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.

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Found 912 results

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] Year

CBMM Memos

Mhaskar, H. & Poggio, T. Deep vs. shallow networks : An approximation theory perspective. (2016).

Original submission, visit the link above for the updated version (960.27 KB)

Zhang, C., Evangelopoulos, G., Voinea, S., Rosasco, L. & Poggio, T. A Deep Representation for Invariance And Music Classification. (2014).

CBMM-Memo-002.pdf (1.63 MB)

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

CBMM-Memo-085.pdf (2.2 MB)

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

CBMM-Memo-064.pdf (3 MB)

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

CBMM-Memo-088.pdf (7.88 MB)

Anselmi, F., Rosasco, L., Tan, C. & Poggio, T. Deep Convolutional Networks are Hierarchical Kernel Machines. (2015).

CBMM Memo 035_rev5.pdf (975.65 KB)

Kuo, Y. - L., Katz, B. & Barbu, A. Deep compositional robotic planners that follow natural language commands. (2020).

CBMM-Memo-124.pdf (1.03 MB)

Mao, J. et al. Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN). (2015).

CBMM Memo 033.pdf (839.42 KB)

Berzak, Y., Reichart, R. & Katz, B. Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL. (2016).

memo-50.pdf (493.74 KB)

Adler, A. & Wax, M. Constant Modulus Algorithms via Low-Rank Approximation. (2018).

CBMM-Memo-077.pdf (795.61 KB)

Goodman, N. D., Tenenbaum, J. B. & Gerstenberg, T. Concepts in a Probabilistic Language of Thought. (2014).

CBMM-Memo-010.pdf (902.53 KB)

Poggio, T., Mutch, J. & Isik, L. Computational role of eccentricity dependent cortical magnification. (2014).

CBMM-Memo-017.pdf (1.04 MB)

Poggio, T. & Fraser, M. Compositional Sparsity of Learnable Functions. (2024).

This is an update of the AMS paper (230.72 KB)

Kuo, Y. - L., Barbu, A. & Katz, B. Compositional RL Agents That Follow Language Commands in Temporal Logic. (2021).

CBMM-Memo-127.pdf (2.12 MB)

Kuo, Y. - L., Katz, B. & Barbu, A. Compositional Networks Enable Systematic Generalization for Grounded Language Understanding. (2021).

CBMM-Memo-129.pdf (1.2 MB)

Barbu, A. et al. The Compositional Nature of Event Representations in the Human Brain. (2014).

CBMM Memo 011.pdf (3.95 MB)

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

CBMM Memo 031.pdf (1.14 MB)

Liao, Q., Miranda, B., Hidary, J. & Poggio, T. Classical generalization bounds are surprisingly tight for Deep Networks. (2018).

CBMM-Memo-091.pdf (1.43 MB)

CBMM-Memo-091-v2.pdf (1.88 MB)

Madan, S. et al. On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations. (2020).

CBMM-Memo-111.pdf (9.76 MB)

Shalev-Shwartz, S. & Shashua, A. Can we Contain Covid-19 without Locking-down the Economy?. (2020).

CBMM Memo 104 v4 (Apr. 6, 2020) (418.25 KB)

CBMM Memo 104 v3 (Apr. 1, 2020) (452.94 KB)

CBMM Memo 104 v2 (Mar. 28, 2020) (427.39 KB)

CBMM-Memo-104.pdf (425.12 KB)

Villalobos, K. M. et al. Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?. (2018).

CBMM-Memo-095.pdf (1.96 MB)

Liao, Q., Leibo, J. Z., Mroueh, Y. & Poggio, T. Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?. (2014).

CBMM-Memo-003.pdf (963.66 KB)

Lake, B. M., Ullman, T., Tenenbaum, J. B. & Gershman, S. J. Building machines that learn and think like people. (2016).

machines_that_think.pdf (3.45 MB)

Liao, Q. & Poggio, T. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. (2016).

CBMM Memo No. 047 (1.29 MB)

Adler, A., Wax, M. & Pantazis, D. Brain Signals Localization by Alternating Projections. arXiv (2019).

CBMM-Memo-099.pdf (421.67 KB)

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