Publication
] VerbCorner: Testing theories of argument structure through crowdsourcing. Workshop on Events in Language (2016).
VerbCorner_EventsInLanguage.pdf (1.14 MB)
VerbCorner_EventsInLanguage.pdf (1.14 MB) Vector-based pedestrian navigation in cities. Nature Computational Science 1, 678 - 685 (2021). s43588-021-00130-y.pdf (1.96 MB)
s43588-021-00130-y.pdf (1.96 MB) Using task-optimized neural networks to understand why brains have specialized processing for faces . Computational and Systems Neurosciences (2020).
Using Multimodal DNNs to Study Vision-Language Integration in the Brain. ICLR 2023 (2023). at <https://openreview.net/pdf?id=OQQ1p0pFP4>
Using machine learning to understand age and gender classification based on infant temperament. PLOS ONE 17, e0266026 (2022).
Using fNIRS to Map Functional Specificity in the Infant Brain: An fROI Approach. (2015). SRCD2015_NIRS_poster.pdf (2.14 MB)
SRCD2015_NIRS_poster.pdf (2.14 MB) 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). doi:10.1002/hbm.25815
Using artificial neural networks to ask ‘why’ questions of minds and brains. Trends in Neurosciences 46, 240 - 254 (2023).
On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation. Shared Visual Representations in Human and Machine Intelligence (SVRHM) Workshop at NeurIPS (2021). at <https://openreview.net/forum?id=Rpazl253IHb>
Untangling in Invariant Speech Recognition. Neural Information Processing Systems (NeurIPS 2019) (2019). 9583-untangling-in-invariant-speech-recognition.pdf (2.09 MB)
9583-untangling-in-invariant-speech-recognition.pdf (2.09 MB) Unsupervised Learning of Visual Structure using Predictive Generative Networks. International Conference on Learning Representations (ICLR) (2016). at <http://arxiv.org/pdf/1511.06380v2.pdf>
UNSUPERVISED LEARNING OF VISUAL STRUCTURE USING PREDICTIVE GENERATIVE NETWORKS. (2015). CBMM Memo 040_rev1.pdf (1.92 MB)
CBMM Memo 040_rev1.pdf (1.92 MB) 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)
CBMM Memo No. 001 (940.36 KB) Unsupervised Learning of Invariant Representations in Hierarchical Architectures. (2013). 1311.4158v2.pdf (3.78 MB)
1311.4158v2.pdf (3.78 MB) Unsupervised learning of invariant representations. Theoretical Computer Science (2015). doi:10.1016/j.tcs.2015.06.048
Unsupervised learning of clutter-resistant visual representations from natural videos. (2014). 1409.3879v2.pdf (3.64 MB)
1409.3879v2.pdf (3.64 MB) Unsupervised Discovery of 3D Physical Objects. International Conference on Learning Representations (2021). at <https://openreview.net/forum?id=lf7st0bJIA5>
Universal Dependencies for Learner English. (2016). memo-52_rev1.pdf (472.67 KB)
memo-52_rev1.pdf (472.67 KB) Universal and Non-universal Features of Musical Pitch Perception Revealed by Singing. Current Biology (2019). doi:10.1016/j.cub.2019.08.020
Understanding the Role of Recurrent Connections in Assembly Calculus. (2022). CBMM-Memo-137.pdf (1.49 MB)
CBMM-Memo-137.pdf (1.49 MB) Understanding "almost": Empirical and computational studies of near misses. 38th Annual Meeting of the Cognitive Science Society (2016). Understanding almost (Gerstenberg, Tenenbaum, 2016).pdf (4.08 MB)
Understanding almost (Gerstenberg, Tenenbaum, 2016).pdf (4.08 MB) Uncovering representations of sleep-associated hippocampal ensemble spike activity. Scientific Reports 6, (2016).
Unconscious perception of an opponent's goal. Vision Sciences Society Annual Meeting (VSS 2015) (2015). doi:10.1167/15.12.43
A ubiquitous spectrolaminar motif of local field potential power across the primate cortexAbstract. Nature Neuroscience 27, 547 - 560 (2024).