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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Marques, T. & DiCarlo, J. J. A meta-analysis of ANNs as models of primate V1 . Bernstein (2019).

Marciniak, K., Dicke, P. W. & Thier, P. Monkeys head-gaze following is fast, precise and not fully suppressible. Proc Biol Sci 282, 20151020 (2015).

Marciniak et al 2015 Proc R Soc B Monkeys head gaze following is fast precise and not fully suppressible.pdf (7.07 MB)

Mao, J. et al. Generation and Comprehension of Unambiguous Object Descriptions. The Conference on Computer Vision and Pattern Recognition (CVPR) (2016). at <https://github.com/ mjhucla/Google_Refexp_toolbox>

object_description_cbmm.pdf (2.21 MB)

Mao, J., Xu, J., Jing, Y. & Yuille, A. Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images. NIPS 2016 (2016).

6590-training-and-evaluating-multimodal-word-embeddings-with-large-scale-web-annotated-images.pdf (1.57 MB)

Mao, J. et al. Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images. International Conference of Computer Vision (2015). at <www.stat.ucla.edu/~junhua.mao/projects/child_learning.html>

child_learning_iccv2015.pdf (1.16 MB)

Mao, J. et al. Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN). (2015).

CBMM Memo 033.pdf (839.42 KB)

Manek, G. et al. Pruning Convolutional Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1707.05455>

1707.05455.pdf (143.46 KB)

Malkin, E., Deza, A. & Poggio, T. CUDA-Optimized real-time rendering of a Foveated Visual System. Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2020 (2020). at <https://arxiv.org/abs/2012.08655>

Foveated_Drone_SVRHM_2020.pdf (13.44 MB)

v1 (12/15/2020) (14.7 MB)

Mahowald, K. et al. Dissociating language and thought in large language models. Trends in Cognitive Sciences 28, 517 - 540 (2024).

Magid, R. & Schulz, L. Quit while you’re ahead: Preschoolers’ persistence and willingness to accept challenges are affected by social comparison. Annual Meeting of the Cognitive Science Society (CogSci) (2015).

15_Cogsci_Magid&Schulz.pdf (513.72 KB)

Magid, R. Imagination and the generation of new ideas. Cognitive Development 34, 99–110 (2015).

Imagination and the generation of new ideas (266.63 KB)

Magid, R. & Schulz, L. Moral alchemy: How love changes norms. Cognition 167, 135 -150 (2017).

Moral Alchemy_Magid&Schulz.pdf (627.46 KB)

Magid, R., Yan, P., Siegel, M., Tenenbaum, J. B. & Schulz, L. Changing minds: Children’s inferences about third party belief revision. Developmental Science e12553 (2017). doi:10.1111/desc.12553

Changing Minds_MagidYanSiegelTenenbaumSchulz_in press.pdf (915.8 KB)

Madhavan, R. et al. Neural Interactions Underlying Visuomotor Associations in the Human Brain. Cerebral Cortex 1–17, (2018).

Madhavan, R. et al. Decrease in gamma-band activity tracks sequence learning. Frontiers in Systems Neuroscience 8, (2015).

fnsys-08-00222.pdf (5.62 MB)

Madan, S. et al. On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations. (2020).

CBMM-Memo-111.pdf (9.76 MB)

Madan, S. et al. When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations. Nature Machine Intelligence 4, 146 - 153 (2022).

Madan, S. et al. Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex. NeurIPS 2024 (2024).

Ma, K. - T., Sim, T. & Kankanhalli, M. VIP: A unifying framework for eye-gaze research. (2013). at <http://mmas.comp.nus.edu.sg/VIP.html>

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Luo, Y., Boix, X., Roig, G., Poggio, T. & Zhao, Q. Foveation-based Mechanisms Alleviate Adversarial Examples. (2016).

cbmm_memo_044.pdf (11.48 MB)

Lu, W., Lian, X. & Yuille, A. Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency. (2014).

CBMM-Memo-018_opt.pdf (5.02 MB)

Lotter, W., Kreiman, G. & Cox, D. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ICLR (2017).

1605.08104.pdf (2.9 MB)

Lotter, W., Kreiman, G. & Cox, D. UNSUPERVISED LEARNING OF VISUAL STRUCTURE USING PREDICTIVE GENERATIVE NETWORKS. (2015).

CBMM Memo 040_rev1.pdf (1.92 MB)

Lotter, W., Kreiman, G. & Cox, D. A neural network trained to predict future video frames mimics critical properties of biological neuronal responses and perception. Nature Machine Learning (2020).

1805.10734.pdf (9.59 MB)

Lotter, W., Kreiman, G. & Cox, D. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. (2017).

CBMM-Memo-064.pdf (3 MB)

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