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 285 results

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2019

Stephenson, C. et al. Untangling in Invariant Speech Recognition. Neural Information Processing Systems (NeurIPS 2019) (2019).

9583-untangling-in-invariant-speech-recognition.pdf (2.09 MB)

Phillips-Jones, T., Coronel, S. Otero, Sani, I. & Freiwald, W. A. A Virtual Reality Experimental Approach for Studying How the Brain Implements Attentive Behaviors. Tri-Institute 2019 Gateways to the Laboratory Summer Program (2019).

Banburski, A. et al. Weight and Batch Normalization implement Classical Generalization Bounds . ICML (2019).

Ellis, K. et al. Write, Execute, Assess: Program Synthesis with a REPL. Neural Information Processing Systems (NeurIPS 2019) (2019).

9116-write-execute-assess-program-synthesis-with-a-repl.pdf (3.9 MB)

2018

Mhaskar, H. & Poggio, T. An analysis of training and generalization errors in shallow and deep networks. (2018).

CBMM-Memo-076.pdf (772.61 KB)

CBMM-Memo-076v2.pdf (2.67 MB)

Xiao, W., Chen, H., Liao, Q. & Poggio, T. Biologically-plausible learning algorithms can scale to large datasets. (2018).

CBMM-Memo-092.pdf (1.31 MB)

Schrimpf, M. & Kubilius, J. Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?. bioRxiv preprint (2018). doi:10.1101/407007

Brain-Score bioRxiv.pdf (789.83 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., 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)

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/>

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

Tacchetti, A., Isik, L. & Poggio, T. Invariant Recognition Shapes Neural Representations of Visual Input. Annual Review of Vision Science 4, 403 - 422 (2018).

annurev-vision-091517-034103.pdf (1.55 MB)

Misra, P., Marconi, A., Peterson, M. F. & Kreiman, G. Minimal memory for details in real life events. Scientific Reports 8, (2018).

Hu, S. et al. Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes. Cell Reports 25, 2635 - 2642.e5 (2018).

Tang, H. et al. Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences (2018). doi:10.1073/pnas.1719397115

1719397115.full_.pdf (1.1 MB)

Adhya, D. et al. Shared gene co-expression networks in autism from induced pluripotent stem cell (iPSC) neurons. BioRxiv (2018). doi:10.1101/349415

Arend, L. et al. Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results. (2018).

CBMM-Memo-093.pdf (2.99 MB)

Poggio, T. & Liao, Q. Theory I: Deep networks and the curse of dimensionality. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018).

02_761-774_00966_Bpast.No_.66-6_28.12.18_K1.pdf (1.18 MB)

Poggio, T. & Liao, Q. Theory II: Deep learning and optimization. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018).

03_775-788_00920_Bpast.No_.66-6_31.12.18_K2.pdf (5.43 MB)

Banburski, A. et al. Theory III: Dynamics and Generalization in Deep Networks. (2018).

Original, intermediate versions are available under request (2.67 MB)

CBMM Memo 90 v12.pdf (4.74 MB)

Theory_III_ver44.pdf Update Hessian (4.12 MB)

Theory_III_ver48 (Updated discussion of convergence to max margin) (2.56 MB)

fixing errors and sharpening some proofs (2.45 MB)

Powell, L. J. & Spelke, E. S. Third-Party Preferences for Imitators in Preverbal Infants. Open Mind 2, 61 - 71 (2018).

Wang, J. et al. Visual concepts and compositional voting. (2018).

CBMM-Memo-087.pdf (3.37 MB)

Wang, J. et al. Visual Concepts and Compositional Voting. Annals of Mathematical Sciences and Applications (AMSA) 3, 151–188 (2018).

2017

Chandrasekhar, V. et al. Compression of Deep Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1701.04923>

1701.04923.pdf (614.33 KB)

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

CBMM-Memo-069.pdf (6.47 MB)

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