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CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

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Zhang, M., Badkundri, R., Talbot, M. B., Zawar, R. & Kreiman, G. Hypothesis-driven Online Video Stream Learning with Augmented Memory. arXiv (2021). doi:10.48550/arXiv.2104.02206

2104.02206.pdf (2.25 MB)

Zhang, M. et al. Look twice: A generalist computational model predicts return fixations across tasks and species. PLOS Computational Biology 18, e1010654 (2022).

journal.pcbi_.1010654.pdf (4.51 MB)

Zhang, C., Voinea, S., Evangelopoulos, G., Rosasco, L. & Poggio, T. Discriminative Template Learning in Group-Convolutional Networks for Invariant Speech Representations. INTERSPEECH-2015 (International Speech Communication Association (ISCA), 2015). at <http://www.isca-speech.org/archive/interspeech_2015/i15_3229.html>

Zaslavsky, N., Maldonado, M. & Culbertson, J. Let's talk (efficiently) about us: Person systems achieve near-optimal compression. Proceedings of the Annual Meeting of the Cognitive Science Society 43, (2021).

Zaslavsky, N., Garvin, K., Kemp, C., Tishby, N. & Regier, T. The evolution of color naming reflects pressure for efficiency: Evidence from the recent pastAbstract. Journal of Language Evolution (2022). doi:10.1093/jole/lzac001

Zaslavsky, N., Hu, J. & Levy, R. Emergence of Pragmatic Reasoning From Least-Effort Optimization . 13th International Conference on the Evolution of Language (EvoLang) (2020).

Zarco, W. & Freiwald, W. A. Visual Features for Invariant Coding by Face Selective Neurons . 2019 Conference on Cognitive Computational Neuroscience (CCN) (2019).

Zador, A. et al. Catalyzing next-generation Artificial Intelligence through NeuroAIAbstract. Nature Communications 14, (2023).

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Yuille, A. & Mottaghi, R. Complexity of Representation and Inference in Compositional Models with Part Sharing. (2015).

CBMM Memo 031.pdf (1.14 MB)

Yuille, A. & Liu, C. Deep Nets: What have they ever done for Vision?. (2018).

CBMM-Memo-088.pdf (7.88 MB)

Yu, H., Siddharth, N., Barbu, A. & Siskind, J. Mark. A Compositional Framework for Grounding Language Inference, Generation, and Acquisition in Video. (2015). doi:doi:10.1613/jair.4556

Yu, C., Burgess, N., Sahani, M. & Gershman, S. J. Successor-Predecessor Intrinsic Exploration. Advances in Neural Information Processing Systems 36 (NeurIPS 2023) (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/hash/e6f2b968c4ee8ba260cd7077e39590dd-Abstract-Conference.html>

Yildirim, I., Siegel, M. & Tenenbaum, J. B. Perceiving Fully Occluded Objects with Physical Simulation. Cognitive Science Conference (CogSci) (2015).

Yildirim, I. & Janner, M. Causal and compositional generative models in online perception. 39th Annual Conference of the Cognitive Science Society (Belledonne, M., Wallraven, C., Freiwald, W. A. & Tenenbaum, J. B.) (2017).

yildirim_janner_2_1.pdf (6.88 MB)

Yildirim, I., Belledonne, M., Freiwald, W. A. & Tenenbaum, J. B. Efficient inverse graphics in biological face processing. Science Advances 6, eaax5979 (2020).

eaax5979.full_.pdf (3.22 MB)

Yildirim, I., Siegel, M. H., Soltani, A. A., Chaudhuri, S. Ray & Tenenbaum, J. B. Perception of 3D shape integrates intuitive physics and analysis-by-synthesis. Nature Human Behaviour (2023). doi:10.1038/s41562-023-01759-7

Yildirim, I., Wu, J., Kanwisher, N. & Tenenbaum, J. B. An integrative computational architecture for object-driven cortex. Current Opinion in Neurobiology 55, 73 - 81 (2019).

Yildirim, I., Kulkarni, T., Freiwald, W. A. & Tenenbaum, J. B. Efficient and robust analysis-by-synthesis in vision: A computational framework, behavioral tests, and modeling neuronal representations. Annual Conference of the Cognitive Science Society (2015).

yildirimetal_cogsci15.pdf (3.22 MB)

Yildirim, I., Gerstenberg, T., Saeed, B., Toussant, M. & Tenenbaum, J. B. Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints. Proceedings of the 39th Annual Conference of the Cognitive Science Society (2017).

Physical problem solving Joint planning with symbolic, geometric, and dynamic constraints, Yildirim et al., 2017.pdf (2.46 MB)

Yildirim, I., Freiwald, W. A. & J., T. Efficient inverse graphics in biological face processing. bioRxiv (2018). at <https://www.biorxiv.org/content/early/2018/04/02/282798>

Yildirim, I., Kulkarni, T., Freiwald, W. A. & Tenenbaum, J. B. Explaining Monkey Face Patch System as Efficient Analysis-by-Synthesis. (2014).

yildirimetal_cosyne15.pdf (313.57 KB)

Yildirim, I. et al. Causal and compositional generative models in online perception. 39th Annual Meeting of the Cognitive Science Society - COGSCI 2017 (2017). at <https://mindmodeling.org/cogsci2017/papers/0266/index.html>

Yang, C. et al. Evolutionary and biomedical insights from a marmoset diploid genome assembly. Nature (2021). doi:10.1038/s41586-021-03535-x

Yang, Z. & Freiwald, W. A. Joint encoding of facial identity, orientation, gaze, and expression in the middle dorsal face areaSignificance. Proceedings of the National Academy of Sciences 118, (2021).

Yang, S., Bill, J., Drugowitsch, J. & Gershman, S. J. Human visual motion perception shows hallmarks of Bayesian structural inference. Scientific Reports 11, (2021).

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