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ORCIDRichard G. Baraniuk
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- affiliation: Rice University, Houston, TX, USA
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2020 – today
- 2024
[j122]Daniel LeJeune, Pratik Patil, Hamid Javadi, Richard G. Baraniuk, Ryan J. Tibshirani:
Asymptotics of the Sketched Pseudoinverse. SIAM J. Math. Data Sci. 6(1): 199-225 (2024)- [j121]Yehuda Dar, Daniel LeJeune, Richard G. Baraniuk:
The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression. SIAM J. Math. Data Sci. 6(2): 454-480 (2024) - [j120]Lorenzo Luzi, Paul M. Mayer, Josue Casco-Rodriguez, Ali Siahkoohi, Richard G. Baraniuk:
Boomerang: Local sampling on image manifolds using diffusion models. Trans. Mach. Learn. Res. 2024 (2024) - [j119]Hossein Babaei
, Sina Alemohammad, Richard G. Baraniuk:
Covariate Balancing Methods for Randomized Controlled Trials Are Not Adversarially Robust. IEEE Trans. Neural Networks Learn. Syst. 35(4): 5014-5026 (2024) - [c258]Shashank Sonkar, Kangqi Ni, Lesa Tran Lu, Kristi Kincaid, John S. Hutchinson, Richard G. Baraniuk:
Automated Long Answer Grading with RiceChem Dataset. AIED (1) 2024: 163-176 - [c257]Shashank Sonkar, Naiming Liu, Debshila Basu Mallick, Richard G. Baraniuk:
Marking: Visual Grading with Highlighting Errors and Annotating Missing Bits. AIED (1) 2024: 309-323 - [c256]Lorenzo Luzi, Daniel LeJeune, Ali Siahkoohi, Sina Alemohammad, Vishwanath Saragadam, Hossein Babaei, Naiming Liu, Zichao Wang, Richard G. Baraniuk:
Titan: Bringing the Deep Image Prior to Implicit Representations. ICASSP 2024: 6165-6169 - [c255]Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi, Ahmed Imtiaz Humayun, Hossein Babaei, Daniel LeJeune, Ali Siahkoohi, Richard G. Baraniuk:
Self-Consuming Generative Models Go MAD. ICLR 2024 - [c254]T. Mitchell Roddenberry, Vishwanath Saragadam, Maarten V. de Hoop, Richard G. Baraniuk:
Implicit Neural Representations and the Algebra of Complex Wavelets. ICLR 2024 - [c253]Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
Deep Networks Always Grok and Here is Why. ICML 2024 - [c252]Tam Minh Nguyen, César A. Uribe, Tan Minh Nguyen, Richard G. Baraniuk:
PIDformer: Transformer Meets Control Theory. ICML 2024 - [c251]Shashank Sonkar, Xinghe Chen, Myco Le, Naiming Liu, Debshila Basu Mallick, Richard G. Baraniuk:
Code Soliloquies for Accurate Calculations in Large Language Models. LAK 2024: 828-835 - [i172]Josue Casco-Rodriguez, Caleb Kemere, Richard G. Baraniuk:
[Re] The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Non-Gaussian Observation Models. CoRR abs/2401.14429 (2024) - [i171]Shashank Sonkar, Kangqi Ni, Sapana Chaudhary, Richard G. Baraniuk:
Pedagogical Alignment of Large Language Models. CoRR abs/2402.05000 (2024) - [i170]Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
Deep Networks Always Grok and Here is Why. CoRR abs/2402.15555 (2024) - [i169]Tam Nguyen, César A. Uribe, Tan M. Nguyen, Richard G. Baraniuk:
PIDformer: Transformer Meets Control Theory. CoRR abs/2402.15989 (2024) - [i168]Shashank Sonkar, Naiming Liu, Debshila Basu Mallick, Richard G. Baraniuk:
Marking: Visual Grading with Highlighting Errors and Annotating Missing Bits. CoRR abs/2404.14301 (2024) - [i167]Shashank Sonkar, Kangqi Ni, Lesa Tran Lu, Kristi Kincaid, John S. Hutchinson, Richard G. Baraniuk:
Automated Long Answer Grading with RiceChem Dataset. CoRR abs/2404.14316 (2024) - [i166]Shashank Sonkar, Naiming Liu, Richard G. Baraniuk:
Regressive Side Effects of Training Language Models to Mimic Student Misconceptions. CoRR abs/2404.15156 (2024) - [i165]Shashank Sonkar, Richard G. Baraniuk:
Many-Shot Regurgitation (MSR) Prompting. CoRR abs/2405.08134 (2024) - [i164]Paul M. Mayer, Lorenzo Luzi, Ali Siahkoohi, Don H. Johnson, Richard G. Baraniuk:
Removing Bias from Maximum Likelihood Estimation with Model Autophagy. CoRR abs/2405.13977 (2024) - [i163]Omer Ronen, Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk, Bin Yu:
ScaLES: Scalable Latent Exploration Score for Pre-Trained Generative Networks. CoRR abs/2406.09657 (2024) - [i162]Naiming Liu, Zichao Wang, Richard G. Baraniuk:
Synthetic Context Generation for Question Generation. CoRR abs/2406.13188 (2024) - [i161]Tan M. Nguyen, Tam Nguyen, Nhat Ho, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher:
A Primal-Dual Framework for Transformers and Neural Networks. CoRR abs/2406.13781 (2024) - [i160]Naiming Liu, Shashank Sonkar, Myco Le, Richard G. Baraniuk:
MalAlgoQA: A Pedagogical Approach for Evaluating Counterfactual Reasoning Abilities. CoRR abs/2407.00938 (2024) - [i159]Randall Balestriero, Ahmed Imtiaz Humayun, Richard G. Baraniuk:
On the Geometry of Deep Learning. CoRR abs/2408.04809 (2024) - [i158]Sina Alemohammad, Ahmed Imtiaz Humayun, Shruti Agarwal, John P. Collomosse, Richard G. Baraniuk:
Self-Improving Diffusion Models with Synthetic Data. CoRR abs/2408.16333 (2024) - 2023
- [j118]Fernando Gama, Nicolas Zilberstein, Martin Sevilla, Richard G. Baraniuk, Santiago Segarra:
Unsupervised Learning of Sampling Distributions for Particle Filters. IEEE Trans. Signal Process. 71: 3852-3866 (2023) - [c250]Vincent Aleven, Richard G. Baraniuk, Emma Brunskill, Scott Crossley, Dora Demszky, Stephen Fancsali, Shivang Gupta, Kenneth R. Koedinger, Chris Piech, Steven Ritter, Danielle R. Thomas, Simon Woodhead, Wanli Xing:
Towards the Future of AI-Augmented Human Tutoring in Math Learning. AIED (Posters/Late Breaking Results/...) 2023: 26-31 - [c249]Shashank Sonkar, Richard G. Baraniuk:
Deduction under Perturbed Evidence: Probing Student Simulation (Knowledge Tracing) Capabilities of Large Language Models. LLM@AIED 2023: 26-33 - [c248]Katie Bainbridge, Candace A. Walkington, Armon Ibrahim, Iris Zhong, Debshila Basu Mallick, Julianna Washington, Richard G. Baraniuk:
A Case Study using Large Language Models to Generate Metadata for Math Questions. LLM@AIED 2023: 34-42 - [c247]Jasper Tan, Daniel LeJeune, Blake Mason, Hamid Javadi, Richard G. Baraniuk:
A Blessing of Dimensionality in Membership Inference through Regularization. AISTATS 2023: 10968-10993 - [c246]Zichao Wang, Richard G. Baraniuk:
MultiQG-TI: Towards Question Generation from Multi-modal Sources. BEA@ACL 2023: 682-691 - [c245]Ahmed Imtiaz Humayun, Randall Balestriero, Guha Balakrishnan, Richard G. Baraniuk:
SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries. CVPR 2023: 3789-3798 - [c244]Vishwanath Saragadam, Daniel LeJeune, Jasper Tan, Guha Balakrishnan, Ashok Veeraraghavan, Richard G. Baraniuk:
WIRE: Wavelet Implicit Neural Representations. CVPR 2023: 18507-18516 - [c243]Shashank Sonkar, Naiming Liu, Debshila Basu Mallick, Richard G. Baraniuk:
CLASS: A Design Framework for Building Intelligent Tutoring Systems Based on Learning Science principles. EMNLP (Findings) 2023: 1941-1961 - [c242]Tan M. Nguyen, Tam Nguyen, Long Bui, Hai Do, Duy Khuong Nguyen, Dung D. Le, Hung Tran-The, Nhat Ho, Stanley J. Osher, Richard G. Baraniuk:
A Probabilistic Framework for Pruning Transformers Via a Finite Admixture of Keys. ICASSP 2023: 1-5 - [c241]Zichao Wang, Weili Nie, Zhuoran Qiao, Chaowei Xiao, Richard G. Baraniuk, Anima Anandkumar:
Retrieval-based Controllable Molecule Generation. ICLR 2023 - [c240]Tan Minh Nguyen, Tam Minh Nguyen, Nhat Ho, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher:
A Primal-Dual Framework for Transformers and Neural Networks. ICLR 2023 - [c239]Steven Ritter, Neil T. Heffernan, Joseph Jay Williams, Derek Lomas, Klinton Bicknell, Jeremy Roschelle, Ben Motz, Danielle S. McNamara, Richard G. Baraniuk, Debshila Basu Mallick, René F. Kizilcec, Ryan Baker, Stephen Fancsali, April Murphy:
Fourth Annual Workshop on A/B Testing and Platform-Enabled Learning Research. L@S 2023: 254-256 - [c238]Debshila Basu Mallick, Brittany C. Bradford, Richard G. Baraniuk:
Secure Education and Learning Research at Scale with OpenStax Kinetic. L@S 2023: 360-362 - [c237]Brittany C. Bradford, Debshila Basu Mallick, Richard G. Baraniuk:
Unlocking Financial Success: Empowering Higher Ed Students and Developing Financial Literacy Interventions at Scale. L@S 2023: 363-367 - [c236]Tam Nguyen, Tan Nguyen, Richard G. Baraniuk:
Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals. NeurIPS 2023 - [c235]Shashank Sonkar, Zichao Wang, Richard G. Baraniuk:
MANER: Mask Augmented Named Entity Recognition for Extreme Low-Resource Languages. SustaiNLP 2023: 219-226 - [c234]Lorenzo Luzi, Carlos Ortiz Marrero, Nile Wynar, Richard G. Baraniuk, Michael J. Henry:
Evaluating generative networks using Gaussian mixtures of image features. WACV 2023: 279-288 - [i157]Vishwanath Saragadam, Daniel LeJeune, Jasper Tan, Guha Balakrishnan, Ashok Veeraraghavan, Richard G. Baraniuk:
WIRE: Wavelet Implicit Neural Representations. CoRR abs/2301.05187 (2023) - [i156]Ahmed Imtiaz Humayun, Randall Balestriero, Guha Balakrishnan, Richard G. Baraniuk:
SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries. CoRR abs/2302.12828 (2023) - [i155]Shashank Sonkar, Lucy Liu, Debshila Basu Mallick, Richard G. Baraniuk:
CLASS Meet SPOCK: An Education Tutoring Chatbot based on Learning Science Principles. CoRR abs/2305.13272 (2023) - [i154]Shashank Sonkar, Richard G. Baraniuk:
Investigating the Role of Feed-Forward Networks in Transformers Using Parallel Attention and Feed-Forward Net Design. CoRR abs/2305.13297 (2023) - [i153]Shashank Sonkar, Richard G. Baraniuk:
Deduction under Perturbed Evidence: Probing Student Simulation Capabilities of Large Language Models. CoRR abs/2305.14507 (2023) - [i152]Sina Alemohammad, Josue Casco-Rodriguez, Lorenzo Luzi, Ahmed Imtiaz Humayun, Hossein Babaei, Daniel LeJeune, Ali Siahkoohi, Richard G. Baraniuk:
Self-Consuming Generative Models Go MAD. CoRR abs/2307.01850 (2023) - [i151]Zichao Wang, Richard G. Baraniuk:
MultiQG-TI: Towards Question Generation from Multi-modal Sources. CoRR abs/2307.04643 (2023) - [i150]Shashank Sonkar, Myco Le, Xinghe Chen, Naiming Liu, Debshila Basu Mallick, Richard G. Baraniuk:
Code Soliloquies for Accurate Calculations in Large Language Models. CoRR abs/2309.12161 (2023) - [i149]T. Mitchell Roddenberry, Vishwanath Saragadam, Maarten V. de Hoop, Richard G. Baraniuk:
Implicit Neural Representations and the Algebra of Complex Wavelets. CoRR abs/2310.00545 (2023) - [i148]Naiming Liu, Shashank Sonkar, Zichao Wang, Simon Woodhead, Richard G. Baraniuk:
Novice Learner and Expert Tutor: Evaluating Math Reasoning Abilities of Large Language Models with Misconceptions. CoRR abs/2310.02439 (2023) - [i147]Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
Training Dynamics of Deep Network Linear Regions. CoRR abs/2310.12977 (2023) - [i146]Tam Nguyen, Tan M. Nguyen, Richard G. Baraniuk:
Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals. CoRR abs/2312.00751 (2023) - [i145]Micah Goldblum, Anima Anandkumar, Richard G. Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C. Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson:
Perspectives on the State and Future of Deep Learning - 2023. CoRR abs/2312.09323 (2023) - 2022
- [j117]David J. Brenes, C. J. Barberan, Brady Hunt, Sonia G. Parra, Mila P. Salcedo, Júlio C. Possati-Resende, Miriam L. Cremer, Philip E. Castle, José H. T. G. Fregnani, Mauricio Maza, Kathleen M. Schmeler, Richard G. Baraniuk, Rebecca R. Richards-Kortum:
Multi-task network for automated analysis of high-resolution endomicroscopy images to detect cervical precancer and cancer. Comput. Medical Imaging Graph. 97: 102052 (2022) - [j116]Ali Mousavi, Richard G. Baraniuk:
Uniform Partitioning of Data Grid for Association Detection. IEEE Trans. Pattern Anal. Mach. Intell. 44(2): 1098-1107 (2022) - [j115]Bao Wang, Tan M. Nguyen, Tao Sun, Andrea L. Bertozzi, Richard G. Baraniuk, Stanley J. Osher:
Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent. SIAM J. Imaging Sci. 15(2): 738-761 (2022) - [j114]Yehuda Dar, Richard G. Baraniuk:
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Tasks. SIAM J. Math. Data Sci. 4(4): 1447-1472 (2022) - [j113]Ángel Bueno Rodríguez, Randall Balestriero, Silvio De Angelis, M. Carmen Benítez, Luciano Zuccarello, Richard G. Baraniuk, Jesús M. Ibáñez, Maarten V. de Hoop:
Recurrent Scattering Network Detects Metastable Behavior in Polyphonic Seismo-Volcanic Signals for Volcano Eruption Forecasting. IEEE Trans. Geosci. Remote. Sens. 60: 1-23 (2022) - [j112]Haoran You, Randall Balestriero, Zhihan Lu, Yutong Kou, Huihong Shi, Shunyao Zhang, Shang Wu, Yingyan Lin, Richard G. Baraniuk:
Max-Affine Spline Insights Into Deep Network Pruning. Trans. Mach. Learn. Res. 2022 (2022) - [j111]Pavan K. Kota, Daniel LeJeune, Rebekah A. Drezek, Richard G. Baraniuk:
Extreme Compressed Sensing of Poisson Rates From Multiple Measurements. IEEE Trans. Signal Process. 70: 2388-2401 (2022) - [j110]T. Mitchell Roddenberry, Fernando Gama, Richard G. Baraniuk, Santiago Segarra:
On Local Distributions in Graph Signal Processing. IEEE Trans. Signal Process. 70: 5564-5577 (2022) - [c233]Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan M. Sengupta, Richard G. Baraniuk, Behnaam Aazhang:
Spatial Transformer K-Means. IEEECONF 2022: 1444-1448 - [c232]Zichao Wang, Jakob Valdez, Debshila Basu Mallick, Richard G. Baraniuk:
Towards Human-Like Educational Question Generation with Large Language Models. AIED (1) 2022: 153-166 - [c231]Nigel Fernandez, Aritra Ghosh, Naiming Liu, Zichao Wang, Benoît Choffin, Richard G. Baraniuk, Andrew S. Lan:
Automated Scoring for Reading Comprehension via In-context BERT Tuning. AIED (1) 2022: 691-697 - [c230]Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values. CVPR 2022: 10631-10640 - [c229]Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein:
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CVPR 2022: 13689-13698 - [c228]Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan, Richard G. Baraniuk, Ashok Veeraraghavan:
MINER: Multiscale Implicit Neural Representation. ECCV (23) 2022: 318-333 - [c227]Naiming Liu, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan:
Open-ended Knowledge Tracing for Computer Science Education. EMNLP 2022: 3849-3862 - [c226]C. J. Barberan, Sina Alemmohammad, Naiming Liu, Randall Balestriero, Richard G. Baraniuk:
NeuroView-RNN: It's About Time. FAccT 2022: 1683-1697 - [c225]Sina Alemohammad, Hossein Babaei, C. J. Barberan, Naiming Liu, Lorenzo Luzi, Blake Mason, Richard G. Baraniuk:
NFT-K: Non-Fungible Tangent Kernels. ICASSP 2022: 3798-3802 - [c224]Randall Balestriero, Zichao Wang, Richard G. Baraniuk:
DeepHull: Fast Convex Hull Approximation in High Dimensions. ICASSP 2022: 3888-3892 - [c223]Ahmed Imtiaz Humayun, Randall Balestriero, Anastasios Kyrillidis, Richard G. Baraniuk:
No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds. ICASSP 2022: 4433-4437 - [c222]Fernando Gama, Nicolas Zilberstein, Richard G. Baraniuk, Santiago Segarra:
Unrolling Particles: Unsupervised Learning of Sampling Distributions. ICASSP 2022: 5498-5502 - [c221]Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining. ICLR 2022 - [c220]Tam Minh Nguyen, Tan Minh Nguyen, Dung D. D. Le, Duy Khuong Nguyen, Viet-Anh Tran, Richard G. Baraniuk, Nhat Ho, Stanley J. Osher:
Improving Transformers with Probabilistic Attention Keys. ICML 2022: 16595-16621 - [c219]Steven Ritter, Neil T. Heffernan, Joseph Jay Williams, Derek Lomas, Ben Motz, Debshila Basu Mallick, Klinton Bicknell, Danielle S. McNamara, René F. Kizilcec, Jeremy Roschelle, Richard G. Baraniuk, Ryan Baker:
Third Annual Workshop on A/B Testing and Platform-Enabled Learning Research. L@S 2022: 252-254 - [c218]Tan Minh Nguyen, Richard G. Baraniuk, Robert M. Kirby, Stanley J. Osher, Bao Wang:
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization. MSML 2022: 189-204 - [c217]Jasper Tan, Blake Mason, Hamid Javadi, Richard G. Baraniuk:
Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference. NeurIPS 2022 - [i144]Jasper Tan, Blake Mason, Hamid Javadi, Richard G. Baraniuk:
Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference. CoRR abs/2202.01243 (2022) - [i143]Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan, Richard G. Baraniuk, Ashok Veeraraghavan:
MINER: Multiscale Implicit Neural Representations. CoRR abs/2202.03532 (2022) - [i142]Romain Cosentino, Randall Balestriero, Yanis Bahroun, Anirvan M. Sengupta, Richard G. Baraniuk, Behnaam Aazhang:
Spatial Transformer K-Means. CoRR abs/2202.07829 (2022) - [i141]T. Mitchell Roddenberry, Fernando Gama, Richard G. Baraniuk, Santiago Segarra:
On Local Distributions in Graph Signal Processing. CoRR abs/2202.10649 (2022) - [i140]C. J. Barberan, Sina Alemohammad, Naiming Liu, Randall Balestriero, Richard G. Baraniuk:
NeuroView-RNN: It's About Time. CoRR abs/2202.11811 (2022) - [i139]Ahmed Imtiaz Humayun, Randall Balestriero, Richard G. Baraniuk:
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values. CoRR abs/2203.01993 (2022) - [i138]Ahmed Imtiaz Humayun, Randall Balestriero, Anastasios Kyrillidis, Richard G. Baraniuk:
No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds. CoRR abs/2203.02502 (2022) - [i137]Rudolf H. Riedi, Randall Balestriero, Richard G. Baraniuk:
Singular Value Perturbation and Deep Network Optimization. CoRR abs/2203.03099 (2022) - [i136]Naiming Liu, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan:
Open-Ended Knowledge Tracing. CoRR abs/2203.03716 (2022) - [i135]Gowthami Somepalli, Liam Fowl, Arpit Bansal, Ping-Yeh Chiang, Yehuda Dar, Richard G. Baraniuk, Micah Goldblum, Tom Goldstein:
Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective. CoRR abs/2203.08124 (2022) - [i134]Vishwanath Saragadam, Randall Balestriero, Ashok Veeraraghavan, Richard G. Baraniuk:
DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors. CoRR abs/2204.03145 (2022) - [i133]Nigel Fernandez, Aritra Ghosh, Naiming Liu, Zichao Wang, Benoît Choffin, Richard G. Baraniuk, Andrew S. Lan:
Automated Scoring for Reading Comprehension via In-context BERT Tuning. CoRR abs/2205.09864 (2022) - [i132]Jasper Tan, Daniel LeJeune, Blake Mason, Hamid Javadi, Richard G. Baraniuk:
Benign Overparameterization in Membership Inference with Early Stopping. CoRR abs/2205.14055 (2022) - [i131]Tan M. Nguyen, Richard G. Baraniuk, Robert M. Kirby, Stanley J. Osher, Bao Wang:
Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization. CoRR abs/2208.00579 (2022) - [i130]Zichao Wang, Weili Nie, Zhuoran Qiao, Chaowei Xiao, Richard G. Baraniuk, Anima Anandkumar:
Retrieval-based Controllable Molecule Generation. CoRR abs/2208.11126 (2022) - [i129]Randall Balestriero, Richard G. Baraniuk:
Batch Normalization Explained. CoRR abs/2209.14778 (2022) - [i128]Lorenzo Luzi, Ali Siahkoohi, Paul M. Mayer, Josue Casco-Rodriguez, Richard G. Baraniuk:
Boomerang: Local sampling on image manifolds using diffusion models. CoRR abs/2210.12100 (2022) - [i127]Shashank Sonkar, Naiming Liu, Richard G. Baraniuk:
A Visual Tour Of Current Challenges In Multimodal Language Models. CoRR abs/2210.12565 (2022) - [i126]Daniel LeJeune, Pratik Patil, Hamid Javadi, Richard G. Baraniuk, Ryan J. Tibshirani:
Asymptotics of the Sketched Pseudoinverse. CoRR abs/2211.03751 (2022) - [i125]Yehuda Dar, Lorenzo Luzi, Richard G. Baraniuk:
Overfreezing Meets Overparameterization: A Double Descent Perspective on Transfer Learning of Deep Neural Networks. CoRR abs/2211.11074 (2022) - [i124]Vishwanath Saragadam, Zheyi Han, Vivek Boominathan, Luocheng Huang, Shiyu Tan, Johannes E. Fröch, Karl F. Böhringer, Richard G. Baraniuk, Arka Majumdar, Ashok Veeraraghavan:
Foveated Thermal Computational Imaging in the Wild Using All-Silicon Meta-Optics. CoRR abs/2212.06345 (2022) - [i123]Shashank Sonkar, Zichao Wang, Richard G. Baraniuk:
MANER: Mask Augmented Named Entity Recognition for Extreme Low-Resource Languages. CoRR abs/2212.09723 (2022) - 2021
- [j109]Vishwanath Saragadam, Michael DeZeeuw, Richard G. Baraniuk, Ashok Veeraraghavan, Aswin C. Sankaranarayanan:
SASSI - Super-Pixelated Adaptive Spatio-Spectral Imaging. IEEE Trans. Pattern Anal. Mach. Intell. 43(7): 2233-2244 (2021) - [j108]Randall Balestriero, Richard G. Baraniuk:
Mad Max: Affine Spline Insights Into Deep Learning. Proc. IEEE 109(5): 704-727 (2021) - [j107]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) - [c216]Zichao Wang, Sebastian Tschiatschek, Simon Woodhead, José Miguel Hernández-Lobato, Simon Peyton Jones, Richard G. Baraniuk, Cheng Zhang:
Educational Question Mining At Scale: Prediction, Analysis and Personalization. AAAI 2021: 15669-15677 - [c215]Andrew E. Waters, Vinay K. Chaudhri, Debshila Basu Mallick, Richard G. Baraniuk:
A Relationship Selection Task (short paper). iTextbooks@AIED 2021: 88-92 - [c214]Zichao Wang, Andrew S. Lan, Richard G. Baraniuk:
Mathematical Formula Representation via Tree Embeddings. iTextbooks@AIED 2021: 121-133 - [c213]Zichao Wang, Kyle Manning, Debshila Basu Mallick, Richard G. Baraniuk:
Towards Blooms Taxonomy Classification Without Labels. AIED (1) 2021: 433-445 - [c212]Vinay K. Chaudhri, Matthew Boggess, Han Lin Aung, Debshila Basu Mallick, Andrew C. Waters, Richard G. Baraniuk:
A Case Study in Bootstrapping Ontology Graphs from Textbooks. AKBC 2021 - [c211]Tianyi Yao, Daniel LeJeune, Hamid Javadi, Richard G. Baraniuk, Genevera I. Allen:
Minipatch Learning as Implicit Ridge-Like Regularization. BigComp 2021: 65-68 - [c210]Zichao Wang, Mengxue Zhang, Richard G. Baraniuk, Andrew S. Lan:
Scientific Formula Retrieval via Tree Embeddings. IEEE BigData 2021: 1493-1503 - [c209]Mengxue Zhang, Zichao Wang, Richard G. Baraniuk, Andrew S. Lan:
Math Operation Embeddings for Open-ended Solution Analysis and Feedback. EDM 2021 - [c208]Zichao Wang, Andrew S. Lan, Richard G. Baraniuk:
Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints. EMNLP (1) 2021: 5986-5999 - [c207]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. ICASSP 2021: 2950-2954 - [c206]