Publications | Deep Learning ONR MURI

Anastasios Angelopoulos, Emmanuel Candes, and Ryan J. Tibshirani. Conformal PID Control for Time Series Prediction. Neural Information Processing Systems, 2023.
Tiffany Ding, Anastasios Angelopoulos, Stephen Bates, Michael Jordan, and Ryan J. Tibshirani. Class-Conditional Conformal Prediction with Many Classes. Neural Information Processing Systems, 2023.
Alden, Green, Siva Balakrishnan, Ryan J. Tibshirani. Minimax Optimal Regression over Sobolev Spaces via Laplacian Eigenmaps on Neighborhood Graphs. Information and Inference, 2023. (this is to be updated from the older arXiv ref)
Addison Hu, Alden Green, and Ryan Tibshirani. The Voronoigram: Minimax Estimation of Bounded Variation Functions From Scattered Data.
Luzi, L., Siahkoohi, A., Mayer, P. M., Casco-Rodriguez, J., & Baraniuk, R. G. Boomerang: Local Sampling on Image Manifolds using Diffusion Models. Transactions on Machine Learning Research, 2024.
Dar, Y., LeJeune, D., & Baraniuk, R. G. The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression. SIAM Journal on Mathematics of Data Science (SIMODS), 2024.
Roddenberry, M., Saragadam, V., Balakrishnan, G., & Baraniuk, R. G. Implicit Neural Representations and the Algebra of Complex Wavelets. International Conference on Learning Representations (ICLR), Vienna, May 2024.
Alemohammad, S., Casco-Rodriguez, J., Luzi, L., Humayun, A. I., Babaei, H., LeJeune, D., Siahkoohi, A., & Baraniuk, R. G. Self-Consuming Generative Models Go MAD. International Conference on Learning Representations (ICLR), Vienna, May 2024.
Luzi, L., LeJeune, D., Siahkoohi, A., Alemohammad, S., Saragadam, V., Babaei, H., Liu, N., Wang, Z., & Baraniuk, R. G. Titan: Bringing The Deep Image Prior to Implicit Representations. IEEE International Conference on Acoustics, Speech and Signal Processing – ICASSP’24, Seoul, Korea, April 2024.
Sonkar, S., Liu, L., Basu Mallick, D., & Baraniuk, R. G. Code Soliloquies for Accurate Calculations in Large Language Models. International Conference on Learning Analytics and Knowledge (LAK24), Kyoto, March 2024.
Gama, F., Zilberstein, N., Sevilla, M., Baraniuk, R. G., & Segarra, S. Unsupervised Learning of Sampling Distributions for Particle Filters. IEEE Transactions on Signal Processing, 2023.
Nguyen, T., Ho, N., Patel, A., Anandkumar, A., Jordan, M., & Baraniuk, R. G. A Bayesian Perspective of Convolutional Neural Networks through a Deconvolutional Generative Model. Journal of Machine Learning Research, 2023.
Nguyen, T. M., Nguyen, T., Bui, L., Do, H., Nguyen, D. K., Le, D. D., Tran-The, H., Ho, N., Osher, S. J., & Baraniuk, R. G. A Probabilistic Framework for Pruning Transformers via a Finite Admixture of Keys. IEEE International Conference on Acoustics, Speech and Signal Processing – ICASSP’23, Rhodes, June 2023.
Humayun, A. I., Balestriero, R., Balakrishnan, G., & Baraniuk, R. G. SplineCam: Exact Visualization of Deep Neural Network Geometry and Decision Boundaries. Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, June 2023.
Nguyen, T. M., Nguyen, T., Ho, N., Bertozzi, A. L., Baraniuk, R. G., & Osher, S. A Primal-Dual Framework for Transformers and Neural Networks. International Conference on Learning Representations (ICLR), Kigali, Rwanda, May 2023.
Tan, J., LeJeune, D., Mason, B., Javadi, H., & Baraniuk, R. G. A Blessing of Dimensionality in Membership Inference through Regularization. International Conference on Artificial Intelligence and Statistics (AISTATS), Valencia, Spain, April 2023.
Luzi, L., Ortiz Marrero, C., Wynar, N., Baraniuk, R. G., & Henry, M. J. Evaluating Generative Networks using Gaussian Mixtures of Image Features. Winter Conference on Applications of Computer Vision (WACV), Hawaii, January 2023.
Nguyen, Tam, Nguyen M. Tan, and Richard Baraniuk .Mitigating Over-smoothing in Transformers via Regularized Nonlocal Functionals. Advances in Neural Information Processing Systems, New Orleans, United States, December 2023.
Arikan, T., Weiss, A., Vishnu, H., Deane, G., Singer, A., & Wornell, G. (2023, June). Learning Environmental Structure Using Acoustic Probes with a Deep Neural Network. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
F Han, S Osher, W Li. Tensor Train Based Sampling Algorithms for Approximating Regularized Wasserstein Proximal Operators. arXiv preprint arXiv:2401.13125, 2024.
M Zhou, S Osher, W Li. A Deep Learning Algorithm for Computing Mean Field Control Problems via Forward-Backward Score Dynamics. arXiv preprint arXiv:2401.09547, 2024.
L Yang, SJ Osher. PDE Generalization of In-Context Operator Networks: A Study on 1D Scalar Nonlinear Conservation Laws. arXiv preprint arXiv:2401.07364, 2024.
L Yang, S Liu, T Meng, SJ Osher. In-Context Operator Learning with Data Prompts for Differential Equation Problems. Proceedings of the National Academy of Sciences, 120 (39), e2310142120, 2024.
HY Tan, S Osher, W Li. Noise-Free Sampling Algorithms via Regularized Wasserstein Proximals. arXiv preprint arXiv:2308.14945, 2023.
L Yang, T Meng, S Liu, SJ Osher. Prompting In-Context Operator Learning with Sensor Data, Equations, and Natural Language. arXiv preprint arXiv:2308.05061, 2023.
L Yang, S Liu, T Meng, SJ Osher. In-Context Operator Learning for Differential Equation Problems. arXiv preprint arXiv:2304.07993, 2023.
W Li, S Liu, S Osher. A Kernel Formula for Regularized Wasserstein Proximal Operators. arXiv preprint arXiv:2301.10301, 2023.
R DeVore, RD Nowak, R Parhi, JW Siegel. Weighted variation spaces and approximation by shallow ReLU networks arXiv preprint arXiv:2307.15772, 2023
J Shenouda, R Parhi, RD Nowak. A Continuous Transform for Localized Ridgelets IEEE International Conference on Sampling Theory and Applications (SampTA) 2023
J Shenouda, R Parhi, K Lee, RD Nowak Vector-Valued Variation Spaces and Width Bounds for DNNs: Insights on Weight Decay Regularization arXiv preprint arXiv:2305.16534
R Parhi and RD Nowak Deep Learning Meets Sparse Regularization: A signal processing perspective in IEEE Signal Processing Magazine, vol. 40, no. 6, pp. 63-74, Sept. 2023
L Yang, J Zhang, J Shenouda, D Papailiopoulos, K Lee, RD Nowak PathProx: A Proximal Gradient Algorithm for Weight Decay Regularized Deep Neural Networks arXiv preprint arXiv:2210.03069
R Parhi and RD Nowak, Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks in IEEE Transactions on Information Theory, vol. 69, no. 2, pp. 1125-1140, Feb. 2023
R Parhi and RD Nowak Banach Space Representer Theorems for Neural Networks
and Ridge Splines Journal of Machine Learning Research 2021
R Parhi and RD Nowak On Continuous-Domain Inverse Problems With Sparse Superpositions of Decaying Sinusoids as Solutions ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, Singapore, 2022, pp. 5603-5607
Y Wen, N Jain, J Kirchenbauer, M Goldblum, J Geiping, T Goldstein, Hard Prompts Made Easy: Gradient-based Discrete Optimization for Prompt Tuning and Discovery in Advances in Neural Information Processing Systems 36, 2024, pp. 80.
Y Wen, J Kirchenbauer, J Geiping, T Goldstein, Tree-Rings Watermarks: Invisible Fingerprints for Diffusion Images in Advances in Neural Information Processing Systems 36
G Somepalli, V Singla, M Goldblum, J Geiping, T Goldstein, Understanding and Mitigating Copying in Diffusion Models in arXiv preprint arXiv:2305.20086.
J Kirchenbauer, J Geiping, Y Wen, J Katz, I Miers, T Goldstein, A Watermark for Large Language Models in arXiv preprint arXiv:2301.10226.
G Somepalli, V Singla, M Goldblum, J Geiping, T Goldstein, Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 119.
Shah, A., Roy, A., Shah, K., Mishra, S., Jacobs, D., Cherian, A., & Chellappa, R. (2023). HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Shah, A., Lundell, B., Sawhney, H., & Chellappa, R. (2023). STEPs: Self-Supervised Key Step Extraction from Unlabeled Procedural Videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)
Roy, A., Shah, A., Shah, K., Dhar, P., Cherian, A., & Chellappa, R. (2022). FeLMi: Few shot Learning with hard Mixup. In Advances in Neural Information Processing Systems (NeurIPS)
Pramanick, S., Han, G., Hou, R., Nag, S., Lim, S.-N., Ballas, N., Wang, Q., Chellappa, R., & Almahairi, A. (2024). Jack of All Tasks, Master of Many: Designing General-purpose Coarse-to-Fine Vision-Language Model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Roy, A., Suin, M., Shah, A., Shah, K., Liu, J., & Chellappa, R. (n.d.). DiffNat: Improving diffusion image quality using natural image statistics, in arXiv preprint arXiv:2312.12423
Roy, A., Shah, A., Shah, K., Roy, A., & Chellappa, R. (n.d.). Cap2Aug: Caption guided Image-to-Image data Augmentation, in arXiv preprint arXiv:2212.0540
Wei, G., Wang, F., Shah, A., & Chellappa, R. (n.d.). Dual Prompt Tuning for Domain-Aware Federated Learning, in arXiv preprint arXiv:2310.0310

Goldblum, M., Tsipras, D., Xie, C., Chen, X., Schwarzschild, A., Song, D., Madry, A., Li, B., & Goldstein, T. Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
Gama, F., Zilberstein, N., Baraniuk, R. G., & Segarra, S. Unrolling Particles: Unsupervised Learning of Sampling Distributions, IEEE International Conference on Acoustics, Speech and Signal Processing – ICASSP’22, May 2022.
Sapoval, N., Aghazadeh, A., Nute, M. G., Antunes, D. A., Balaji, A., Baraniuk, R. G., Barberan, C. J., Dannenfelser, R., Dun, C., Edrisi, M., & Elworth, R. A. Current progress and open challenges for applying deep learning across the biosciences. Nature Communications, 13(1), pp.1-12, 2022.
Alemohammad, S., Babaei, H., Barberan, C. J., Liu, N., Luzi, L., Mason, B., & Baraniuk, R. G. NFT-K: Non-Fungible Tangent Kernels. IEEE International Conference on Acoustics, Speech and Signal Processing – ICASSP’22, May 2022.
Humayun, A. I., Balestriero, R., Kyrillidis, A., & Baraniuk, R. G. No More Than 6ft Apart: Robust K-Means via Radius Upper Bounds. IEEE International Conference on Acoustics, Speech and Signal Processing – ICASSP’22, May 2022.
Balestriero, R., Wang, Z., & Baraniuk, R. G. DeepHull: Fast Convex Hull Approximation in High Dimensions. IEEE International Conference on Acoustics, Speech and Signal Processing – ICASSP’22, May 2022.
Barberan, C. J., Alemohammad, S., Liu, N., Balestriero, R., & Baraniuk, R. G. NeuroView-RNN: It's About Time. FAccT, 2022.
Brenes, D., Barberan, C. J., Hunt, B., Parra, S. G., Salcedo, M. P., Possati-Resende, J. C., Cremer, M. L., Castle, P. E., Fregnani, J. H., Maza, M., & Schmeler, K. M. Multi-task network for automated analysis of high-resolution endomicroscopy images to detect cervical precancer and cancer, Computerized Medical Imaging and Graphics, 97, 2022.
Saragadam, V., Balestriero, R., Veeraraghavan, A., & Baraniuk, R. G. DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors. arXiv:2204.03145, 2022.
Dar, Y., LeJeune, D., & Baraniuk, R. G. The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression. arXiv: 2103.05621v2, 2022.
Shrotri, A. A., Narodytska, N., Ignatiev, A., Meel, K. S., Marques-Silva, J., & Vardi, M. Y. Constraint-Driven Explanations for Black-Box ML Models. AAAI, 2022.
Patil, P., Rinaldo, A. & Tibshirani, R. J. Estimating functionals of the out-of-sample error distribution in high-dimensional ridge regression. AISTATS, 2022.
Li, W., Liu, S., & Osher, S. Controlling conservation laws II: Compressible Navier–Stokes equations. Journal of Computational Physics, 2022.
Humayun, A. I., Balestriero, R. & Baraniuk, R. G. Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.
Humayun, A. I., Balestriero, R., & Baraniuk, R. G. Magnet: Uniform sampling from deep generative network manifolds without retraining. ICLR, 2022.
Saragadam, V., Tan, J., Balakrishnan, G., Baraniuk, R. G., & Veeraraghavan, A. MINER: Multiscale Implicit Neural Representations. arXiv:2202.03532, 2022.
Tibshirani, R. J. Divided Differences, Falling Factorials, and Discrete Splines: Another Look at Trend Filtering and Related Problems. Foundations and Trends in Machine Learning, Vol. 15, No. 6, 694-846, 2022.
Curry, M. J., Lyi, U., Goldstein, T., & Dickerson, J. P. Learning revenue-maximizing auctions with differentiable matching. AISTATS, 2022.
Riedi, R. H., Balestriero, R., & Baraniuk, R. G., Singular Value Perturbation and Deep Network Optimization, Constructive Approximation, 2022.
Lin, A.T., Debord, M., Estabridis, K., Hewer, G., Montufar, G., & Osher, S. Decentralized multi-agents by imitation of a centralized controller. In Mathematical and Scientific Machine Learning, PMLR. 2022.
Pramanick, S., Roy, A., & Patel, V. M. Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection. Winter Conference on Computer Vision, 2022.
Tan, J., LeJeune, D., Mason, B., Javadi, H., & Baraniuk, R. G. Benign Overparameterization in Membership Inference with Early Stopping. arXiv:2205.14055, 2022.
Tan, J., Mason, B., Javadi, H., & Baraniuk, R. G. Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference. arXiv:2202.01243, 2022.
Baker, J., Xia, H., Wang, Y., Cherkaev, E., Narayan, A., Chen, L., Xin, J., Bertozzi, A. L., Osher, S. J., & Wang, B. Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs. arXiv:2204.0862, 2022.
Heaton, H., Fung, S. W., & Osher, S. Global Solutions to Nonconvex Problems by Evolution of Hamilton-Jacobi PDEs. arXiv:2202.11014, 2022.
Shah, A., Sra, S., Chellappa, R., Cherian, A. Max-Margin Contrastive Learning. AAAI, 2022.
Heaton, H., Fung, S. W., Lin, A.T., Osher, S., & Yin, W. Wasserstein-Based Projections with Applications to Inverse Problems. SIAM Journal on Mathematics of Data Science, 2022.
Green, A., Balakrishnan, S. & Tibshirani, R. J. Minimax Optimal Regression over Sobolev Spaces via Laplacian Eigenmaps on Neighborhood Graphs. arXiv:2111.07394, 2021.
Ni, R. Shu, M., Souri, H., Goldblum, M., & Goldstein, T. The Close Relationship Between Contrastive Learning and Meta-Learning. ICLR, 2021.
Barberan, C. J., Balestriero, R. & Baraniuk, R. G. NeuroView: Explainable Deep Network Decision Making, arXiv:2110.07778, 2021.
Saragadam, V., Dave, A., Veeraraghavan, A. & Baraniuk, R. G. Thermal Image Processing via Physics-Inspired Deep Networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021.
Shu, M., Wu, Z., Goldblum, M., & Goldstein, T. Encoding Robustness to Image Style via Adversarial Feature Perturbations. NeurIPS, 2021.
Schwarzschild, A., Borgnia, E., Gupta, A., Huang, F., Vishkin, U., Goldblum, M., & Goldstein, T. Can you learn an algorithm? generalizing from easy to hard problems with recurrent networks. NeurIPS, 2021.
Luzi, L., Marrero, C. O., Wynar, N., Baraniuk, R. G., & Henry, M. J. Evaluating generative networks using Gaussian mixtures of image features, arXiv:2110.05240, 2021.
Xia, H., Suliafu, V., Ji, H., Nguyen, T., Bertozzi, A. L., Osher, S., & Wang, B. Heavy ball neural ordinary differential equations. NeurIPS, 2021.
Nguyen, T., Suliafu, V., Osher, S., Chen, L., & Wang, B. Fmmformer: Efficient and flexible transformer via decomposed near-field and far-field attention. NeurIPS, 2021.
Li, W., Liu, S., & Osher, S. Controlling conservation laws I: entropy-entropy flux. arXiv:2111.05473, 2021.
Tan, J., Boominathan, V., Baraniuk, R. G., & Veeraraghavan, A. EDoF-ToF: Extended depth of field time-of-flight imaging. Optics Express, 29(23), 2021.
Fung, S. W., Heaton, H., Li, Q., McKenzie, D., Osher, S., & Yin, W. Efficient training of infinite-depth neural networks via Jacobian-Free backpropagation. AMS Fall Western Sectional Meeting, 2021.
Nguyen, T., Nguyen, T., Le, D., Nguyen, K., Tran, A., Baraniuk, R. G., Ho, N., & Osher, S. Improving transformers with probabilistic attention keys. ICML, 2021.
Wang, B., Xia, H., Nguyen, T., & Osher, S. How does momentum benefit deep neural networks architecture design? a few case studies. arXiv:2110.07034, 2021.
Thorpe, M., Nguyen, T. M., Xia, H., Strohmer, T., Bertozzi, A., Osher, S., & Wang, B. GRAND++: Graph neural diffusion with a source term. ICLR, 2021.
Lin, A. T., Fung, S. W., Li, W., Nurbekyan, L., & Osher, S. Alternating the population and control neural networks to solve high-dimensional stochastic mean-field games. Proceedings of the National Academy of Sciences (PNAS), 2021.
Lin, A. T., Li, W., Osher, S., & Montúfar, G. Wasserstein proximal of GANs. In International Conference on Geometric Science of Information, 2021.
Balestriero, R., & Baraniuk, R. G. Mad Max: Affine Spline Insights into Deep Learning. Proceedings of the IEEE (special issue on Advances in Machine Learning and Deep Neural Networks), 2021.
Baraniuk, R. G., Donoho, L., & Gavish, M. The Science of Deep Learning. Proceedings of the National Academy of Sciences (PNAS), PNAS:117:48:30029, 2020.
Ongie, G., Jalal, A., Metzler, C. A., Baraniuk, R. G., Dimakis, A. G., & Willett, R. Deep Learning Techniques for Inverse Problems in Imaging. IEEE Journal on Selected Topics in Information Theory (inaugural issue), 2020.
Lejeune, D., Javadi, H., & Baraniuk, R. G. The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization. NeurIPS, 2021.
Chaudhri, V. K., Boggess, M., Aung, H. L., Basu Mallick, D., Waters, A. C., & Baraniuk, R. G. Bootstrapping Ontology Graphs. Automated Knowledge Base Construction, 2021.
Alemohammad, S., Balestriero, R., Wang, J., & Baraniuk, R. G. The Recurrent Neural Tangent Kernel. ICLR, 2021.
Yao, T., LeJeune, D., Javadi, H., Baraniuk, R. G., & Allen, G. A. Minipatch Learning as Implicit Ridge- Like Regularization. IEEE International Conference on Big Data and Smart Computing, 2021.
Balestriero, R., Paris, S., & Baraniuk, R. G. Analytical Probability Distributions and EM-Learning for Deep Generative Networks. NeurIPS, 2020.
Nguyen, T. M., Ho, N., Patel, A., Anandkumar A., Jordan, M., & Baraniuk, R. G. A Bayesian Perspective of Convolutional Neural Networks through a Deconvolutional Generative Model. submitted to Journal of Machine Learning Research, arXiv preprint arxiv:1811.02657, 2020.
Dar, Y. and Baraniuk, R. G. Double Double Descent: On Generalization Errors in Transfer Learning between LInear Regression Tasks. arXiv preprint arXiv:2006.07002, 2021.
Luzi, L., Dar, Y., & Baraniuk, R. Double Descent and Other Interpolation Phenomena in GANs. arXiv preprint arXiv:2106.04003, 2021.
Dar, Y., Muthukumar, V. & Baraniuk, R. G. A farewell to the bias-variance tradeoff? an overview of the theory of overparameterized machine learning. arXiv:2109.02355, 2021.
Kota, P. K., LeJeune, D., Drezek, R. A., & Baraniuk, R. G. Extreme Compressed Sensing of Poisson Rates from Multiple Measurements. arXiv preprint arXiv:2103.08711, 2021.
Green, A., Balakrishnan, S., Tibshirani, R. Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:2602-2610, 2021.
Patik, P., Wei, Y., Rinaldo, A., Tibshirani, R. Uniform Consistency of Cross-Validation Estimators for High-Dimensional Ridge Regression. Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:3178-3186, 2021.
Goldblum, M., Reich, S., Fowl, L., Ni, R., Cherepanova, V., & Goldstein, T. Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks. ICML 2020, arXiv preprint arXiv:2002.06753, 2020.
Huang, W., Emam, Z., Goldblum, M., Fowl, L., Terry, J.K., Huang, F., & Goldstein, T. Understanding Generalization through Visualizations. arXiv preprint arXiv:1906.03291, 2019.
Pope, P., Zhu, C., Abdelkader, A., Goldblum, M., & Goldstein, T. The Intrinsic Dimension of Images and Its Impact on Learning. ICLR, 2020.
Sankararaman, K. A., De, So., Xu, Z., Huang, W. R., & Goldstein, T. The Impact of Neural Network Overparameterization on Gradient Confusion and Stochastic Gradient Descent. ICML, 2020.
Parhi, R. and Nowak, R. What Kinds of Functions do Deep Neural Networks Learn? Insights from Variational Spline Theory. arXiv preprint arXiv:2105.03361, 2021.
Daubechies, I., DeVore, R., Foucart, S., Hanin, B., & Petrova, G. Nonlinear Approximation and (Deep) ReLU Networks. arXiv preprint arXiv:1905.02199, 2020.
DeVore, R., Hanin, B., & Petrova, G. Neural Network Approximation. arXiv preprint arXiv:2012.14501, 2020.
Shah, A., Mishra, S., Bansal, A., Chen, J.C., Chellappa, R., & Srivastava, A. Pose And Joint-Aware Action Recognition. Winter Conference on Computer Vision, 2022.
Pramanick, S., Roy, A., & Patel, V.M, Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection. Winter Conference on Computer Vision, 2022.
Nguyen, T. M., Baraniuk, R. G., Bertozzi, A., Osher, S., & Wang, B. MomentumRNN: Integrating Momentum into Recurrent Neural Networks. NeurIPS, 2020.
Nguyen, T. M., Suliafu, V., Osher, S. J., Chen, L., & Wang, B. FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention. NeurIPS, 2021.
Xia, H., Suliafu, V., Ji, H., Nguyen, T. M., Bertozzi, A. L., Osher, S. J., & Wang, B. Heavy Ball Neural Ordinary Differential Equations. NeurIPS, 2021.
Wang, B., Nguyen, T. M., Bertozzi, A. L., Baraniuk, R. G., & Osher, S. J. Scheduled Restart Momentum for Accelerated Stochastic Gradient Descent. arXiv preprint arXiv:2002.10583, 2020.
Kyrillidis, A., Shrivastava, A., Vardi, M. Y., & Zhang Z. Solving Hybrid Boolean Constraints in Continuous Space via Multilinear Fourier Expansions. Artif. Intell. 299: 103559, 2021.
Kyrillidis, A., Vardi M. Y., Zhang, Z. On Continuous Local BDD-Based Search for Hybrid SAT Solving. AAAI: 3841-3850, 2021.
Dudek, J. M., Phan, V. H, N., & Vardi, M. Y. ProCount: Weighted Projected Model Counting with Graded Project-Join Trees. SAT: 152-170, 2021.