4 citations · 7 across the 6 of their papers we have counts for
7 papers
Spiking Approximations of the MaxPooling Operation in Deep SNNs
Ramashish Gaurav, Bryan Tripp, Apurva Narayan
Spiking Neural Networks (SNNs) are an emerging domain of biologically inspired neural networks that have shown promise for low-power AI. A number of methods exist for building deep…
Neuron-based explanations of neural networks sacrifice completeness and interpretability
Nolan Dey, Eric Taylor, Alexander Wong +2
High quality explanations of neural networks (NNs) should exhibit two key properties. Completeness ensures that they accurately reflect a network's function and interpretability ma…
Active Perception and Representation for Robotic Manipulation
Youssef Zaky, Gaurav Paruthi, Bryan Tripp +1
The vast majority of visual animals actively control their eyes, heads, and/or bodies to direct their gaze toward different parts of their environment. In contrast, recent applicat…
A dataset of 40K naturalistic 6-degree-of-freedom robotic grasp demonstrations
Rajan Iyengar, Victor Reyes Osorio, Presish Bhattachan +2
Modern approaches to grasp planning often involve deep learning. However, there are only a few large datasets of labelled grasping examples on physical robots, and available datase…
Convolutional Neural Networks Regularized by Correlated Noise
Shamak Dutta, Bryan Tripp, Graham Taylor
Neurons in the visual cortex are correlated in their variability. The presence of correlation impacts cortical processing because noise cannot be averaged out over many neurons. In…
Similarities and differences between stimulus tuning in the inferotemporal visual cortex and convolutional networks
Bryan Tripp
Deep convolutional neural networks (CNNs) trained for object classification have a number of striking similarities with the primate ventral visual stream. In particular, activity i…
A Rich Source of Labels for Deep Network Models of the Primate Dorsal Visual Stream
Omid Rezai, Pinar Boyraz Jentsch, Bryan Tripp
Deep convolutional neural networks (CNNs) have structures that are loosely related to that of the primate visual cortex. Surprisingly, when these networks are trained for object cl…