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Conference Paper

Adhya, D. et al. Shared gene co-expression networks in autism from induced pluripotent stem cell (iPSC) neurons. BioRxiv (2018). doi:10.1101/349415

Zhang, Z. et al. Single-Shot Object Detection with Enriched Semantics. Conference on Computer Vision and Pattern Recognition (CVPR) (2018). at <http://cvpr2018.thecvf.com/>

Dillon, M. R. & Spelke, E. S. Spatial cognition across development. SRCD (2017).

Palmer, I., Rouditchenko, A., Barbu, A., Katz, B. & Glass, J. Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset. Interspeech 2021 (2021). doi:10.21437/Interspeech.2021

Cheng, E., Kuo, Y. - L., Cases, I., Katz, B. & Barbu, A. Spontaneous sign emergence in humans and machines through an embodied communication game. JCoLE Workshop (2022).

Bergen, L. & Goodman, N. D. The strategic use of noise in pragmatic reasoning. (2014).

Gaziv, G., Lee, M. J. & DiCarlo, J. J. Strong and Precise Modulation of Human Percepts via Robustified ANNs. NeurIPS 2023 (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/hash/d00904cebc0d5b69fada8ad33d0f1422-Abstract-Conference.html>

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>

Soltani, A. Arsalan, Huang, H., Wu, J., Kulkarni, T. & Tenenbaum, J. B. Synthesizing 3D Shapes via Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017). doi:10.1109/CVPR.2017.269

Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks.pdf (2.86 MB)

Mao, J. et al. Temporal and Object Quantification Networks. Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (Zhou, Z. - H.) (2021). doi:10.24963/ijcai.2021/386

0386.pdf (472.5 KB)

Paul, R., Barbu, A., Felshin, S., Katz, B. & Roy, N. Temporal Grounding Graphs for Language Understanding with Accrued Visual-Linguistic Context. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI 2017) (2017). at <c>

Schrimpf, M., Sato, F., Sanghavi, S. & DiCarlo, J. J. Temporal information for action recognition only needs to be integrated at a choice level in neural networks and primates . COSYNE (2020).

Jozwik, K. M., Schrimpf, M., Kanwisher, N. & DiCarlo, J. J. To find better neural network models of human vision, find better neural network models of primate vision. BioRxiv (2019). at <https://www.biorxiv.org/content/10.1101/688390v1.full>

Eisape, T., Levy, R., Tenenbaum, J. B. & Zaslavsky, N. Toward human-like object naming in artificial neural systems . International Conference on Learning Representations (ICLR 2020), Bridging AI and Cognitive Science workshop (2020).

Tacchetti, A., Voinea, S. & Evangelopoulos, G. Trading robust representations for sample complexity through self-supervised visual experience. Advances in Neural Information Processing Systems 31 (Bengio, S. et al.) 9640–9650 (Curran Associates, Inc., 2018). at <http://papers.nips.cc/paper/8170-trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf>

trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf (3.32 MB)

NeurIPS2018_Poster.pdf (6.12 MB)

Mao, J., Xu, J., Jing, Y. & Yuille, A. Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images. NIPS 2016 (2016).

6590-training-and-evaluating-multimodal-word-embeddings-with-large-scale-web-annotated-images.pdf (1.57 MB)

Kuo, Y. - L. et al. Trajectory Prediction with Linguistic Representations. 2022 IEEE International Conference on Robotics and Automation (ICRA) (2022). doi:10.1109/ICRA46639.2022.9811928

Du, Y., Smith, K. A., Ullman, T., Tenenbaum, J. B. & Wu, J. Unsupervised Discovery of 3D Physical Objects. International Conference on Learning Representations (2021). at <https://openreview.net/forum?id=lf7st0bJIA5>

Lotter, W., Kreiman, G. & Cox, D. Unsupervised Learning of Visual Structure using Predictive Generative Networks. International Conference on Learning Representations (ICLR) (2016). at <http://arxiv.org/pdf/1511.06380v2.pdf>

Wang, B., Mayo, D., Deza, A., Barbu, A. & Conwell, C. On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation. Shared Visual Representations in Human and Machine Intelligence (SVRHM) Workshop at NeurIPS (2021). at <https://openreview.net/forum?id=Rpazl253IHb>

Subramaniam, V. et al. Using Multimodal DNNs to Study Vision-Language Integration in the Brain. ICLR 2023 (2023). at <https://openreview.net/pdf?id=OQQ1p0pFP4>

Dobs, K., Kell, A. J. E., Martinez-Trujillo, J., Cohen, M. & Kanwisher, N. Using task-optimized neural networks to understand why brains have specialized processing for faces . Computational and Systems Neurosciences (2020).

Phillips-Jones, T., Coronel, S. Otero, Sani, I. & Freiwald, W. A. A Virtual Reality Experimental Approach for Studying How the Brain Implements Attentive Behaviors. Tri-Institute 2019 Gateways to the Laboratory Summer Program (2019).

Rosenfeld, A. & Ullman, S. Visual Concept Recognition and Localization via Iterative Introspection. . Asian Conference on Computer Vision (2016).

Focusing on parts of interest (910.14 KB)

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