3.5k citations · 7.7k across the 28 of their papers we have counts for
4 papers · 1 filter
Hamiltonian Neural Networks
Sam Greydanus, Misko Dzamba, Jason Yosinski
Even though neural networks enjoy widespread use, they still struggle to learn the basic laws of physics. How might we endow them with better inductive biases? In this paper, we dr…
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Anh Nguyen, Alexey Dosovitskiy, Jason Yosinski +2
Deep neural networks (DNNs) have demonstrated state-of-the-art results on many pattern recognition tasks, especially vision classification problems. Understanding the inner working…
Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks
Anh Nguyen, Jason Yosinski, Jeff Clune
We can better understand deep neural networks by identifying which features each of their neurons have learned to detect. To do so, researchers have created Deep Visualization tech…
Hands-free Evolution of 3D-printable Objects via Eye Tracking
Nick Cheney, Jeff Clune, Jason Yosinski +1
Interactive evolution has shown the potential to create amazing and complex forms in both 2-D and 3-D settings. However, the algorithm is slow and users quickly become fatigued. We…