Publication
] Scene Graph Parsing as Dependency Parsing. (2018).
CBMM-Memo-082.pdf (869 KB)
CBMM-Memo-082.pdf (869 KB) Mesoscopic physiological interactions in the human brain reveal small-world properties. Cell Reports 36, 109585 (2021).
Semantic Part Segmentation using Compositional Model combing Shape and Appearance. CVPR (2015). JianyuWangSemanticCVPR2015 (1).pdf (6.15 MB)
JianyuWangSemanticCVPR2015 (1).pdf (6.15 MB) 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>
Do Deep Neural Networks Suffer from Crowding?. (2017). CBMM-Memo-069.pdf (6.47 MB)
CBMM-Memo-069.pdf (6.47 MB) Word-level Invariant Representations From Acoustic Waveforms. INTERSPEECH 2014 - 15th Annual Conf. of the International Speech Communication Association (International Speech Communication Association (ISCA), 2014). at <http://www.isca-speech.org/archive/interspeech_2014/i14_2385.html>
Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception. Science Advances 6, eabd4205 (2020). gk7967.pdf (3.07 MB)
gk7967.pdf (3.07 MB) Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?. (2018). CBMM-Memo-095.pdf (1.96 MB)
CBMM-Memo-095.pdf (1.96 MB) Do Neural Networks for Segmentation Understand Insideness?. (2020). CBMM-Memo-105.pdf (4.63 MB) CBMM Memo 105 v2 (July 2, 2020) (3.2 MB) CBMM Memo 105 v3 (January 25, 2022) (8.33 MB)
CBMM-Memo-105.pdf (4.63 MB) CBMM Memo 105 v2 (July 2, 2020) (3.2 MB) CBMM Memo 105 v3 (January 25, 2022) (8.33 MB) Empirical Inference 59 - 69 (Springer Berlin Heidelberg, 2013). doi:10.1007/978-3-642-41136-610.1007/978-3-642-41136-6_7 Author's Version (147.25 KB)
Author's Version (147.25 KB) Implicit regularization with strongly convex bias: Stability and acceleration. Analysis and Applications 21, 165 - 191 (2023).
Teachers recruit mentalizing regions to represent learners’ beliefs. Proceedings of the National Academy of Sciences 120, (2023).
Fast iterative regularization by reusing dataAbstract. Journal of Inverse and Ill-posed Problems (2023). doi:10.1515/jiip-2023-0009
Neural mechanisms supporting facial expressions . unknown (2019).
Predicting actions before they occur. (2015). PredictingActions (1.43 MB)
Supplemental Video 1: Experimental set up and task (16.38 MB) Supplemental Video 2: An example FullVid and CutVid trial clips from experiment 4 (5.47 MB)
PredictingActions (1.43 MB) Supplemental Video 1: Experimental set up and task (16.38 MB) Supplemental Video 2: An example FullVid and CutVid trial clips from experiment 4 (5.47 MB) Thalamic contribution to CA1-mPFC interactions during sleep. Society for Neuroscience's Annual Meeting - SfN 2017 (2017).
AbstractSFNfinal.docx (13.14 KB)
AbstractSFNfinal.docx (13.14 KB) No evidence for prolactin’s involvement in the post-ejaculatory refractory periodAbstract. Communications Biology 4, (2021).
Effort as a bridging concept across action and action understanding: Weight and Physical Effort in Predictions of Efficiency in Other Agents. International Conference on Infant Studies (ICIS) (2016).
Learning physical parameters from dynamic scenes. Cognitive Psychology 104, 57-82 (2018). T-Ullman-etal_CogPsych_LearningPhysicalParametersFromDynamicScenes.pdf (3.15 MB)
T-Ullman-etal_CogPsych_LearningPhysicalParametersFromDynamicScenes.pdf (3.15 MB) Mind Games: Game Engines as an Architecture for Intuitive Physics. Trends in Cognitive Science 21, 649 - 665 (2017). Preprint submitted to Trends in Cognitive Science (17.64 MB)
Preprint submitted to Trends in Cognitive Science (17.64 MB)