| A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends S Sengupta, S Basak, P Saikia, S Paul, V Tsalavoutis, FD Atiah, V Ravi, ... Knowledge-Based Systems 194, 33, 2020 | 384 | 2020 |
| Vision transformers are robust learners S Paul*, PY Chen*, *equal contribution Proceedings of the AAAI conference on Artificial Intelligence 36 (2), 2071-2081, 2022 | 292 | 2022 |
| Diffusers: State-of-the-art diffusion models P Von Platen, S Patil, A Lozhkov, P Cuenca, N Lambert, K Rasul, ... | 278 | 2022 |
| Peft: State-of-the-art parameter-efficient fine-tuning methods S Mangrulkar, S Gugger, L Debut, Y Belkada, S Paul, B Bossan URL: https://github. com/huggingface/peft, 2022 | 168 | 2022 |
| Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning S Paul*, S Ganju*, *equal contribution NeurIPS Tackling Climate Change with Machine Learning Workshop, 2021 | 27 | 2021 |
| Fast and accurate quantized camera scene detection on smartphones, mobile ai 2021 challenge: Report A Ignatov, G Malivenko, R Timofte Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 20 | 2021 |
| G-SimCLR: Self-Supervised Contrastive Learning with Guided Projection via Pseudo Labelling S Chakraborty*, A Roy Gosthipaty*, S Paul*, *equal contribution https://ieeexplore.ieee.org/document/9346544, 2020 | 17 | 2020 |
| PEFT: state-of-the-art parameter-efficient fine-tuning methods (2022) S Mangrulkar, S Gugger, L Debut, Y Belkada, S Paul, B Bossan URL https://github. com/huggingface/peft, 2023 | 16 | 2023 |
| A novel transfer learning-based missing value imputation on discipline diverse real test datasets—a comparative study with different machine learning algorithms J Gupta, S Paul, A Ghosh Emerging Technologies in Data Mining and Information Security: Proceedings …, 2019 | 11 | 2019 |
| Pixart-{\delta}: Fast and controllable image generation with latent consistency models J Chen, Y Wu, S Luo, E Xie, S Paul, P Luo, H Zhao, Z Li arXiv preprint arXiv:2401.05252, 2024 | 6 | 2024 |
| Using lora for efficient stable diffusion fine-tuning P Cuenca, S Paul | 6 | 2023 |
| Progressive knowledge distillation of stable diffusion xl using layer level loss Y Gupta, VV Jaddipal, H Prabhala, S Paul, P Von Platen arXiv preprint arXiv:2401.02677, 2024 | 4 | 2024 |
| A CFS–DNN-based intrusion detection system S Paul, C Banerjee, M Ghoshal Advances in Communication, Devices and Networking: Proceedings of ICCDN 2017 …, 2018 | 4 | 2018 |
| Hands-On Python Deep Learning for the Web A Singh, S Paul https://www.packtpub.com/in/data/hands-on-python-deep-learning-for-web, 2020 | 3* | 2020 |
| Instruction-tuning Stable Diffusion with InstructPix2Pix S Paul https://huggingface.co/blog/instruction-tuning-sd, 2023 | 2 | 2023 |
| Getting it Right: Improving Spatial Consistency in Text-to-Image Models A Chatterjee, GBM Stan, E Aflalo, S Paul, D Ghosh, T Gokhale, L Schmidt, ... arXiv preprint arXiv:2404.01197, 2024 | 1 | 2024 |
| DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Model S Marjit, H Singh, N Mathur, S Paul, CM Yu, PY Chen arXiv preprint arXiv:2402.17412, 2024 | 1 | 2024 |
| Practical Adversarial Robustness in Deep Learning: Problems and Solutions PY Chen, S Paul IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021 | 1 | 2021 |
| Margin-aware Preference Optimization for Aligning Diffusion Models without Reference J Hong*, S Paul*, N Lee, K Rasul, J Thorne, J Jeong, *equal contribution arXiv e-prints, arXiv: 2406.06424, 2024 | | 2024 |
| All Things ViTs: Understanding and Interpreting Attention in Vision H Chefer*, S Paul*, *equal contribution IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023 | | 2023 |
Pin-Yu ChenPrincipal Research Scientist, IBM Research AI; MIT-IBM Watson AI Lab; RPI-IBM AIRC在 ibm.com 的电子邮件经过验证
