Publication

Export 545 results:
2018
Yuille, A. & Liu, C. Deep Nets: What have they ever done for Vision?. (2018).PDF icon CBMM-Memo-088.pdf (7.88 MB)
Shen, W. et al. Deep Regression Forests for Age Estimation. (2018).PDF icon CBMM-Memo-085.pdf (2.2 MB)
Kuo, Y. - L., Barbu, A. & Katz, B. Deep sequential models for sampling-based planning. The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018) (2018). doi:10.1109/IROS.2018.8593947PDF icon kuo2018planning.pdf (637.67 KB)
Araya-Polo, M., Jennings, J., Adler, A. & Dahlke, T. Deep-learning tomography. The Leading Edge 37, 58 - 66 (2018).PDF icon TLE2018.pdf (1.9 MB)
Zhang, Z., Xie, C., Wang, J., Xie, L. & Yuille, A. DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion. (2018).PDF icon CBMM-Memo-083.pdf (2.32 MB)
Zhang, Z., Xie, C., Wang, J., Xie, L. & Yuille, A. DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion. Conference on Computer Vision and Pattern Recognition (CVPR) (2018). at <http://cvpr2018.thecvf.com/>
Toussaint, M., Allen, K., Smith, K. A. & Tenenbaum, J. B. Differentiable physics and stable modes for tool-use and manipulation planning. Robotics: Science and Systems 2018 (2018).PDF icon ToussaintEtAl_DiffPhysStable.pdf (1.97 MB)
Harari, D., Tenenbaum, J. B. & Ullman, S. Discovery and usage of joint attention in images. arXiv.org (2018). at <https://arxiv.org/abs/1804.04604>PDF icon 1804.04604v1.pdf (488.85 KB)
Meyers, E. Dynamic population coding and its relationship to working memory. Journal of Neurophysiology 120, 2260 - 2268 (2018).
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>
Belbute-Peres, Fde Avila, Smith, K. A., Allen, K., Tenenbaum, J. B. & Kolter, Z. End-to-end differentiable physics for learning and control. Advances in Neural Information Processing Systems 31 (NIPS 2018) (2018).PDF icon 7948-end-to-end-differentiable-physics-for-learning-and-control.pdf (794.17 KB)
Isik, L., Tacchetti, A. & Poggio, T. A fast, invariant representation for human action in the visual system. Journal of Neurophysiology (2018). doi:https://doi.org/10.1152/jn.00642.2017
Zhang, M. et al. Finding any Waldo with zero-shot invariant and efficient visual search. Nature Communications 9, (2018).
Dillon, M. R. & Spelke, E. S. From Map Reading to Geometric Intuitions. Developmental Psychology (2018). doi:http://dx.doi.org/10.1037/dev0000509
Ben-Yosef, G., Assif, L. & Ullman, S. Full interpretation of minimal images. Cognition 171, 65 - 84 (2018).
Ben-Yosef, G., Assif, L. & Ullman, S. Full interpretation of minimal images. Cognition 171, 65-84 (2018).PDF icon Full interpretation of minimal images.pdf (4.55 MB)
Ross, C., Barbu, A., Berzak, Y., Myanganbayar, B. & Katz, B. Grounding language acquisition by training semantic parsersusing captioned videos. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), (2018). at <http://aclweb.org/anthology/D18-1285>PDF icon Ross-et-al_ACL2018_Grounding language acquisition by training semantic parsing using caption videos.pdf (3.5 MB)
Traer, J. & McDermott, J. H. Human inference of force from impact sounds: Perceptual evidence for inverse physics. Annual Meeting of the Acoustical Society 143, (2018).
Traer, J. & McDermott, J. H. Human recognition of environmental sounds is not always robust to reverberation. Annual Meeting of the Acoustical Society 143, (2018).
Ben-Yosef, G. & Ullman, S. Image interpretation above and below the object level. (2018).PDF icon CBMM-Memo-089.pdf (2.06 MB)
Ben-Yosef, G. & Ullman, S. Image interpretation above and below the object level. Interface Focus 8, 20180020 (2018).
Saxe, R. Imaging the infant brain. Japanese Society for Neuroscience Kobe Japan, (2018).
Tacchetti, A., Isik, L. & Poggio, T. Invariant Recognition Shapes Neural Representations of Visual Input. Annual Review of Vision Science 4, 403 - 422 (2018).PDF icon annurev-vision-091517-034103.pdf (1.55 MB)
O'Brien, N., Latessa, S., Evangelopoulos, G. & Boix, X. The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors. workshop on "AI for Social Good", NIPS 2018 (2018). at <http://hdl.handle.net/1721.1/120056>PDF icon fake-news-paper-NIPS.pdf (147.36 KB)PDF icon fake-news-paper-NIPS_2018_v2.pdf (147.36 KB)
Mlynarski, W. & McDermott, J. H. Learning Mid-Level Auditory Codes from Natural Sound Statistics. Neural Computation 30, 631-669 (2018).

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