79 citations · 440 across the 39 of their papers we have counts for
5 papers · 1 filter
Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics
Biye Jiang, David M. Chan, Tianhao Zhang +1
The internal states of most deep neural networks are difficult to interpret, which makes diagnosis and debugging during training challenging. Activation maximization methods are wi…
Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making
Daniel Seita, Nawid Jamali, Michael Laskey +6
A fundamental challenge in manipulating fabric for clothes folding and textiles manufacturing is computing "pick points" to effectively modify the state of an uncertain manifold. W…
Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data
Xinlei Pan, Sung-Li Chiang, John Canny
Self-driving vehicle vision systems must deal with an extremely broad and challenging set of scenes. They can potentially exploit an enormous amount of training data collected from…
t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data
David M. Chan, Roshan Rao, Forrest Huang +1
Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples. Existing visualization methods…
Textual Explanations for Self-Driving Vehicles
Jinkyu Kim, Anna Rohrbach, Trevor Darrell +2
Deep neural perception and control networks have become key components of self-driving vehicles. User acceptance is likely to benefit from easy-to-interpret textual explanations wh…