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Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation Igor V. Tetko

Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation By Igor V. Tetko

Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation by Igor V. Tetko

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Summary

The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019.

Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation Summary

Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part I by Igor V. Tetko

The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions.

Table of Contents

Bidirectional associative memory with block coding: A comparison of iterative retrieval methods.- Stability analysis of a generalised class of BAM neural networks with mixed delays.- Dissipativity Analysis of a Class of Competitive Neural Networks with Proportional Delays.- A Nonlinear Fokker-Planck Description of Continuous Neural Network Dynamics.- Multi-modal associative storage and retrieval using Hopfield auto-associative memory network.- Chaotic Complex-Valued Associative Memory with Adaptive Scaling Factor Independent of Multi-Values.- A Comparative Analysis of Preprocessing Methods for Single-Trial Event Related Potential Detection.- Sleep State Analysis using Calcium Imaging Data by Non-negative Matrix Factorization.- Detection of directional information flow induced by TMS based on symbolic transfer entropy.- Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network.- Distinguishing Violinists and Pianists based on their Brain Signals.- Research on Image-to-Image Translation with Capsule Network.- Multi-View Capsule Network.- Advanced Capsule Networks via Context Awareness.- DDRM-CapsNet: Capsule Network based on Deep Dynamic Routing Mechanism for complex data.- Squeezed Very Deep Convolutional Neural Networks for Text Classification.- NeuroPower: Designing Energy Efficient Convolutional Neural Network Architecture for Embedded Systems.- Swap kernel regression.- Model-Agnostic Explanations for Decisions using Minimal Patterns.- NARPCA: Neural Accumulate-Retract PCA for Low-latency High-throughput Processing on Datastreams.- An Evaluation of Various Regression Models for the Prediction of Two-Terminal Network Reliability.- Capsule Generative Models.- Evaluating CNNs on the Gestalt Principle of Closure.- Recovering Localized Adversarial Attacks.- On the Interpretation of Recurrent Neural Networks as Finite State Machines.- Neural field model for measuring and reproducing time intervals.- Widely Linear Complex-valued Autoencoder: Dealing with Noncircularity in Generative-Discriminative Models.- NatCSNN: A Convolutional Spiking Neural Network for recognition of objects extracted from natural images.- Deep Semantic Asymmetric Hashing.- A Neural Network for Semi-Supervised Learning on Manifolds.- Counting with Analog Neurons.- On the Bounds of Function Approximations.- Probabilistic Bounds for Approximation by Neural Networks.- Tree Memory Networks for Sequence Processing.- On Deep Set Learning and the Choice of Aggregations.- Hilbert Vector Convolutional Neural Network : 2D Neural Network on 1D Data.- The Same Size Dilated Attention Network for Keypoint Detection.- Gradient-Based Learning of Compositional Dynamics with Modular RNNs.- Transfer Learning with Sparse Associative Memories.- Linear Memory Networks.- A Multi-Armed Bandit Algorithm Available in Stationary or Non-Stationary Environments Using Self-Organizing Maps.- Cooperation and Coordination Regimes by Deep Q-Learning in Multi-agent Task Executions.- Boosting Reinforcement Learning with Unsupervised Feature Extraction.- A multi-objective Reinforcement Learning algorithm for JSSP.- A Reinforcement Learning Approach for Sequential Spatial Transformer Networks.- Deep Recurrent Policy Networks for Planning under Partial Observability.- Mixed-Reality Deep Reinforcement Learning for a Reach-to-grasp Task.- FMNet: Multi-Agent Cooperation by Communicating with Featured Message Network.- Inferring Event-Predictive Goal-Directed Object Manipulations in REPRISE.- On Unsupervised Learning of Traversal Cost and Terrain Types Identification using Self-Organizing Maps.- Scaffolding Haptic Attention with Controller Gating.- Benchmarking Incremental Regressors in Traversal Cost Assessment.- CPG driven RBF Network Control with Reinforcement Learning for Gait Optimization of a Dung Beetle-like Robot.- Training Delays in Spiking Neural Networks.- An Izhikevich Model Neuron MOS Circuit for Low Voltage Operation.- UAV Detection: A STDP trained Deep Convolutional Spiking Neural Network Retina-Neuromorphic Approach.- Autonoumous Learning Paradigm for Spiking Neural Networks.- Multi-objective Spiking Neural Network Hardware Mapping Based on Immune Genetic Algorithm.- The Importance of Self-excitation in Spiking Neural Networks Evolved to Recognize Temporal Patterns.- Estimating and factoring the dropout induced distribution with Gaussian mixture model.- Sequence disambiguation with synaptic traces in associative neural networks.- Robust Optimal-Size Implementation of Finite State Automata with Synfire Ring-Based Neural Networks.- A Neural Circuit Model of Adaptive Robust Tracking Control for Continuous-Time Nonlinear Systems.

Additional information

NLS9783030304867
9783030304867
3030304868
Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part I by Igor V. Tetko
Igor V. Tetko
Theoretical Computer Science and General Issues
New
Paperback
Springer Nature Switzerland AG
2019-09-06
839
N/A
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