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Found 906 results
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Liu, S. & Spelke, E. S. Six-month-old infants represent action efficiency on a continuous scale. 9th Biennial Meeting of the Cognitive Development Society (CDS) (2015).
Livingstone, M. S., Arcaro, M. J. & Schade, P. F. Cortex Is Cortex: Ubiquitous Principles Drive Face-Domain Development. Trends in Cognitive Sciences (2018). doi:10.1016/j.tics.2018.10.009PDF icon 1-s2.0-S1364661318302572-main.pdf (260.4 KB)
Lotter, W., Kreiman, G. & Cox, D. UNSUPERVISED LEARNING OF VISUAL STRUCTURE USING PREDICTIVE GENERATIVE NETWORKS. (2015).PDF icon 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).PDF icon 1805.10734.pdf (9.59 MB)
Lotter, W., Kreiman, G. & Cox, D. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. ICLR (2017).PDF icon 1605.08104.pdf (2.9 MB)
Lotter, W., Kreiman, G. & Cox, D. A neural network trained for prediction mimics diverse features of biological neurons and perception. Nature Machine Intelligence 2, 210 - 219 (2020).
Lotter, W., Kreiman, G. & Cox, D. Unsupervised Learning of Visual Structure using Predictive Generative Networks. International Conference on Learning Representations (ICLR) (2016). at <http://arxiv.org/pdf/1511.06380v2.pdf>
Lotter, W., Kreiman, G. & Cox, D. Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning. (2017).PDF icon CBMM-Memo-064.pdf (3 MB)
Lotter, W., Kreiman, G. & Cox, D. PredNet - "Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning" [code]. (2016).
Lotter, W., Kreiman, G. & Cox, D. A neural network trained to predict future videoframes mimics critical properties of biologicalneuronal responses and perception. ( arXiv | Cornell University, 2018). at <https://arxiv.org/pdf/1805.10734.pdf>PDF icon 1805.10734.pdf (9.59 MB)
Lu, W., Lian, X. & Yuille, A. Parsing Semantic Parts of Cars Using Graphical Models and Segment Appearance Consistency. (2014).PDF icon CBMM-Memo-018_opt.pdf (5.02 MB)
Luo, Y., Boix, X., Roig, G., Poggio, T. & Zhao, Q. Foveation-based Mechanisms Alleviate Adversarial Examples. (2016).PDF icon cbmm_memo_044.pdf (11.48 MB)
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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>
Madan, S. et al. On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations. (2020).PDF icon CBMM-Memo-111.pdf (9.76 MB)
Madan, S. et al. Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex. NeurIPS 2024 (2024).
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).
Madhavan, R. et al. Decrease in gamma-band activity tracks sequence learning. Frontiers in Systems Neuroscience 8, (2015).PDF icon fnsys-08-00222.pdf (5.62 MB)
Madhavan, R. et al. Neural Interactions Underlying Visuomotor Associations in the Human Brain. Cerebral Cortex 1–17, (2018).
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.12553PDF icon Changing Minds_MagidYanSiegelTenenbaumSchulz_in press.pdf (915.8 KB)
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).PDF icon 15_Cogsci_Magid&Schulz.pdf (513.72 KB)
Magid, R. Imagination and the generation of new ideas. Cognitive Development 34, 99–110 (2015).PDF icon 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).PDF icon Moral Alchemy_Magid&Schulz.pdf (627.46 KB)
Mahowald, K. et al. Dissociating language and thought in large language models. Trends in Cognitive Sciences 28, 517 - 540 (2024).
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>PDF icon Foveated_Drone_SVRHM_2020.pdf (13.44 MB)PDF icon v1 (12/15/2020) (14.7 MB)
Manek, G. et al. Pruning Convolutional Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1707.05455>PDF icon 1707.05455.pdf (143.46 KB)

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