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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CBMM Memos

Harari, D., Gao, T., Kanwisher, N., Tenenbaum, J. B. & Ullman, S. Measuring and modeling the perception of natural and unconstrained gaze in humans and machines. (2016).

CBMM-Memo-059.pdf (1.71 MB)

Poggio, T. & Cooper, Y. Loss landscape: SGD has a better view. (2020).

CBMM-Memo-107.pdf (1.03 MB)

Typos and small edits, ver11 (955.08 KB)

Small edits, corrected Hessian for spurious case (337.19 KB)

Mlynarski, W. & McDermott, J. H. Learning Mid-Level Auditory Codes from Natural Sound Statistics. (2017).

MlynarskiMcDermott_Memo060.pdf (7.11 MB)

Mhaskar, H., Liao, Q. & Poggio, T. Learning Functions: When Is Deep Better Than Shallow. (2016). at <https://arxiv.org/pdf/1603.00988v4.pdf>

Evangelopoulos, G., Voinea, S., Zhang, C., Rosasco, L. & Poggio, T. Learning An Invariant Speech Representation. (2014).

CBMM-Memo-022-1406.3884v1.pdf (1.81 MB)

Wang, C., Ross, C., Kuo, Y. - L., Katz, B. & Barbu, A. Learning a natural-language to LTL executable semantic parser for grounded robotics. (2020). doi:https://doi.org/10.48550/arXiv.2008.03277

CBMM-Memo-122.pdf (1.03 MB)

Xu, M. et al. The Janus effects of SGD vs GD: high noise and low rank. (2023).

Updated with appendix showing empirically that the main results extend to deep nonlinear networks (2.95 MB)

Small updates...typos... (616.82 KB)

Poggio, T., Anselmi, F. & Rosasco, L. I-theory on depth vs width: hierarchical function composition. (2015).

cbmm_memo_041.pdf (1.18 MB)

Leibo, J. Z., Liao, Q., Anselmi, F. & Poggio, T. The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. (2014). doi:10.1101/004473

CBMM Memo 004_new.pdf (2.25 MB)

Anselmi, F., Rosasco, L. & Poggio, T. On Invariance and Selectivity in Representation Learning. (2015).

CBMM Memo No. 029 (812.07 KB)

Dehaene-Lambertz, G. & Spelke, E. S. The infancy of the human brain. (2016). doi:http://dx.doi.org/10.1016/j.neuron.2015.09.026

CBMM-Memo-053.pdf (1.51 MB)

Tejwani, R. et al. Incorporating Rich Social Interactions Into MDPs. (2022).

CBMM-Memo-133.pdf (1.68 MB)

Poggio, T., Liao, Q. & Xu, M. Implicit dynamic regularization in deep networks. (2020).

v1.2 (2.29 MB)

v.59 Update on rank (2.43 MB)

Ullman, S. et al. Image interpretation by iterative bottom-up top- down processing. (2021).

CBMM-Memo-120.pdf (2.83 MB)

Ben-Yosef, G. & Ullman, S. Image interpretation above and below the object level. (2018).

CBMM-Memo-089.pdf (2.06 MB)

Mottaghi, R., Fidler, S., Yuille, A., Urtasun, R. & Parikh, D. Human-Machine CRFs for Identifying Bottlenecks in Holistic Scene Understanding. (2014).

CBMM-Memo-020.pdf (1.89 MB)

Liao, Q., Leibo, J. Z. & Poggio, T. How Important is Weight Symmetry in Backpropagation?. (2015).

1510.05067v3.pdf (615.32 KB)

Poggio, T. How Deep Sparse Networks Avoid the Curse of Dimensionality: Efficiently Computable Functions are Compositionally Sparse. (2022).

v1.0 (984.15 KB)

v5.7 adding in context learning etc (1.16 MB)

Gan, Y. & Poggio, T. A Homogeneous Transformer Architecture. (2023).

CBMM Memo 143 v2 (1.1 MB)

Nickel, M., Rosasco, L. & Poggio, T. Holographic Embeddings of Knowledge Graphs. (2015).

holographic-embeddings.pdf (677.87 KB)

Sanders, H., Wilson, M. A. & Gershman, S. J. Hippocampal Remapping as Hidden State Inference. (2019). doi:https://doi.org/10.1101/743260

CBMM-Memo-101.pdf (12.78 MB)

Deza, A., Liao, Q., Banburski, A. & Poggio, T. Hierarchically Local Tasks and Deep Convolutional Networks. (2020).

CBMM_Memo_109.pdf (2.12 MB)

Morère, O. et al. Group Invariant Deep Representations for Image Instance Retrieval. (2016).

CBMM-Memo-043.pdf (2.66 MB)

Winston, P. Henry. The Genesis Story Understanding and Story Telling System A 21st Century Step toward Artificial Intelligence. (2014).

CBMM-Memo-019_StoryWhitePaper.pdf (894.38 KB)

Pinto, A., Rangamani, A. & Poggio, T. On Generalization Bounds for Neural Networks with Low Rank Layers. (2024).

CBMM-Memo-151.pdf (697.31 KB)

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