Artificial Neural Networks and Machine Learning – ICANN 2020

29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II

AvIgor Farkaš,Paolo Masulli

Häftad, Engelska, 2020

Del 12397 i serien Lecture Notes in Computer Science

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Beskrivning

The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, held in Bratislava, Slovakia, in September 2020.*The total of 139 full papers presented in these proceedings was carefully reviewed and selected from 249 submissions.

Produktinformation

Utgivningsdatum:2020-10-17
Mått:155 x 235 x 49 mm
Vikt:1 364 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:891
Förlag:Springer Nature Switzerland AG
ISBN:9783030616151

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Informationsteknik: allmänt inom Data och IT
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Innehållsförteckning

Model Compression I.- Fine-grained Channel Pruning for Deep Residual Neural Networks.- A Lightweight Fully Convolutional Neural Network of High Accuracy Surface Defect Detection.- Detecting Uncertain BNN Outputs on FPGA Using Monte Carlo Dropout Sampling.- Neural network compression via learnable wavelet transforms.- Fast and Robust Compression of Deep Convolutional Neural Networks.- Model Compression II.- Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima.- Log-Nets: Logarithmic Feature-Product Layers Yield More Compact Networks.- Tuning Deep Neural Network's hyperparameters constrained to deployability on tiny systems.- Obstacles to Depth Compression of Neural Networks.- Multi-task and Multi-label Learning.- Multi-Label Quadruplet Dictionary Learning.- Pareto Multi-Task Deep Learning.- Convex Graph Laplacian Multi-Task Learning SVM.- Neural Network Theory and Information Theoretic Learning.- Prediction Stabilityas a Criterion in Active Learning.- Neural Spectrum Alignment: Empirical Study.- Nonlinear, Nonequilibrium Landscape Approach to Neural Network Dynamics.- Hopfield Networks for Vector Quantization.- Prototype-Based Online Learning on Homogeneously Labeled Streaming Data.- Normalization and Regularization Methods.- Neural Network Training with Safe Regularization in the Null Space of Batch Activations.- The Effect of Batch Normalization in the Symmetric Phase.- Regularized Pooling.- Reinforcement Learning I.- Deep Recurrent Deterministic Policy Gradient for Physical Control.- Exploration via Progress-Driven Intrinsic Rewards.- An improved reinforcement learning based heuristic dynamic programming algorithm for model-free optimal control.- PBCS: Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning.- Understanding failures of deterministic actor-critic with continuous action spaces and sparse rewards.- Reinforcement Learning II.- GAN-based Planning Model in Deep Reinforcement Learning.- Guided Reinforcement Learning via Sequence Learning.- Neural Machine Translation based on Improved Actor-Critic Method.- Neural Machine Translation based on Prioritized Experience Replay.- Improving Multi-Agent Reinforcement Learning with Imperfect Human Knowledge.- Reinforcement Learning III.- Adaptive Skill Acquisition in Hierarchical Reinforcement Learning.- Social Navigation with Human Empowerment driven Deep Reinforcement Learning.- Curious Hierarchical Actor-Critic Reinforcement Learning.- Policy Entropy for Out-of-Distribution Classification.- Reservoir Computing.- Analysis of reservoir structure contributing to robustness against structural failure of Liquid State Machine.- Quantifying robustness and capacity of reservoir computers with consistency profiles.- Two-Step FORCE Learning Algorithm for Fast Convergence in Reservoir Computing.- Morphological Computation of Skin Focusing on Fingerprint Structure.- Time Series Clustering with Deep Reservoir Computing.- ReservoirPy: an Efficient and User-Friendly Library to Design Echo State Networks.- Robotics and Neural Models of Perception and Action.- Adaptive, Neural Robot Control – Path Planning on 3D Spiking Neural Networks.- CABIN: A Novel Cooperative Attention Based Location Prediction Network Using Internal-External Trajectory Dependencies.- Neuro-Genetic Visuomotor Architecture for Robotic Grasping.- From Geometries to Contact Graphs.- Sentiment Classification.- Structural Position Network for Aspect-based Sentiment Classification.- Cross-Domain Sentiment Classification using Topic Attention and Dual-Task Adversarial Training.- Data Augmentation for Sentiment Analysis in English – the Online Approach.- Spiking Neural Networks I.- Dendritic computation in a point neuron model..- Benchmarking Deep Spiking Neural Networks on Neuromorphic Hardware.- Unsupervised Learning of Spatio-Temporal Receptive Fields from an Event-Based Vision Sensor.- Spike-Train Level Unsupervised Learning Algorithm for Deep Spiking Belief Networks.- Spiking Neural Networks II.- Modelling Neuromodulated Information Flow and Energetic Consumption at Thalamic Relay Synapses.- Learning Precise Spike Timings with Eligibility Traces.- Meta-STDP rule stabilizes synaptic weights under in vivo-like ongoing spontaneous activity in a computational model of CA1 pyramidal cell.- Adaptive Chemotaxis for improved Contour Tracking using Spiking Neural Networks.- Text Understanding I.- Mental Imagery-Driven Neural Network to Enhance Representation for Implicit Discourse Relation Recognition.- Adaptive Convolution Kernel for Text Classification via Multi-Channel Representations.- Text generation in discrete space.- Short text processing for analyzing user portraits: A dynamic combination.- Text Understanding II.- A Hierarchical Fine-Tuning Approach Based on Joint Embedding of Words and Parent Categories forHierarchical Multi-label Text Classification.- Boosting Tricks for Word Mover’s Distance.- Embedding Compression with Right Triangle Similarity Transformations.- Neural Networks for Detecting Irrelevant Questions during Visual Question Answering.- F-Measure Optimisation and Label Regularisation for Energy-based Neural Dialogue State Tracking Models.- Unsupervised Learning.- Unsupervised Change Detection using Joint Autoencoders for Age-Related Macular Degeneration Progression.- A fast algorithm to find Best Matching Units in Self-Organizing Maps.- Tumor Characterization using Unsupervised Learning of Mathematical Relations within Breast Cancer Data.- Balanced SAM-kNN: Online Learning with Heterogeneous Drift and Imbalanced Data.- A Rigorous Link Between Self-Organizing Maps and Gaussian Mixture Models.- Collaborative Clustering through Optimal Transport.