| Captum: A unified and generic model interpretability library for PyTorch. arXiv 2020 N Kokhlikyan, V Miglani, M Martin, E Wang, B Alsallakh, J Reynolds, ... arXiv preprint arXiv:2009.07896, 0 | 949 | |
| Mind the Pad--CNNs Can Develop Blind Spots B Alsallakh, N Kokhlikyan, V Miglani, J Yuan, O Reblitz-Richardson arXiv preprint arXiv:2010.02178, 2020 | 100 | 2020 |
| Pytorch captum N Kokhlikyan, V Miglani, M Martin, E Wang, J Reynolds, A Melnikov, ... GitHub repository, 2019 | 53 | 2019 |
| Xair: A framework of explainable ai in augmented reality X Xu, A Yu, TR Jonker, K Todi, F Lu, X Qian, JM Evangelista Belo, T Wang, ... Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems …, 2023 | 45 | 2023 |
| Captum: A unified and generic model interpretability library for PyTorch. arXiv N Kokhlikyan, V Miglani, M Martin, E Wang, B Alsallakh, J Reynolds, ... arXiv preprint arXiv:2009.07896 2, 2020 | 42 | 2020 |
| Instantiating resources of an IT-service JE Arwe, G Breiter, M Chodorowski, F Dross, N Kokhlikyan, HA Le, ... US Patent 9,203,774, 2015 | 29 | 2015 |
| Investigating saturation effects in integrated gradients V Miglani, N Kokhlikyan, B Alsallakh, M Martin, O Reblitz-Richardson arXiv preprint arXiv:2010.12697, 2020 | 27 | 2020 |
| Using captum to explain generative language models V Miglani, A Yang, AH Markosyan, D Garcia-Olano, N Kokhlikyan arXiv preprint arXiv:2312.05491, 2023 | 23 | 2023 |
| Method cards for prescriptive machine-learning transparency D Adkins, B Alsallakh, A Cheema, N Kokhlikyan, E McReynolds, P Mishra, ... Proceedings of the 1st International Conference on AI Engineering: Software …, 2022 | 20 | 2022 |
| Prescriptive and descriptive approaches to machine-learning transparency D Adkins, B Alsallakh, A Cheema, N Kokhlikyan, E McReynolds, P Mishra, ... CHI conference on human factors in computing systems extended abstracts, 1-9, 2022 | 14 | 2022 |
| Investigating sanity checks for saliency maps with image and text classification N Kokhlikyan, V Miglani, B Alsallakh, M Martin, O Reblitz-Richardson arXiv preprint arXiv:2106.07475, 2021 | 14 | 2021 |
| Debugging the internals of convolutional networks B Alsallakh, N Kokhlikyan, V Miglani, S Muttepawar, E Wang, S Zhang, ... eXplainable AI approaches for debugging and diagnosis., 2021 | 13 | 2021 |
| Instantiating resources of an IT-service JE Arwe, G Breiter, M Chodorowski, F Dross, N Kokhlikyan, HA Le, ... US Patent 9,432,247, 2016 | 13 | 2016 |
| Filtering electronic messages C Sathi, A Tarasov, D Mykhaylov, N Kokhlikyan, R Ivchenko US Patent 10,447,635, 2019 | 12 | 2019 |
| Building recommender systems with PyTorch D Mudigere, M Naumov, J Spisak, G Chauhan, N Kokhlikyan, A Singh, ... Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020 | 8 | 2020 |
| Fine-grained interpretation and causation analysis in deep NLP models H Sajjad, N Kokhlikyan, F Dalvi, N Durrani arXiv preprint arXiv:2105.08039, 2021 | 7 | 2021 |
| PyTorch Captum. 2019 N Kokhlikyan, V Miglani, M Martin, E Wang, J Reynolds, A Melnikov, ... URL: https://github. com/pytorch/captum, 0 | 6 | |
| A tour of visualization techniques for computer vision datasets B Alsallakh, P Bhattacharya, V Feng, N Kokhlikyan, O Reblitz-Richardson, ... arXiv preprint arXiv:2204.08601, 2022 | 5 | 2022 |
| Instantiating resources of an IT-service JE Arwe, G Breiter, M Chodorowski, F Dross, N Kokhlikyan, HA Le, ... US Patent 9,515,866, 2016 | 5 | 2016 |
| Error discovery by clustering influence embeddings F Wang, J Adebayo, S Tan, D Garcia-Olano, N Kokhlikyan Advances in Neural Information Processing Systems 36, 41765-41777, 2023 | 3 | 2023 |
Bilal AlsallakhVoxel AIПотвърден имейл адрес: voxelai.com
