Baraniuk, Richard

46 publications

ICML 2025 Mitigating Over-Exploration in Latent Space Optimization Using Les Omer Ronen, Ahmed Imtiaz Humayun, Richard Baraniuk, Randall Balestriero, Bin Yu

TMLR 2025 SaFARi: State-Space Models for Frame-Agnostic Representation Hossein Babaei, Mel White, Sina Alemohammad, Richard Baraniuk

ICLRW 2025 Self-Improving Diffusion Models with Synthetic Data Sina Alemohammad, Ahmed Imtiaz Humayun, Shruti Agarwal, John Collomosse, Richard Baraniuk

NeurIPS 2025 WaLRUS: Wavelets for Long Range Representation Using State Space Methods Hossein Babaei, Mel White, Sina Alemohammad, Richard Baraniuk

TMLR 2024 Boomerang: Local Sampling on Image Manifolds Using Diffusion Models Lorenzo Luzi, Paul M Mayer, Josue Casco-Rodriguez, Ali Siahkoohi, Richard Baraniuk

ICML 2024 Deep Networks Always Grok and Here Is Why Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk

ICMLW 2024 Deep Networks Always Grok and Here Is Why Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk

ICMLW 2024 Grokking and the Geometry of Circuit Formation Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk

ICLR 2024 Implicit Neural Representations and the Algebra of Complex Wavelets T Mitchell Roddenberry, Vishwanath Saragadam, Maarten V. de Hoop, Richard Baraniuk

ICML 2024 PIDformer: Transformer Meets Control Theory Tam Minh Nguyen, Cesar A Uribe, Tan Minh Nguyen, Richard Baraniuk

NeurIPSW 2024 RNN Replay: Leakage and Underdamped Dynamics Josue Casco-Rodriguez, Richard Baraniuk

ICMLW 2024 ScaLES: Scalable Latent Exploration Score for Pre-Trained Generative Networks Omer Ronen, Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk, Bin Yu

ICLR 2024 Self-Consuming Generative Models Go MAD Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi, Ahmed Imtiaz Humayun, Hossein Babaei, Daniel LeJeune, Ali Siahkoohi, Richard Baraniuk

NeurIPSW 2024 Visualizing Linear RNNs Through Unrolling Josue Casco-Rodriguez, Tyler Burley, Cj Barberan, Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk

ICLR 2023 A Primal-Dual Framework for Transformers and Neural Networks Tan Minh Nguyen, Tam Minh Nguyen, Nhat Ho, Andrea L. Bertozzi, Richard Baraniuk, Stanley Osher

NeurIPS 2023 Mitigating Over-Smoothing in Transformers via Regularized Nonlocal Functionals Tam Nguyen, Tan Nguyen, Richard Baraniuk

ICMLW 2023 Provable Instance Specific Robustness via Linear Constraints Ahmed Imtiaz Humayun, Josue Casco-Rodriguez, Randall Balestriero, Richard Baraniuk

ICLR 2023 Retrieval-Based Controllable Molecule Generation Zichao Wang, Weili Nie, Zhuoran Qiao, Chaowei Xiao, Richard Baraniuk, Anima Anandkumar

CVPR 2022 Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping Yeh-Chiang, Yehuda Dar, Richard Baraniuk, Micah Goldblum, Tom Goldstein

NeurIPSW 2022 Exact Visualization of Deep Neural Network Geometry and Decision Boundary Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk

ICML 2022 Improving Transformers with Probabilistic Attention Keys Tam Minh Nguyen, Tan Minh Nguyen, Dung D. D. Le, Duy Khuong Nguyen, Viet-Anh Tran, Richard Baraniuk, Nhat Ho, Stanley Osher

ICLR 2022 MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk

TMLR 2022 Max-Affine Spline Insights into Deep Network Pruning Haoran You, Randall Balestriero, Zhihan Lu, Yutong Kou, Huihong Shi, Shunyao Zhang, Shang Wu, Yingyan Lin, Richard Baraniuk

NeurIPS 2022 Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference Jasper Tan, Blake Mason, Hamid Javadi, Richard Baraniuk

CVPR 2022 Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk

NeurIPS 2021 The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization Daniel LeJeune, Hamid Javadi, Richard Baraniuk

ICLR 2021 The Recurrent Neural Tangent Kernel Sina Alemohammad, Zichao Wang, Randall Balestriero, Richard Baraniuk

NeurIPS 2020 Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks Randall Balestriero, Sebastien Paris, Richard Baraniuk

NeurIPS 2020 MomentumRNN: Integrating Momentum into Recurrent Neural Networks Tan Nguyen, Richard Baraniuk, Andrea Bertozzi, Stanley Osher, Bao Wang

ICML 2020 Sub-Linear Memory Sketches for near Neighbor Search on Streaming Data Benjamin Coleman, Richard Baraniuk, Anshumali Shrivastava

ICML 2020 Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors Yehuda Dar, Paul Mayer, Lorenzo Luzi, Richard Baraniuk

AISTATS 2020 The Implicit Regularization of Ordinary Least Squares Ensembles Daniel LeJeune, Hamid Javadi, Richard Baraniuk

AISTATS 2020 Thresholding Graph Bandits with GrAPL Daniel LeJeune, Gautam Dasarathy, Richard Baraniuk

ICLR 2019 A Max-Affine Spline Perspective of Recurrent Neural Networks Zichao Wang, Randall Balestriero, Richard Baraniuk

AISTATS 2019 Adaptive Estimation for Approximate $k$-Nearest-Neighbor Computations Daniel LeJeune, Reinhard Heckel, Richard Baraniuk

ICLR 2019 From Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference Randall Balestriero, Richard Baraniuk

NeurIPS 2019 The Geometry of Deep Networks: Power Diagram Subdivision Randall Balestriero, Romain Cosentino, Behnaam Aazhang, Richard Baraniuk

ICML 2018 Spline Filters for End-to-End Deep Learning Randall Balestriero, Romain Cosentino, Herve Glotin, Richard Baraniuk

ICML 2018 prDeep: Robust Phase Retrieval with a Flexible Deep Network Christopher Metzler, Phillip Schniter, Ashok Veeraraghavan, Richard Baraniuk

NeurIPS 2017 Learned D-AMP: Principled Neural Network Based Compressive Image Recovery Chris Metzler, Ali Mousavi, Richard Baraniuk

NeurIPS 2016 A Probabilistic Framework for Deep Learning Ankit B Patel, Minh Tan Nguyen, Richard Baraniuk

ICML 2016 Dealbreaker: A Nonlinear Latent Variable Model for Educational Data Andrew Lan, Tom Goldstein, Richard Baraniuk, Christoph Studer

NeurIPS 2013 When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements Divyanshu Vats, Richard Baraniuk

NeurIPS 2011 SpaRCS: Recovering Low-Rank and Sparse Matrices from Compressive Measurements Andrew E. Waters, Aswin C. Sankaranarayanan, Richard Baraniuk

NeurIPS 2008 Sparse Signal Recovery Using Markov Random Fields Volkan Cevher, Marco F. Duarte, Chinmay Hegde, Richard Baraniuk

NeurIPS 2007 Random Projections for Manifold Learning Chinmay Hegde, Michael Wakin, Richard Baraniuk