Export 848 results:
Complexity Control by Gradient Descent in Deep Networks. Nature Communications 11, (2020). s41467-020-14663-9.pdf (431.68 KB)
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> Foveated_Drone_SVRHM_2020.pdf (13.44 MB) v1 (12/15/2020) (14.7 MB)
Deep compositional robotic planners that follow natural language commands. (2020). CBMM-Memo-124.pdf (1.03 MB)
Deep compositional robotic planners that follow natural language commands . International Conference on Robotics and Automation (ICRA) (2020).
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)
Dreaming with ARC. Learning Meets Combinatorial Algorithms workshop at NeurIPS 2020 (2020). CBMM Memo 113.pdf (1019.64 KB)
Efficient inverse graphics in biological face processing. Science Advances 6, eaax5979 (2020). eaax5979.full_.pdf (3.22 MB)
Emergence of Pragmatic Reasoning From Least-Effort Optimization . 13th International Conference on the Evolution of Language (EvoLang) (2020).
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020). doi:10.1109/IROS45743.2020.9341325
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas. (2020). CBMM-Memo-125.pdf (2.12 MB)
Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition . COSYNE (2020).
An Exit Strategy from the Covid-19 Lockdown based on Risk-sensitive Resource Allocation. (2020). CBMM-Memo-106.pdf (431.13 KB)
Explicit regularization and implicit bias in deep network classifiers trained with the square loss. arXiv (2020). at <https://arxiv.org/abs/2101.00072>
Face selective patches in marmoset frontal cortexAbstract. Nature Communications 11, (2020).
Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition. Neuron (2020). doi:10.1016/j.neuron.2020.09.035 PIIS0896627320307595.pdf (3.92 MB)
The fine structure of surprise in intuitive physics: when, why, and how much?. Proceedings of the 42th Annual Meeting of the Cognitive Science Society - Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020, virtual, July 29 - August 1, 2020 ( ) (2020). at <https://cogsci.mindmodeling.org/2020/papers/0761/index.html>
For interpolating kernel machines, the minimum norm ERM solution is the most stable. (2020). CBMM_Memo_108.pdf (1015.14 KB) Better bound (without inequalities!) (1.03 MB)
Function approximation by deep networks. Communications on Pure & Applied Analysis 19, 4085 - 4095 (2020). 1534-0392_2020_8_4085.pdf (514.57 KB)
Gross means Great. Progress in Neurobiology 195, 101924 (2020).
Hierarchical neural network models that more closely match primary visual cortex tend to better explain higher level visual cortical responses . COSYNE (2020).
Hierarchical structure is employed by humans during visual motion perception. Proceedings of the National Academy of Sciences 117, 24581 - 24589 (2020).
Hierarchically Local Tasks and Deep Convolutional Networks. (2020). CBMM_Memo_109.pdf (2.12 MB)
Hippocampal remapping as hidden state inference. eLife 9, (2020).
Implicit dynamic regularization in deep networks. (2020). v1.2 (2.29 MB) v.59 Update on rank (2.43 MB)
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)