| Tabular transformers for modeling multivariate time series I Padhi, Y Schiff, I Melnyk, M Rigotti, Y Mroueh, P Dognin, J Ross, R Nair, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 77 | 2021 |
| Image captioning as an assistive technology: Lessons learned from vizwiz 2020 challenge P Dognin, I Melnyk, Y Mroueh, I Padhi, M Rigotti, J Ross, Y Schiff, ... Journal of Artificial Intelligence Research 73, 437-459, 2022 | 40 | 2022 |
| Optimizing functionals on the space of probabilities with input convex neural networks D Alvarez-Melis, Y Schiff, Y Mroueh arXiv preprint arXiv:2106.00774, 2021 | 39 | 2021 |
| Infodiffusion: Representation learning using information maximizing diffusion models Y Wang, Y Schiff, A Gokaslan, W Pan, F Wang, C De Sa, V Kuleshov International Conference on Machine Learning, 36336-36354, 2023 | 11 | 2023 |
| Predicting deep neural network generalization with perturbation response curves Y Schiff, B Quanz, P Das, PY Chen Advances in Neural Information Processing Systems 34, 21176-21188, 2021 | 11 | 2021 |
| Augmenting molecular deep generative models with topological data analysis representations Y Schiff, V Chenthamarakshan, SC Hoffman, KN Ramamurthy, P Das ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 10 | 2022 |
| Auditing and generating synthetic data with controllable trust trade-offs B Belgodere, P Dognin, A Ivankay, I Melnyk, Y Mroueh, A Mojsilovic, ... arXiv preprint arXiv:2304.10819, 2023 | 5 | 2023 |
| Characterizing the latent space of molecular deep generative models with persistent homology metrics Y Schiff, V Chenthamarakshan, KN Ramamurthy, P Das arXiv preprint arXiv:2010.08548, 2020 | 5 | 2020 |
| Caduceus: Bi-directional equivariant long-range dna sequence modeling Y Schiff, CH Kao, A Gokaslan, T Dao, A Gu, V Kuleshov arXiv preprint arXiv:2403.03234, 2024 | 4 | 2024 |
| Semi-autoregressive energy flows: exploring likelihood-free training of normalizing flows P Si, Z Chen, SS Sahoo, Y Schiff, V Kuleshov International Conference on Machine Learning, 31732-31753, 2023 | 4 | 2023 |
| Semi-parametric inducing point networks and neural processes R Rastogi, Y Schiff, A Hacohen, Z Li, I Lee, Y Deng, MR Sabuncu, ... arXiv preprint arXiv:2205.11718, 2022 | 4 | 2022 |
| Learning with stochastic orders C Domingo-Enrich, Y Schiff, Y Mroueh arXiv preprint arXiv:2205.13684, 2022 | 2 | 2022 |
| Alleviating noisy data in image captioning with cooperative distillation P Dognin, I Melnyk, Y Mroueh, I Padhi, M Rigotti, J Ross, Y Schiff arXiv preprint arXiv:2012.11691, 2020 | 2 | 2020 |
| Advancing dna language models: The genomics long-range benchmark CH Kao, E Trop, MK Polen, Y Schiff, BP de Almeida, A Gokaslan, ... ICLR 2024 Workshop on Machine Learning for Genomics Explorations, 2024 | 1 | 2024 |
| Cloud-based real-time molecular screening platform with molformer B Belgodere, V Chenthamarakshan, P Das, P Dognin, T Kurien, I Melnyk, ... Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 1 | 2022 |
| Gi and pal scores: Deep neural network generalization statistics Y Schiff, B Quanz, P Das, PY Chen arXiv preprint arXiv:2104.03469, 2021 | 1 | 2021 |
| DySLIM: Dynamics Stable Learning by Invariant Measure for Chaotic Systems Y Schiff, ZY Wan, JB Parker, S Hoyer, V Kuleshov, F Sha, ... arXiv preprint arXiv:2402.04467, 2024 | | 2024 |
| Using global-shape representations to generate a deep generative model P Das, YZ Schiff, EC Vijil, SC Hoffman, KN Ramamurthy US Patent App. 17/805,481, 2023 | | 2023 |
| Determining analytical model accuracy with perturbation response YZ Schiff, BL Quanz, P Das, PY Chen US Patent App. 17/715,684, 2023 | | 2023 |
| Semi-Autoregressive Energy Flows: Towards Determinant-Free Training of Normalizing Flows P Si, Z Chen, SS Sahoo, Y Schiff, V Kuleshov | | 2022 |
