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 908 results

] Title Type Year

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

F

Fried, I., Rutishauser, U., Cerf, M. & Kreiman, G. Single Neuron Studies of the Human Brain. Probing Cognition. Probing cognition (MIT Press, 2014).

Frogner, C., Zhang, C., Mobahi, H., Araya-Polo, M. & Poggio, T. Learning with a Wasserstein Loss. Advances in Neural Information Processing Systems (NIPS 2015) 28 (2015). at <http://arxiv.org/abs/1506.05439>

Learning with a Wasserstein Loss_1506.05439v2.pdf (2.57 MB)

G

Galanti, T. & Poggio, T. SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks. (2022).

Implicit Rank Minimization.pdf (1.76 MB)

Galanti, T., Siegel, Z., Gupte, A. & Poggio, T. SGD and Weight Decay Provably Induce a Low-Rank Bias in Deep Neural Networks. (2023).

Low-rank bias.pdf (2.38 MB)

Galanti, T. & Galanti, L. On the Implicit Bias Towards Minimal Depth of Deep Neural Networks. arXiv (2022). at <https://arxiv.org/abs/2202.09028>

2202.09028.pdf (2 MB)

Galanti, T., Xu, M., Galanti, L. & Poggio, T. Norm-based Generalization Bounds for Sparse Neural Networks. NeurIPS 2023 (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/file/8493e190ff1bbe3837eca821190b61ff-Paper-Conference.pdf>

NeurIPS-2023-norm-based-generalization-bounds-for-sparse-neural-networks-Paper-Conference.pdf (577.69 KB)

Galanti, T., Xu, M., Galanti, L. & Poggio, T. Norm-Based Generalization Bounds for Compositionally Sparse Neural Networks. (2023).

Norm-based bounds for convnets.pdf (1.2 MB)

Gan, Y., Galanti, T., Poggio, T. & Malach, E. On the Power of Decision Trees in Auto-Regressive Language Modeling. (2024).

CBMM-Memo-149.pdf (2.11 MB)

Gan, Y. & Poggio, T. For HyperBFs AGOP is a greedy approximation to gradient descent. (2024).

CBMM-Memo-148.pdf (1.06 MB)

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

CBMM Memo 143 v2 (1.1 MB)

Gant, J., Banburski, A., Deza, A. & Poggio, T. Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN. NeurIPS 2021 (2021). at <https://nips.cc/Conferences/2021/Schedule?showEvent=21868>

Gao, T., Harari, D., Tenenbaum, J. B. & Ullman, S. When Computer Vision Gazes at Cognition. (2014).

CBMM-Memo-025.pdf (3.78 MB)

Garrote, E., Jhuang, H., Huehne, H., Poggio, T. & Serre, T. A Large Video Database for Human Motion Recognition. (2011).

Kuehne_etal_ICCV2011.pdf (433.27 KB)

Garrote, E. et al. System for Mouse Behavior Recognition. (2010).

Gartstein, M. A. et al. Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).

Gaziv, G., Lee, M. J. & DiCarlo, J. J. Strong and Precise Modulation of Human Percepts via Robustified ANNs. NeurIPS 2023 (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/hash/d00904cebc0d5b69fada8ad33d0f1422-Abstract-Conference.html>

Gaziv, G., Lee, M. J. & DiCarlo, J. J. Robustified ANNs Reveal Wormholes Between Human Category Percepts. arXiv (2023). at <https://arxiv.org/abs/2308.06887>

Gen, C. et al. ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. arXiv (2020). at <https://arxiv.org/abs/2007.04954>

2007.04954.pdf (7.06 MB)

Gershman, S. J. & Ullman, T. D. Causal implicatures from correlational statements. PLOS ONE 18, e0286067 (2023).

Gershman, S. J. The Generative Adversarial Brain. Frontiers in Artificial Intelligence 2, (2019).

Gershman, S. J., Horvitz, E. J. & Tenenbaum, J. B. Computational rationality: A converging paradigm for intelligence in brains, minds, and machines. Science 349, 273-278 (2015).

Gershman, S. J. & Burke, T. Mental control of uncertainty. Cognitive, Affective, & Behavioral Neuroscience 23, 465 - 475 (2022).

Gershman, S. J. How to never be wrong. Psychonomic Bulletin & Review 26, 13 - 28 (2019).

Gershman, S. J., Tenenbaum, J. B. & Jaekel, F. Discovering hierarchical motion structure. Vision Research Available online 26 March 2015, (2015).

hierarchical_motion.pdf (582.01 KB)

Gershman, S. J. Origin of perseveration in the trade-off between reward and complexity. Cognition 204, 104394 (2020).

Pages