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Related: TFIDF
[1801.00119] Towards co-evolution of fitness predictors and Deep Neural Networks[1707.00703] Automated Problem Identification: Regression vs Classification via Evolutionary Deep Networks[1907.01698] HyperNOMAD: Hyperparameter optimization of deep neural networks using mesh adaptive direct search[1807.02816] Improving Deep Learning through Automatic Programming[1810.05526] Automatic Configuration of Deep Neural Networks with EGO[1709.09161] EDEN: Evolutionary Deep Networks for Efficient Machine Learning[1809.10244] Autonomously and Simultaneously Refining Deep Neural Network Parameters by a Bi-Generative Adversarial Network Aided Genetic Algorithm[1908.10714] Automated Architecture Design for Deep Neural Networks[1711.05189] CryptoDL: Deep Neural Networks over Encrypted Data[1901.06261] NeuNetS: An Automated Synthesis Engine for Neural Network Design
Mentions
[1408.5093] Caffe: Convolutional Architecture for Fast Feature Embedding[1806.10282] Auto-Keras: An Efficient Neural Architecture Search System[1802.01548] Regularized Evolution for Image Classifier Architecture Search[1611.02167] Designing Neural Network Architectures using Reinforcement Learning[1708.05552] Practical Block-wise Neural Network Architecture Generation[1712.00559] Progressive Neural Architecture Search[1703.00548] Evolving Deep Neural Networks[1703.01041] Large-Scale Evolution of Image Classifiers[1712.05889] Ray: A Distributed Framework for Emerging AI Applications[1506.00019] A Critical Review of Recurrent Neural Networks for Sequence Learning
Related: Semantic Math
[1303.2975] Towards Automated Proof Strategy Generalisation[1412.6154] Effective persistent homology of digital images[1412.6154] Effective persistent homology of digital images[1301.0302] MANCaLog: A Logic for Multi-Attribute Network Cascades (Technical Report)[1709.00322] Disintegration and Bayesian Inversion via String Diagrams[1706.02413] PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space[1706.02413] PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space[1803.05316] Seven Sketches in Compositionality: An Invitation to Applied Category Theory[1909.01795] Stochastic Submodular Probing with State-Dependent Costs[1604.06162] The Extended Littlestone's Dimension for Learning with Mistakes and Abstentions