Publication
] Learning a Natural-language to LTL Executable Semantic Parser for Grounded Robotics. (Proceedings of Conference on Robot Learning (CoRL-2020), 2020). at <https://corlconf.github.io/paper_385/>
Detecting Semantic Parts on Partially Occluded Objects. (2017).
CBMM-Memo-078.pdf (1.74 MB)
CBMM-Memo-078.pdf (1.74 MB) A Geometric Analysis of Deep Generative Image Models and Its Applications. Proc. International Conference on Learning Representations, 2021 (2021).
Robust Estimation of 3D Human Poses from a Single Image. (2014). CBMM-Memo-013.pdf (510.23 KB)
CBMM-Memo-013.pdf (510.23 KB) 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) 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) 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) Implicit regularization with strongly convex bias: Stability and acceleration. Analysis and Applications 21, 165 - 191 (2023).
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) 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).
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) 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).