Publication

Found 912 results
Author Title [ Type(Desc)] Year
Book Chapter
Barbu, A. et al. Computer Vision – ECCV 2014, Lecture Notes in Computer Science 8693, 612–627 (Springer International Publishing, 2014).
Poggio, T. & Squire, L. R. The History of Neuroscience in Autobiography Volume 8 8, (Society for Neuroscience, 2014).PDF icon Volume Introduction and Preface (232.8 KB)PDF icon TomasoPoggio.pdf (1.43 MB)
Mormann, F. et al. Single neuron studies of the human brain. Probing cognition (2014).
Kreiman, G. Psychology of Learning and Motivation 70, (2019).
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>
CBMM Memos
Amir, N. et al. Abstracts of the 2014 Brains, Minds, and Machines Summer Course. (2014).PDF icon CBMM-Memo-024.pdf (2.86 MB)
Mhaskar, H. & Poggio, T. An analysis of training and generalization errors in shallow and deep networks. (2018).PDF icon CBMM-Memo-076.pdf (772.61 KB)PDF icon CBMM-Memo-076v2.pdf (2.67 MB)
Mhaskar, H. & Poggio, T. An analysis of training and generalization errors in shallow and deep networks. (2019).PDF icon CBMM-Memo-098.pdf (687.36 KB)PDF icon CBMM Memo 098 v4 (08/2019) (2.63 MB)
Berzak, Y., Huang, Y., Barbu, A., Korhonen, A. & Katz, B. Anchoring and Agreement in Syntactic Annotations. (2016).PDF icon CBMM-Memo-055.pdf (768.54 KB)
Poggio, T. Associative Memory as the Core of Intelligence in Technology and Evolution. (2026).PDF icon Review_On_Associative_Memories-14.pdf (245.78 KB)
Reddy, M. Vuyyuru, Banburski, A., Pant, N. & Poggio, T. Biologically Inspired Mechanisms for Adversarial Robustness. (2020).PDF icon CBMM_Memo_110.pdf (3.14 MB)
Xiao, W., Chen, H., Liao, Q. & Poggio, T. Biologically-plausible learning algorithms can scale to large datasets. (2018).PDF icon CBMM-Memo-092.pdf (1.31 MB)
Adler, A., Wax, M. & Pantazis, D. Brain Signals Localization by Alternating Projections. arXiv (2019).PDF icon CBMM-Memo-099.pdf (421.67 KB)
Liao, Q. & Poggio, T. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. (2016).PDF icon CBMM Memo No. 047 (1.29 MB)
Lake, B. M., Ullman, T., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. (2016).PDF icon machines_that_think.pdf (3.45 MB)
Liao, Q., Leibo, J. Z., Mroueh, Y. & Poggio, T. Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?. (2014).PDF icon CBMM-Memo-003.pdf (963.66 KB)
Villalobos, K. M. et al. Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?. (2018).PDF icon CBMM-Memo-095.pdf (1.96 MB)
Shalev-Shwartz, S. & Shashua, A. Can we Contain Covid-19 without Locking-down the Economy?. (2020).PDF icon CBMM Memo 104 v4 (Apr. 6, 2020) (418.25 KB)PDF icon CBMM Memo 104 v3 (Apr. 1, 2020) (452.94 KB)PDF icon CBMM Memo 104 v2 (Mar. 28, 2020) (427.39 KB)PDF icon CBMM-Memo-104.pdf (425.12 KB)
Madan, S. et al. On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations. (2020).PDF icon CBMM-Memo-111.pdf (9.76 MB)
Liao, Q., Miranda, B., Hidary, J. & Poggio, T. Classical generalization bounds are surprisingly tight for Deep Networks. (2018).PDF icon CBMM-Memo-091.pdf (1.43 MB)PDF icon CBMM-Memo-091-v2.pdf (1.88 MB)
Yuille, A. & Mottaghi, R. Complexity of Representation and Inference in Compositional Models with Part Sharing. (2015).PDF icon CBMM Memo 031.pdf (1.14 MB)
Barbu, A. et al. The Compositional Nature of Event Representations in the Human Brain. (2014).PDF icon CBMM Memo 011.pdf (3.95 MB)
Kuo, Y. - L., Katz, B. & Barbu, A. Compositional Networks Enable Systematic Generalization for Grounded Language Understanding. (2021).PDF icon CBMM-Memo-129.pdf (1.2 MB)
Kuo, Y. - L., Barbu, A. & Katz, B. Compositional RL Agents That Follow Language Commands in Temporal Logic. (2021).PDF icon CBMM-Memo-127.pdf (2.12 MB)
Poggio, T. & Fraser, M. Compositional Sparsity of Learnable Functions. (2024).PDF icon This is an update of the AMS paper (230.72 KB)

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