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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Atabaki, A., Marciniak, K., Dicke, P. W. & Thier, P. Assessing the precision of gaze following using a stereoscopic 3D virtual reality setting. Vision Res 112, 68-82 (2015).

Atabaki Marciniak Dicke Thier 2015 Vis Res Assesing the precision of gaze following using a stereoscopic 3D virtual reality setting.pdf (2.52 MB)

Armendariz, M., Xiao, W., Vinken, K. & Kreiman, G. Do computational models of vision need shape-based representations? Evidence from an individual with intriguing visual perceptions. Cognitive Neuropsychology 1 - 3 (2022). doi:10.1080/02643294.2022.2041588

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)

Arcaro, M. J., Schade, P. F., Vincent, J. L., Ponce, C. R. & Livingstone, M. S. Seeing faces is necessary for face-domain formation. Nature Neuroscience 5631628, (2017).

Araya-Polo, M., Adler, A., Farris, S. & Jennings, J. Deep Learning: Algorithms and Applications (SPRINGER-VERLAG, 2019).

Araya-Polo, M., Jennings, J., Adler, A. & Dahlke, T. Deep-learning tomography. The Leading Edge 37, 58 - 66 (2018).

TLE2018.pdf (1.9 MB)

Anzellottti, S., Houlihan, S. Dae, Liburd, Jr, S. & Saxe, R. Leveraging facial expressions and contextual information to investigate opaque representations of emotions. Emotion (2021). doi:10.1037/emo0000685

Anzellotti 2021 Emotion.pdf (1.08 MB)

Anselmi, F., Rosasco, L., Tan, C. & Poggio, T. Deep Convolutional Networks are Hierarchical Kernel Machines. (2015).

CBMM Memo 035_rev5.pdf (975.65 KB)

Anselmi, F. & Poggio, T. Representation Learning in Sensory Cortex: a theory. IEEE Access 1 - 1 (2022). doi:10.1109/ACCESS.2022.3208603

Representation_Learning_in_Sensory_Cortex_a_theory.pdf (1.17 MB)

Anselmi, F., Rosasco, L. & Poggio, T. On invariance and selectivity in representation learning. Information and Inference: A Journal of the IMA iaw009 (2016). doi:10.1093/imaiai/iaw009

imaiai.iaw009.full_.pdf (267.87 KB)

Anselmi, F. et al. Unsupervised learning of invariant representations with low sample complexity: the magic of sensory cortex or a new framework for machine learning?. (2014).

CBMM Memo No. 001 (940.36 KB)

Anselmi, F. et al. Unsupervised learning of invariant representations. Theoretical Computer Science (2015). doi:10.1016/j.tcs.2015.06.048

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

CBMM Memo No. 029 (812.07 KB)

Anselmi, F., Evangelopoulos, G., Rosasco, L. & Poggio, T. Symmetry Regularization. (2017).

CBMM-Memo-063.pdf (6.1 MB)

Anselmi, F. & Poggio, T. Representation Learning in Sensory Cortex: a theory. (2014).

CBMM-Memo-026_neuron_ver45.pdf (1.35 MB)

Anselmi, F. et al. Unsupervised Learning of Invariant Representations in Hierarchical Architectures. (2013).

1311.4158v2.pdf (3.78 MB)

Amir, N. et al. Abstracts of the 2014 Brains, Minds, and Machines Summer Course. (2014).

CBMM-Memo-024.pdf (2.86 MB)

Allen, K. et al. Meta-strategy learning in physical problem solving: the effect of embodied experience. bioRxiv (2021).

2021.07.08.451333v2.full_.pdf (3.05 MB)

Allen, K., Yildirim, I. & Tenenbaum, J. B. Integrating Identification and Perception: A case study of familiar and unfamiliar face processing. Proceedings of the Thirty-Eight Annual Conference of the Cognitive Science Society (2016).

allen_5_13.pdf (2.13 MB)

Allen, K. R. et al. Lifelong learning of cognitive styles for physical problem-solving: The effect of embodied experienceAbstract. Psychonomic Bulletin & Review (2023). doi:10.3758/s13423-023-02400-4

Allen, K., Smith, K. A. & Tenenbaum, J. B. Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning. Proceedings of the National Academy of Sciences 201912341 (2020). doi:10.1073/pnas.1912341117

1912341117.full_.pdf (2.15 MB)

Alfano, P. Didier, Pastore, V. Paolo, Rosasco, L. & Odone, F. Top-tuning: A study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods. Image and Vision Computing 142, 104894 (2024).

Aghajan, Z. M., Kreiman, G. & Fried, I. Minute-scale periodicity of neuronal firing in the human entorhinal cortex. Cell Reports 42, 113271 (2023).

1-s2.0-S2211124723012834-main.pdf (5.33 MB)

Adler, A. & Wax, M. Constant modulus algorithms via low-rank approximation. Signal Processing 160, 263 - 270 (2019).

Adler, A. & Wax, M. Constant Modulus Beamforming Via Low-Rank Approximation. 2018 IEEE Statistical Signal Processing Workshop (SSP) (2018). doi:10.1109/SSP.2018.8450799

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