A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs
Paper by Lindsey et al.: https://arxiv.org/abs/1901.00945
#Neurons #Cognition #MachineLearning #EvolutionaryComputing
Paper by Lindsey et al.: https://arxiv.org/abs/1901.00945
#Neurons #Cognition #MachineLearning #EvolutionaryComputing
Evolved Art with Transparent, Overlapping, and Geometric Shapes
Berg et al.: https://arxiv.org/abs/1904.06110
#NeuralComputing #EvolutionaryComputing #ArtificialIntelligence
Berg et al.: https://arxiv.org/abs/1904.06110
#NeuralComputing #EvolutionaryComputing #ArtificialIntelligence
Wave Physics as an Analog Recurrent Neural Network
Hughes et al.: https://arxiv.org/abs/1904.12831
#ComputationalPhysics #DeepLearning #MachineLearning #EvolutionaryComputing #Physics
Hughes et al.: https://arxiv.org/abs/1904.12831
#ComputationalPhysics #DeepLearning #MachineLearning #EvolutionaryComputing #Physics
arXiv.org
Wave Physics as an Analog Recurrent Neural Network
Analog machine learning hardware platforms promise to be faster and more energy-efficient than their digital counterparts. Wave physics, as found in acoustics and optics, is a natural candidate...
A Survey on Neural Architecture Search
Wistuba et al.: https://arxiv.org/abs/1905.01392
#MachineLearning #ComputerVision #EvolutionaryComputing
Wistuba et al.: https://arxiv.org/abs/1905.01392
#MachineLearning #ComputerVision #EvolutionaryComputing
Adaptive Neural Trees
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
arXiv.org
Adaptive Neural Trees
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is...
"Cellular automata as convolutional neural networks"
By William Gilpin: https://arxiv.org/abs/1809.02942
#CellularAutomata #NeuralNetworks #NeuralComputing #EvolutionaryComputing #ComputationalPhysics
By William Gilpin: https://arxiv.org/abs/1809.02942
#CellularAutomata #NeuralNetworks #NeuralComputing #EvolutionaryComputing #ComputationalPhysics
Playing Atari with Six Neurons
Cuccu et al.: https://arxiv.org/abs/1806.01363
#MachineLearning #ArtificialIntelligence #EvolutionaryComputing
Cuccu et al.: https://arxiv.org/abs/1806.01363
#MachineLearning #ArtificialIntelligence #EvolutionaryComputing
arXiv.org
Playing Atari with Six Neurons
Deep reinforcement learning, applied to vision-based problems like Atari games, maps pixels directly to actions; internally, the deep neural network bears the responsibility of both extracting...
A Survey on Neural Architecture Search
Wistuba et al.: https://arxiv.org/abs/1905.01392
#MachineLearning #ComputerVision #EvolutionaryComputing
Wistuba et al.: https://arxiv.org/abs/1905.01392
#MachineLearning #ComputerVision #EvolutionaryComputing
Cellular automata as convolutional neural networks"
By William Gilpin: https://arxiv.org/abs/1809.02942
#CellularAutomata #NeuralNetworks #NeuralComputing #EvolutionaryComputing #ComputationalPhysics
By William Gilpin: https://arxiv.org/abs/1809.02942
#CellularAutomata #NeuralNetworks #NeuralComputing #EvolutionaryComputing #ComputationalPhysics
Adaptive Neural Trees
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
arXiv.org
Adaptive Neural Trees
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is...
Adaptive Neural Trees
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition
arXiv.org
Adaptive Neural Trees
Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is...
Sparse Networks from Scratch: Faster Training without Losing Performance
Tim Dettmers and Luke Zettlemoyer: https://arxiv.org/abs/1907.04840
Paper: https://arxiv.org/abs/1907.04840
Blog post: https://timdettmers.com/2019/07/11/sparse-networks-from-scratch/
Code: https://github.com/TimDettmers/sparse_learning
#MachineLearning #NeuralComputing #EvolutionaryComputing
Tim Dettmers and Luke Zettlemoyer: https://arxiv.org/abs/1907.04840
Paper: https://arxiv.org/abs/1907.04840
Blog post: https://timdettmers.com/2019/07/11/sparse-networks-from-scratch/
Code: https://github.com/TimDettmers/sparse_learning
#MachineLearning #NeuralComputing #EvolutionaryComputing
arXiv.org
Sparse Networks from Scratch: Faster Training without Losing Performance
We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance...
A Fine-Grained Spectral Perspective on Neural Networks
Greg Yang and Hadi Salman : https://arxiv.org/abs/1907.10599
Compute eigenvalues : https://github.com/thegregyang/NNspectra
#MachineLearning #NeuralComputing #EvolutionaryComputing
Greg Yang and Hadi Salman : https://arxiv.org/abs/1907.10599
Compute eigenvalues : https://github.com/thegregyang/NNspectra
#MachineLearning #NeuralComputing #EvolutionaryComputing
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn, Pieter Abbeel, Sergey Levine : https://arxiv.org/abs/1703.03400
#MachineLearning #ArtificialIntelligence #EvolutionaryComputing
Chelsea Finn, Pieter Abbeel, Sergey Levine : https://arxiv.org/abs/1703.03400
#MachineLearning #ArtificialIntelligence #EvolutionaryComputing
arXiv.org
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning...
Neuroevolution of Self-Interpretable Agents
Tang et al.: https://arxiv.org/abs/2003.08165
#NeuralComputing #EvolutionaryComputing #MachineLearning
Tang et al.: https://arxiv.org/abs/2003.08165
#NeuralComputing #EvolutionaryComputing #MachineLearning