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Wearing a MASK: Compressed Representations of Variable-Length Sequences Using Recurrent Neural Tangent Kernels

Oct 27, 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


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Diagnostic Questions:The NeurIPS 2020 Education Challenge

Aug 03, 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

* 28 pages, 6 figures, NeurIPS 2020 Competition Track 

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Ensembles of Generative Adversarial Networks for Disconnected Data

Jun 25, 2020
Lorenzo Luzi, Randall Balestriero, Richard G. Baraniuk


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An Improved Semi-Supervised VAE for Learning Disentangled Representations

Jun 22, 2020
Weili Nie, Zichao Wang, Ankit B. Patel, Richard G. Baraniuk


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Analytical Probability Distributions and EM-Learning for Deep Generative Networks

Jun 17, 2020
Randall Balestriero, Sebastien Paris, Richard G. Baraniuk


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Interpretable Super-Resolution via a Learned Time-Series Representation

Jun 13, 2020
Randall Balestriero, Herve Glotin, Richard G. Baraniuk


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LaRVAE: Label Replacement VAE for Semi-Supervised Disentanglement Learning

Jun 12, 2020
Weili Nie, Zichao Wang, Ankit B. Patel, Richard G. Baraniuk


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Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks

Jun 12, 2020
Yehuda Dar, Richard G. Baraniuk


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MomentumRNN: Integrating Momentum into Recurrent Neural Networks

Jun 12, 2020
Tan M. Nguyen, Richard G. Baraniuk, Andrea L. Bertozzi, Stanley J. Osher, Bao Wang

* 23 pages, 9 figures 

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Attention Word Embedding

Jun 01, 2020
Shashank Sonkar, Andrew E. Waters, Richard G. Baraniuk


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qDKT: Question-centric Deep Knowledge Tracing

May 25, 2020
Shashank Sonkar, Andrew E. Waters, Andrew S. Lan, Phillip J. Grimaldi, Richard G. Baraniuk


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Deep Learning Techniques for Inverse Problems in Imaging

May 12, 2020
Gregory Ongie, Ajil Jalal, Christopher A. Metzler, Richard G. Baraniuk, Alexandros G. Dimakis, Rebecca Willett


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Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors

Feb 25, 2020
Yehuda Dar, Paul Mayer, Lorenzo Luzi, Richard G. Baraniuk


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Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent

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

* 20 pages, 13 figures, 15 tables 

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InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers

Dec 09, 2019
Tan M. Nguyen, Animesh Garg, Richard G. Baraniuk, Anima Anandkumar

* 17 pages, 14 figures, 2 tables 

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The Implicit Regularization of Ordinary Least Squares Ensembles

Oct 10, 2019
Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk

* 21 pages, 4 figures 

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Drawing early-bird tickets: Towards more efficient training of deep networks

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


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Out-of-Distribution Detection Using Neural Rendering Generative Models

Jul 10, 2019
Yujia Huang, Sihui Dai, Tan Nguyen, Richard G. Baraniuk, Anima Anandkumar


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IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election

May 30, 2019
Indu Manickam, Andrew S. Lan, Gautam Dasarathy, Richard G. Baraniuk

* 9 pages, 4 figures, submitted to ASONAM 2019 

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Thresholding Graph Bandits with GrAPL

May 22, 2019
Daniel LeJeune, Gautam Dasarathy, Richard G. Baraniuk

* 15 pages, 3 figures 

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RACE: Sub-Linear Memory Sketches for Approximate Near-Neighbor Search on Streaming Data

Apr 09, 2019
Benjamin Coleman, Anshumali Shrivastava, Richard G. Baraniuk


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Representing Formal Languages: A Comparison Between Finite Automata and Recurrent Neural Networks

Feb 27, 2019
Joshua J. Michalenko, Ameesh Shah, Abhinav Verma, Richard G. Baraniuk, Swarat Chaudhuri, Ankit B. Patel

* 15 Pages, 13 Figures, Accepted to ICLR 2019 

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Adaptive Estimation for Approximate k-Nearest-Neighbor Computations

Feb 25, 2019
Daniel LeJeune, Richard G. Baraniuk, Reinhard Heckel

* 11 pages, 2 figures. To appear in AISTATS 2019 

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From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference

Oct 22, 2018
Randall Balestriero, Richard G. Baraniuk


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prDeep: Robust Phase Retrieval with a Flexible Deep Network

Jun 29, 2018
Christopher A. Metzler, Philip Schniter, Ashok Veeraraghavan, Richard G. Baraniuk


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MISSION: Ultra Large-Scale Feature Selection using Count-Sketches

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


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Unsupervised Learning with Stein's Unbiased Risk Estimator

May 26, 2018
Christopher A. Metzler, Ali Mousavi, Reinhard Heckel, Richard G. Baraniuk


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