| Learning visual importance for graphic designs and data visualizations Z Bylinskii, NW Kim, P O'Donovan, S Alsheikh, S Madan, H Pfister, ... Proceedings of the 30th Annual ACM symposium on user interface software and …, 2017 | 193 | 2017 |
| When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations S Madan, T Henry, J Dozier, H Ho, N Bhandari, T Sasaki, F Durand, ... Nature Machine Intelligence 4 (2), 146-153, 2022 | 77* | 2022 |
| Parsing and Summarizing Infographics with Synthetically Trained Icon Detection S Madan, Z Bylinskii, C Nobre, M Tancik, A Recasens, K Zhong, ... 2021 IEEE 14th Pacific Visualization Symposium (PacificVis), 31-40, 2021 | 45* | 2021 |
| Synthetically trained icon proposals for parsing and summarizing infographics S Madan, Z Bylinskii, M Tancik, A Recasens, K Zhong, S Alsheikh, ... arXiv preprint arXiv:1807.10441, 2018 | 27 | 2018 |
| In-distribution adversarial attacks on object recognition models using gradient-free search S Madan, T Sasaki, H Pfister, TM Li, X Boix Transactions on Machine Learning Research, 2025 | 22* | 2025 |
| When pigs fly: Contextual reasoning in synthetic and natural scenes P Bomatter, M Zhang, D Karev, S Madan, C Tseng, G Kreiman Proceedings of the IEEE/CVF International Conference on Computer Vision, 255-264, 2021 | 20 | 2021 |
| Exploiting the recognition code for elucidating the mechanism of zinc finger protein-DNA interactions S Dutta, S Madan, D Sundar BMC genomics 17, 109-125, 2016 | 12 | 2016 |
| An ensemble micro neural network approach for elucidating interactions between zinc finger proteins and their target DNA S Dutta, S Madan, H Parikh, D Sundar Bmc Genomics 17, 97-107, 2016 | 11 | 2016 |
| Three approaches to facilitate invariant neurons and generalization to out-of-distribution orientations and illuminations A Sakai, T Sunagawa, S Madan, K Suzuki, T Katoh, H Kobashi, H Pfister, ... Neural Networks 155, 119-143, 2022 | 8 | 2022 |
| & Hertzmann, A.(2017, October). Learning visual importance for graphic designs and data visualizations Z Bylinskii, NW Kim, P O'Donovan, S Alsheikh, S Madan, H Pfister Proceedings of the 30th Annual ACM symposium on user interface software and …, 0 | 5 | |
| Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations A Cooper, X Boix, D Harari, S Madan, H Pfister, T Sasaki, P Sinha arXiv preprint arXiv:2109.13445, 2021 | 4* | 2021 |
| Effects of title wording on memory of trends in line graphs A Newman, Z Bylinskii, S Haroz, S Madan, F Durand, A Oliva Journal of Vision 18 (10), 837-837, 2018 | 4 | 2018 |
| Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex S Madan, W Xiao, M Cao, H Pfister, M Livingstone, G Kreiman Advances in Neural Information Processing Systems (NeurIPS), 2024, 2024 | 3 | 2024 |
| Improving out-of-distribution generalization by mimicking the human visual diet. S Madan, Y Li, M Zhang, H Pfister, G Kreiman The First Workshop on NeuroAI@ NeurIPS2024, 2024 | 3* | 2024 |
| Human or Machine? Turing Tests for Vision and Language M Zhang, G Dellaferrera, A Sikarwar, M Armendariz, N Mudrik, P Agrawal, ... arXiv preprint arXiv:2211.13087, 2022 | 2 | 2022 |
| The Indoor Training-Effect: unexpected gains from distribution shifts in the transition function. S Bono, S Madan, I Grover, M Yasueda, C Breazeal, H Pfister, G Kreiman Proceedings of the AAAI conference on artificial intelligence (AAAI 2025), 2024 | 1* | 2024 |
| Out-of-Distribution generalization behavior of DNN-based encoding models for the visual cortex S Madan, M Cao, W Xiao, H Pfister, G Kreiman Journal of Vision 24 (10), 1148-1148, 2024 | | 2024 |
| DNN-based encoding models for the visual cortex fail to generalize out of the training data distribution. S Madan, W Xiao, M Cao, H Pfister, M Livingstone, G Kreiman Computational Cognitive Neuroscience (CCN), 2024 | | 2024 |
| ZoomMaps: Using Zoom to Capture Areas of Interest on Images Z Bylinskii, A Newman, M Tancik, S Madan, F Durand, A Oliva Journal of Vision 19 (10), 149-149, 2019 | | 2019 |
| DNN-based encoding models for the visual cortex fail to generalize out of the training data distribution. S Madan, W Xiao, M Cao, H Pfister, M Livingstone, G Kreiman | | |
Hanspeter PfisterAn Wang Professor of Computer Science, Harvard UniversityVerified email at seas.harvard.edu
