| Cold diffusion: Inverting arbitrary image transforms without noise A Bansal, E Borgnia, HM Chu, JS Li, H Kazemi, F Huang, M Goldblum, ... Advances in Neural Information Processing Systems (NeurIPS), 2023 | 155 | 2023 |
| Universal guidance for diffusion models A Bansal, HM Chu, A Schwarzschild, S Sengupta, M Goldblum, J Geiping, ... The Twelfth International Conference on Learning Representations (ICLR) 2024, 2024 | 63* | 2024 |
| Transfer learning with deep tabular models R Levin, V Cherepanova, A Schwarzschild, A Bansal, CB Bruss, ... arXiv preprint arXiv:2206.15306, 2022 | 45* | 2022 |
| Universal guidance for diffusion models A Bansal, HM Chu, A Schwarzschild, S Sengupta, M Goldblum, J Geiping, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 44 | 2023 |
| Can neural nets learn the same model twice? investigating reproducibility and double descent from the decision boundary perspective G Somepalli, L Fowl, A Bansal, P Yeh-Chiang, Y Dar, R Baraniuk, ... Proceedings of the ieee/cvf conference on computer vision and pattern …, 2022 | 44 | 2022 |
| Preventing unauthorized use of proprietary data: Poisoning for secure dataset release L Fowl, P Chiang, M Goldblum, J Geiping, A Bansal, W Czaja, T Goldstein arXiv preprint arXiv:2103.02683, 2021 | 35 | 2021 |
| End-to-end algorithm synthesis with recurrent networks: Extrapolation without overthinking A Bansal, A Schwarzschild, E Borgnia, Z Emam, F Huang, M Goldblum, ... Advances in Neural Information Processing Systems 35, 20232-20242, 2022 | 30* | 2022 |
| Certified neural network watermarks with randomized smoothing A Bansal, P Chiang, MJ Curry, R Jain, C Wigington, V Manjunatha, ... International Conference on Machine Learning, 1450-1465, 2022 | 27 | 2022 |
| Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries Y Wen, A Bansal, H Kazemi, E Borgnia, M Goldblum, J Geiping, ... International Conference on Learning Representations (ICLR) 2023, 2023 | 15 | 2023 |
| Loss landscapes are all you need: Neural network generalization can be explained without the implicit bias of gradient descent P Chiang, R Ni, DY Miller, A Bansal, J Geiping, M Goldblum, T Goldstein The Eleventh International Conference on Learning Representations, 2022 | 12 | 2022 |
| Datasets for studying generalization from easy to hard examples A Schwarzschild, E Borgnia, A Gupta, A Bansal, Z Emam, F Huang, ... arXiv preprint arXiv:2108.06011, 2021 | 6 | 2021 |
| MetaBalance: high-performance neural networks for class-imbalanced data A Bansal, M Goldblum, V Cherepanova, A Schwarzschild, CB Bruss, ... arXiv preprint arXiv:2106.09643, 2021 | 5 | 2021 |
| Pag-net: Progressive attention guided depth super-resolution network A Bansal, S Jonna, RR Sahay arXiv preprint arXiv:1911.09878, 2019 | 2 | 2019 |
| Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion H Souri, A Bansal, H Kazemi, L Fowl, A Saha, J Geiping, AG Wilson, ... arXiv preprint arXiv:2403.16365, 2024 | | 2024 |
| Just How Flexible are Neural Networks in Practice? R Shwartz-Ziv, M Goldblum, A Bansal, CB Bruss, Y LeCun, AG Wilson | | 2023 |
| Algorithm Design for Learned Algorithms A Schwarzschild, SM McLeish, A Bansal, G Diaz, A Stein, A Chandnani, ... | | |
Tom GoldsteinVolpi-Cupal Professor of Computer Science, University of MarylandVerified email at cs.umd.edu
