Unconstrained Monotonic Neural Networks
Antoine Wehenkel and Gilles Louppe : https://arxiv.org/abs/1908.05164
#NeuralNetworks #MachineLearning #NeuralComputing
Antoine Wehenkel and Gilles Louppe : https://arxiv.org/abs/1908.05164
#NeuralNetworks #MachineLearning #NeuralComputing
BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networks
Hakhamaneshi et al.: https://arxiv.org/abs/1907.10515
#SignalProcessing #MachineLearning #NeuralComputing
Hakhamaneshi et al.: https://arxiv.org/abs/1907.10515
#SignalProcessing #MachineLearning #NeuralComputing
Single Headed Attention RNN: Stop Thinking With Your Head
Stephen Merity : https://arxiv.org/abs/1911.11423
#ArtificialIntelligence #NeuralComputing #NLP
Stephen Merity : https://arxiv.org/abs/1911.11423
#ArtificialIntelligence #NeuralComputing #NLP
arXiv.org
Single Headed Attention RNN: Stop Thinking With Your Head
The leading approaches in language modeling are all obsessed with TV shows of my youth - namely Transformers and Sesame Street. Transformers this, Transformers that, and over here a bonfire worth...
Single Headed Attention RNN: Stop Thinking With Your Head
Stephen Merity : https://arxiv.org/abs/1911.11423
#ArtificialIntelligence #NeuralComputing #NLP
Stephen Merity : https://arxiv.org/abs/1911.11423
#ArtificialIntelligence #NeuralComputing #NLP
arXiv.org
Single Headed Attention RNN: Stop Thinking With Your Head
The leading approaches in language modeling are all obsessed with TV shows of my youth - namely Transformers and Sesame Street. Transformers this, Transformers that, and over here a bonfire worth...
Network of Evolvable Neural Units: Evolving to Learn at a Synaptic Level
Paul Bertens, Seong-Whan Lee : https://arxiv.org/abs/1912.07589
#NeuralComputing #MachineLearning #ArtificialIntelligence
Paul Bertens, Seong-Whan Lee : https://arxiv.org/abs/1912.07589
#NeuralComputing #MachineLearning #ArtificialIntelligence
arXiv.org
Network of Evolvable Neural Units: Evolving to Learn at a Synaptic Level
Although Deep Neural Networks have seen great success in recent years through
various changes in overall architectures and optimization strategies, their
fundamental underlying design remains...
various changes in overall architectures and optimization strategies, their
fundamental underlying design remains...
"Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm"
Chelsea Finn and Sergey Levine : https://arxiv.org/abs/1710.11622
#MachineLearning #ArtificialIntelligence #MetaLearning #NeuralComputing
Chelsea Finn and Sergey Levine : https://arxiv.org/abs/1710.11622
#MachineLearning #ArtificialIntelligence #MetaLearning #NeuralComputing
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
Playing Atari with Six Neurons
Cuccu et al.: https://arxiv.org/abs/1806.01363
#MachineLearning #ArtificialIntelligence #NeuralComputing
Cuccu et al.: https://arxiv.org/abs/1806.01363
#MachineLearning #ArtificialIntelligence #NeuralComputing
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...