Artificial Neural Networks and Machine Learning - ICANN 2020 : 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15-18, 2020, Proceedings, Part II / edited by Igor Farkaš, Paolo Masulli, Stefan Wermter.

Format:

Book

Contributor:

Farkaš, Igor., Editor.

Masulli, Paolo, Editor.

Wermter, Stefan, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Theoretical computer science and general issues 2512-2029 ; SL 1, 12397

Theoretical Computer Science and General Issues, 2512-2029 ; 12397

Language:

English

Subjects (All):

Artificial intelligence.

Computer engineering.

Computer networks.

Application software.

Computers.

Image processing-Digital techniques.

Computer vision.

Artificial Intelligence.

Computer Engineering and Networks.

Computer and Information Systems Applications.

Computing Milieux.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Local Subjects:

Artificial Intelligence.

Computer Engineering and Networks.

Computer and Information Systems Applications.

Computing Milieux.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Physical Description:

1 online resource (XXVII, 891 pages) : 402 illustrations, 247 illustrations in color.

Edition:

1st ed. 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

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. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action. *The conference was postponed to 2021 due to the COVID-19 pandemic.

Contents:

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 Stability as 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 for Hierarchical 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.

Other Format:

Printed edition:

ISBN:

978-3-030-61616-8

9783030616168

Access Restriction:

Restricted for use by site license.