams, M. Salman Asif, Benjamin W. Avants, Jacob T. Robinson, Richard G. Baraniuk, Aswin C. Sankaranarayanan, Ashok Veeraraghavan:
Lensless Imaging: A computational renaissance. IEEE Signal Process. Mag. 33(5): 23-35 (2016) - [j90]Christopher A. Metzler, Arian Maleki, Richard G. Baraniuk:
From Denoising to Compressed Sensing. IEEE Trans. Inf. Theory 62(9): 5117-5144 (2016) - [c152]Divyanshu Vats, Andrew S. Lan, Christoph Studer, Richard G. Baraniuk:
Optimal ranking of test items using the Rasch model. Allerton 2016: 467-473 - [c151]Andrew S. Lan, Richard G. Baraniuk:
A Contextual Bandits Framework for Personalized Learning Action Selection. EDM 2016: 424-429 - [c150]Christopher A. Metzler, Arian Maleki, Richard G. Baraniuk:
BM3D-PRGAMP: Compressive phase retrieval based on BM3D denoising. ICIP 2016: 2504-2508 - [c149]Christopher A. Metzler, Arian Maleki, Richard G. Baraniuk:
BM3D-PRGAMP: Compressive phase retrieval based on BM3D denoising. ICME Workshops 2016: 1-2 - [c148]Andrew S. Lan, Tom Goldstein, Richard G. Baraniuk, Christoph Studer:
Dealbreaker: A Nonlinear Latent Variable Model for Educational Data. ICML 2016: 266-275 - [c147]Ankit B. Patel,