Sitemap

• Spatial Embedding to optimize RNN topology for control tasks.
• SOTA performance on simple tasks using 1.46% of the weights
• 66% SOTA performance on complex tasks using 0.06% of the weights (200 vs. 350.000)

This paper is about inside-out infrared marker tracking via head mounted displays for smart robot programming.

• Survey on low-power neural networks for predictive maintenance.

• Deep learning neuromorphic model for neural decoding of primate motor commands.
• Model optimization for resource constrained environments.

• Deep learning neuromorphic model for Sleep Stage Detection.
• Comparison with traditional Deep Learning Model.
• New SOTA Performance on Sleep Stage Detection.

• Realtime implementation of the neural decoding model from the previous publication.
• SOTA Performance on the Primate Reaching Dataset

• Open-source framework for converting numerical data into sparse signals.

• The benefits of Hardware-Software co-design on Neural Network energy consumption.
• Up to 4000x less estimated energy

• Spatial Embedding to reduce trainable parameters from O(N²) to O(N)
• Neuron positions are optimized via backprop

• Spatial Embedding to optimize RNN topology for control tasks.
• SOTA performance on simple tasks using 1.46% of the weights
• 66% SOTA performance on complex tasks using 0.06% of the weights (200 vs. 350.000)
• Up to 5000x less estimated energy

This paper is about the efficacy of spiking neural networks for intrusion detection systems.

• Model-based RL based on Dreamer.