Publications

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.

Videos
Support Us

Publication

Search

Show only items where

Author

Type

Term

Year

Keyword

Found 76 results

Author Title Type [ Year

]

Filters: First Letter Of Title is D [Clear All Filters]

2017

Chen, Z. & Wilson, M. A. Deciphering neural codes of memory during sleep. Trends in Neurosciences (2017).

proof (2.98 MB)

Sliwa, J. & Freiwald, W. A. A Dedicated Network for Social Interaction Processing in the Primate Brain. Science Vol. 356, pp. 745-749 (2017).

Lotter, W., Kreiman, G. & Cox, D. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ICLR (2017).

1605.08104.pdf (2.9 MB)

Lotter, W., Kreiman, G. & Cox, D. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. (2017).

CBMM-Memo-064.pdf (3 MB)

Dehghani, N. Design of the Artificial: lessons from the biological roots of general intelligence. (2017). at <https://arxiv.org/pdf/1703.02245>

DesignArtificial_Dehghani_arXiv.pdf (222.47 KB)

Wang, J. et al. Detecting Semantic Parts on Partially Occluded Objects. (2017).

CBMM-Memo-078.pdf (1.74 MB)

Wang, J. et al. Detecting Semantic Parts on Partially Occluded Objects. British Machine Vision Conference (BMVC) (2017). at <https://bmvc2017.london/proceedings/>

Meyers, E., Riley, M., Qi, X. - L. & Constantinidis, C. Differences in dynamic and static coding within different subdivision of the prefrontal cortex. Society for Neuroscience's Annual Meeting - SfN 2017 (2017). at <http://www.abstractsonline.com/pp8/#!/4376/presentation/4782>

Meyers, E., Liang, A., Katsuki, F. & Constantinidis, C. Differential Processing of Isolated Object and Multi-item Pop-Out Displays in LIP and PFC. Cerebral Cortex (2017). doi:10.1093/cercor/bhx243

Tacchetti, A., Voinea, S. & Evangelopoulos, G. Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets. (2017).

CBMM-Memo-062.pdf (9.37 MB)

Volokitin, A., Roig, G. & Poggio, T. Do Deep Neural Networks Suffer from Crowding?. (2017).

CBMM-Memo-069.pdf (6.47 MB)

Volokitin, A. & Roig, G. Do Deep Neural Networks Suffer from Crowding? [code]. (2017).

2018

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

CBMM-Memo-088.pdf (7.88 MB)

Shen, W. et al. Deep Regression Forests for Age Estimation. (2018).

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.2018.8593947

kuo2018planning.pdf (637.67 KB)

Araya-Polo, M., Jennings, J., Adler, A. & Dahlke, T. Deep-learning tomography. The Leading Edge 37, 58 - 66 (2018).

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).

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/>

Palepu, A. & Kreiman, G. Development of automated interictal spike detector. 40th International Conference of the IEEE Engineering in Medicine and Biology Society - EMBC 2018 (2018). at <https://embc.embs.org/2018/>

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).

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>

1804.04604v1.pdf (488.85 KB)

Meyers, E. Dynamic population coding and its relationship to working memory. Journal of Neurophysiology 120, 2260 - 2268 (2018).

2019

Lewis, O. & Hermann, K. Data for free: Fewer-shot algorithm learning with parametricity data augmentation. ICLR 2019 (2019).

Kuo, Y. - L., Katz, B. & Barbu, A. Deep Compositional Robotic Planners that Follow Natural Language Commands. Workshop on Visually Grounded Interaction and Language (ViGIL) at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS), (2019). at <https://vigilworkshop.github.io/>

Kell, A. J. E. & McDermott, J. H. Deep neural network models of sensory systems: windows onto the role of task constraints. Current Opinion in Neurobiology 55, 121 - 132 (2019).

Pages