All Publications

2022

Y. Tazi, Berger, M., and Freiwald, W. A., “Towards an objective characterization of an individual's facial movements using Self-Supervised Person-Specific-Models”, arXiv, 2022.
CBMM Funded
C. Xu, Yang, S., Galanti, T., Wu, B., Yue, X., Zhai, B., Zhan, W., Vajda, P., Keutzer, K., and Tomizuka, M., Lecture Notes in Computer ScienceComputer Vision – ECCV 2022Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models, vol. 13697. Cham: Springer Nature Switzerland, 2022, pp. 638 - 656.
CBMM Funded
A. Marliawaty Bagus, Marques, T., Sanghavi, S., DiCarlo, J. J., and Schrimpf, M., “Primate Inferotemporal Cortex Neurons Generalize Better to Novel Image Distributions Than Analogous Deep Neural Networks Units”, in NeurIPS, 2022.
CBMM Related
F. Anselmi and Poggio, T., “Representation Learning in Sensory Cortex: a theory”, IEEE Access, pp. 1 - 1, 2022.PDF icon Representation_Learning_in_Sensory_Cortex_a_theory.pdf (1.17 MB)
CBMM Funded
CBMM Memo No.
137
A. Rangamani and Xie, Y., “Understanding the Role of Recurrent Connections in Assembly Calculus”. 2022.PDF icon CBMM-Memo-137.pdf (1.49 MB)
CBMM Funded
CBMM Memo No.
136
Y. Han, Poggio, T., and Cheung, B., “System identification of neural systems: If we got it right, would we know?”. 2022.PDF icon CBMM-Memo-136.pdf (1.75 MB)
CBMM Funded
J. Dapello, Kar, K., Schrimpf, M., Geary, R., Ferguson, M., Cox, D. D., and DiCarlo, J. J., “Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness”, bioRxiv, 2022.
CBMM Related
C. Guo, Lee, M. J., Leclerc, G., Dapello, J., Rao, Y., Madry, A., and DiCarlo, J. J., “Adversarially trained neural representations may already be as robust as corresponding biological neural representations”, arXiv, 2022.
CBMM Related
K. Kar, “A computational probe into the behavioral and neural markers of atypical facial emotion processing in autism”, The Journal of Neuroscience, pp. JN-RM-2229-21, 2022.
CBMM Related
A. Banburski and Rangamani, A., “Neural Collapse in Deep Homogeneous Classifiers and the role of Weight Decay”, in IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, 2022.
CBMM Funded
K. Dobs, Martinez-Trujillo, J., Kell, A. J. E., and Kanwisher, N., “Brain-like functional specialization emerges spontaneously in deep neural networks”, Science Advances, vol. 8, no. 11, 2022.
CBMM Funded
CBMM Memo No.
134
T. Galanti and Poggio, T., “SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks”. 2022.PDF icon Implicit Rank Minimization.pdf (1.76 MB)
CBMM Funded
T. Galanti and Galanti, L., “On the Implicit Bias Towards Minimal Depth of Deep Neural Networks”, arXiv, 2022.PDF icon 2202.09028.pdf (2 MB)
CBMM Funded
F. S. Kamps, Richardson, H., N. Murty, A. Ratan, Kanwisher, N., and Saxe, R., “Using child‐friendly movie stimuli to study the development of face, place, and object regions from age 3 to 12 years”, Human Brain Mapping, 2022.
CBMM Related
CBMM Memo No.
121
M. Yamada, D'Amario, V., Takemoto, K., Boix, X., and Sasaki, T., “Transformer Module Networks for Systematic Generalization in Visual Question Answering”. 2022.PDF icon CBMM-Memo-121.pdf (1.06 MB)PDF icon version 2 (3/22/2023) (1.33 MB)
CBMM Funded
S. Madan, Henry, T., Dozier, J., Ho, H., Bhandari, N., Sasaki, T., Durand, F., Pfister, H., and Boix, X., “When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations”, Nature Machine Intelligence, vol. 4, no. 2, pp. 146 - 153, 2022.
CBMM Funded
CBMM Memo No.
119
A. Sakai, Sunagawa, T., Madan, S., Suzuki, K., Katoh, T., Kobashi, H., Pfister, H., Sinha, P., Boix, X., and Sasaki, T., “Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations”. 2022.PDF icon CBMM-Memo-119.pdf (31.08 MB)
CBMM Funded
CBMM Memo No.
135
A. Gupte, Banburski, A., and Poggio, T., “PCA as a defense against some adversaries”. 2022.PDF icon CBMM-Memo-135.pdf (2.58 MB)
CBMM Funded
W. Berrios and Deza, A., “Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4”, in BrainScore Workshop at COSYNE, 2022.
CBMM Funded
A. Harrington and Deza, A., “Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks”, International Conference on Learning Representations (ICLR). 2022.
CBMM Funded

2021

Y. Xiang, Graeber, T., Enke, B., and Gershman, S. J., “Confidence and central tendency in perceptual judgment”, Attention, Perception, & Psychophysics, vol. 83, no. 7, pp. 3024 - 3034, 2021.
CBMM Funded
A. Baidya, Dapello, J., DiCarlo, J. J., and Marques, T., “Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs”, in NeurIPS 2021, 2021.
CBMM Funded
J. Gant, Banburski, A., Deza, A., and Poggio, T., “Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN”, NeurIPS 2021. 2021.
CBMM Funded
A. Ratan N. Murty, Bashivan, P., Abate, A., DiCarlo, J. J., and Kanwisher, N., “Computational models of category-selective brain regions enable high-throughput tests of selectivity”, Nature Communications, vol. 12, no. 1, 2021.PDF icon s41467-021-25409-6.pdf (6.47 MB)
CBMM Funded
CBMM Memo No.
120
S. Ullman, Assif, L., Strugatski, A., Vatashsky, B. - Z., Levi, H., Netanyahu, A., and Yaari, A. Uri, “Image interpretation by iterative bottom-up top- down processing”. 2021.PDF icon CBMM-Memo-120.pdf (2.83 MB)
CBMM Funded

Pages