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Found 912 results
Author [ Title(Asc)] Type Year
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Xia, F., Wang, P., Chen, L. -chieh & Yuille, A. Zoom Better to See Clearer: Human Part Segmentation with Auto Zoom Net. ECCV (2016).
Xia, F., Wang, P., Chen, L. -chieh & Yuille, A. Zoom better to see clearer: Human and object parsing with hierarchical auto-zoom net. ECCV (2016).PDF icon auto-zoom_net.pdf (5.77 MB)
Tejwani, R. et al. Zero-shot linear combinations of grounded social interactions with Linear Social MDPs. Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI) (2023).
Y
Dillon, M. R. & Spelke, E. S. Young children's use of distance and angle information during map reading. SRCD (2017).
Dillon, M. R. & Spelke, E. S. Young Children’s Use of Surface and Object Information in Drawings of Everyday Scenes. Child Development (2016). doi:10.1111/cdev.12658
Dillon, M. R. & Spelke, E. S. Young children’s automatic and alternating use of scene and object information in spatial symbols. Budapest CEU Conference on Cognitive Development (2015).
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Xiao, W. & Kreiman, G. XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization. PLOS Computational Biology 16, e1007973 (2020).PDF icon gk7791.pdf (2.39 MB)
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Ellis, K. et al. Write, Execute, Assess: Program Synthesis with a REPL. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 9116-write-execute-assess-program-synthesis-with-a-repl.pdf (3.9 MB)
Mendoza-Halliday, D., Torres, S. & Martinez-Trujillo, J. Mechanisms of Sensory Working Memory: Attention and Performance XXV. (Elsevier Inc. , 2015). at <https://www.sciencedirect.com/book/9780128013717/mechanisms-of-sensory-working-memory>
Voinea, S., Zhang, C., Evangelopoulos, G., Rosasco, L. & Poggio, T. Word-level Invariant Representations From Acoustic Waveforms. INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association (International Speech Communication Association (ISCA), 2014). at <http://www.isca-speech.org/archive/interspeech_2014/i14_2385.html>
Lin, H. & Tegmark, M. Why does deep and cheap learning work so well?. Journal of Statistical Physics 168, 1223–1247 (2017).PDF icon 1608.08225.pdf (2.14 MB)
Dobs, K., Kell, A. J. E., Martinez-Trujillo, J., Cohen, M. & Kanwisher, N. Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks . Conference on Cognitive Computational Neuroscience (2020).
Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B. & Liao, Q. Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. International Journal of Automation and Computing 1-17 (2017). doi:10.1007/s11633-017-1054-2PDF icon art%3A10.1007%2Fs11633-017-1054-2.pdf (1.68 MB)
Fisher, C. & Freiwald, W. A. Whole-agent selectivity within the macaque face-processing system. Proceedings of the National Academy of Sciences (PNAS) 112, (2015).PDF icon Authors' last version of article.  (3.1 MB)
Dasgupta, I., Schulz, E. & Gershman, S. J. Where do hypotheses come from?. (2016).PDF icon CBMM-Memo-056-v2.pdf (733.35 KB)
Bomatter, P. et al. When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes. International Conference on Computer Vision (ICCV) (2021). doi:10.1109/iccv48922.2021.00032PDF icon Bomatter_When_Pigs_Fly_Contextual_Reasoning_in_Synthetic_and_Natural_Scenes_ICCV_2021_paper.pdf (3.24 MB)
Kool, W., Cushman, F. A. & Gershman, S. J. When Does Model-Based Control Pay Off?. PLoS Comput Biol 12, e1005090 (2016).PDF icon KoolEtAl_PLOS_CB.PDF (5.85 MB)
Gao, T., Harari, D., Tenenbaum, J. B. & Ullman, S. When Computer Vision Gazes at Cognition. (2014).PDF icon CBMM-Memo-025.pdf (3.78 MB)
Mhaskar, H., Liao, Q. & Poggio, T. When and Why Are Deep Networks Better Than Shallow Ones?. AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence (2017).
Madan, S. et al. When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations. Nature Machine Intelligence 4, 146 - 153 (2022).
Li, C. & Deza, A. What Matters In Branch Specialization? Using a Toy Task to Make Predictions. Shared Visual Representations in Human and Machine Intelligence (SVRHM) Workshop at NeurIPS (2021). at <https://openreview.net/forum?id=0kPS1i6wict>
Pouncy, T., Tsividis, P. & Gershman, S. J. What Is the Model in Model‐Based Planning?. Cognitive Science 45, (2021).
Isik, L. et al. What is changing when: decoding visual information in movies from human intracranial recordings. NeuroImage 180, Part A, 147-159 (2018).PDF icon Human neurophysiological responses during movies (2.78 MB)
Isik, L., Singer, J., Madsen, J., Kanwisher, N. & Kreiman, G. What is changing when: Decoding visual information in movies from human intracranial recordings. Neuroimage (2017). doi:https://doi.org/10.1016/j.neuroimage.2017.08.027
Tiwary, K. et al. What if Eye..? Computationally Recreating Vision Evolution. arXiv (2025). at <https://arxiv.org/abs/2501.15001>PDF icon 2501.15001v1.pdf (5.2 MB)

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