Publication

Found 906 results
[ Author(Asc)] 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 
G
Gan, Y., Galanti, T., Poggio, T. & Malach, E. On the Power of Decision Trees in Auto-Regressive Language Modeling. (2024).PDF icon CBMM-Memo-149.pdf (2.11 MB)
Galanti, T. & Poggio, T. SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks. (2022).PDF icon 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).PDF icon Low-rank bias.pdf (2.38 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>PDF icon NeurIPS-2023-norm-based-generalization-bounds-for-sparse-neural-networks-Paper-Conference.pdf (577.69 KB)
Galanti, T. & Galanti, L. On the Implicit Bias Towards Minimal Depth of Deep Neural Networks. arXiv (2022). at <https://arxiv.org/abs/2202.09028>PDF icon 2202.09028.pdf (2 MB)
Galanti, T., Xu, M., Galanti, L. & Poggio, T. Norm-Based Generalization Bounds for Compositionally Sparse Neural Networks. (2023).PDF icon Norm-based bounds for convnets.pdf (1.2 MB)
F
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>PDF icon Learning with a Wasserstein Loss_1506.05439v2.pdf (2.57 MB)
Fried, I., Rutishauser, U., Cerf, M. & Kreiman, G. Single Neuron Studies of the Human Brain. Probing Cognition. Probing cognition (MIT Press, 2014).
Freiwald, W. A. Gross means Great. Progress in Neurobiology 195, 101924 (2020).
Freiwald, W. A. Social interaction networks in the primate brain. Current Opinion in Neurobiology 65, 49 - 58 (2020).
Freiwald, W., Deen, B., Sliwa, J. & Schwiedrzik, C. M. Specialized Networks for Social Cognition in the Primate Brain. (In Press).
Freiwald, W. A. & Hosoya, H. Neuroscience: A Face’s Journey through Space and Time. Current Biology 31, R13 - R15 (2021).
Freiwald, W. A. The neural mechanisms of face processing: cells, areas, networks, and models. Current Opinion in Neurobiology 60, 184 - 191 (2020).
Francl, A. & McDermott, J. H. Deep neural network models of sound localization reveal how perception is adapted to real-world environments. Nature Human Behavior 6, 111–133 (2022).PDF icon s41562-021-01244-z.pdf (7.22 MB)
Fisher, C. & Freiwald, W. A. Whole-agent selectivity within the macaque face-processing system. Proceedings of the National Academy of Sciences (PNAS) 112, (2015).PDF icon Authors' last version of article.  (3.1 MB)
Fisher, C. & Freiwald, W. A. Contrasting Specializations for Facial Motion within the Macaque Face-Processing System. Current Biology 25, (2015).PDF icon Facial Motion Selectivity in the Macaque Brain (1.43 MB)
Fischer, J., Mikhael, J. G., Tenenbaum, J. B. & Kanwisher, N. Functional neuroanatomy of intuitive physical inference. Proceedings of the National Academy of Sciences 113, E5072 - E5081 (2016).
Fetaya, E., Shamir, O. & Ullman, S. Graph Approximation and Clustering on a Budget. Artificial Intelligence and Statistics 38, (2015).PDF icon fetaya shamir Ullman 2015.pdf (664.26 KB)
Feliciano-Ramos, P. A., Galazo, M., Penagos, H. & Wilson, M. Hippocampal memory reactivation during sleep is correlated with specific cortical states of the retrosplenial and prefrontal cortices. Learning & Memory 30, 221 - 236 (2023).
Feather, J., Durango, A., Gonzalez, R. & McDermott, J. H. Metamers of neural networks reveal divergence from human perceptual systems. NIPS 2019 (2019). at <https://papers.nips.cc/paper/9198-metamers-of-neural-networks-reveal-divergence-from-human-perceptual-systems>PDF icon Feather_etal_2019_NeurIPS_metamers.pdf (4.7 MB)
Feather, J., Leclerc, G., Mądry, A. & McDermott, J. H. Model metamers reveal divergent invariances between biological and artificial neural networks. Nature Neuroscience (2023). doi:10.1038/s41593-023-01442-0
Fazeli, N. et al. See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion. Science Robotics 4, eaav3123 (2019).
E
Evangelopoulos, G., Voinea, S., Zhang, C., Rosasco, L. & Poggio, T. Learning An Invariant Speech Representation. (2014).PDF icon CBMM-Memo-022-1406.3884v1.pdf (1.81 MB)
Eric, W., Kevin, W. & Kreiman, G. Learning scene gist with convolutional neural networks to improve object recognition. 2018 52nd Annual Conference on Information Sciences and Systems (CISS) (2018). doi:10.1109/CISS.2018.8362305PDF icon 08362305.pdf (3.17 MB)
Ellis, K. et al. Write, Execute, Assess: Program Synthesis with a REPL. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 9116-write-execute-assess-program-synthesis-with-a-repl.pdf (3.9 MB)

Pages