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/>
A Geometric Analysis of Deep Generative Image Models and Its Applications. Proc. International Conference on Learning Representations, 2021 (2021).
Detecting Semantic Parts on Partially Occluded Objects. British Machine Vision Conference (BMVC) (2017). at <https://bmvc2017.london/proceedings/>
Mesoscopic physiological interactions in the human brain reveal small-world properties. Cell Reports 36, 109585 (2021).
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) 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).
Critical Cues in Early Physical Reasoning. SRCD (2017).
Atoms of recognition in human and computer vision. PNAS 113, 2744–2749 (2016). mirc_author_manuscript_with_figures_and_SI-2.pdf (1.65 MB)
mirc_author_manuscript_with_figures_and_SI-2.pdf (1.65 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).