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Artificial neural networks and machine learning -- ICANN 2019 : workshop and special sessions : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, proceedings

Artificial neural networks and machine learning -- ICANN 2019 : workshop and special sessions : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, proceedings (Loan 3 times)

Material type
Personal Author
Tetko, Igor V.
Title Statement
Artificial neural networks and machine learning -- ICANN 2019 : workshop and special sessions : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, proceedings / Igor V. Tetko ... [et al.], (eds.).
Publication, Distribution, etc
Cham :   Springer,   c2019.  
Physical Medium
xxxii, 852 p. : ill. ; 24 cm.
Series Statement
Lecture notes in computer science,0302-9743 ; 11731
Bibliography, Etc. Note
Includes bibliographical references and index.
Subject Added Entry-Topical Term
Neural networks (Computer science) --Congresses. Machine learning --Congresses. Artificial intelligence --Congresses.
000 00000nam u2200205 a 4500
001 000046015257
005 20200203155150
008 200131s2019 sz a b 101 0 eng d
020 ▼a 9783030304928
040 ▼a 211009 ▼c 211009 ▼d 211009
082 0 4 ▼a 006.32 ▼2 23
084 ▼a 006.32 ▼2 DDCK
090 ▼a 006.32 ▼b I612 ▼c 28
111 2 ▼a International Conference on Artificial Neural Networks (European Neural Network Society) ▼n (28th : ▼d 2019 : ▼c Munich, Germany)
245 1 0 ▼a Artificial neural networks and machine learning -- ICANN 2019 : ▼b workshop and special sessions : 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, proceedings / ▼c Igor V. Tetko ... [et al.], (eds.).
246 3 0 ▼a ICANN 2019
260 ▼a Cham : ▼b Springer, ▼c c2019.
300 ▼a xxxii, 852 p. : ▼b ill. ; ▼c 24 cm.
490 1 ▼a Lecture notes in computer science, ▼x 0302-9743 ; ▼v 11731
504 ▼a Includes bibliographical references and index.
650 0 ▼a Neural networks (Computer science) ▼v Congresses.
650 0 ▼a Machine learning ▼v Congresses.
650 0 ▼a Artificial intelligence ▼v Congresses.
700 1 ▼a Tetko, Igor V.
830 0 ▼a Lecture notes in computer science ; ▼v 11731.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/Western Books/ Call Number 006.32 I612 28 Accession No. 111823300 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

A Reservoir Computing Framework for Continuous Gesture Recognition.- Using conceptors to transfer between long-term and short-term Memory.- Bistable Perception in Conceptor Networks.- Continual Learning exploiting Structure of Fractal Reservoir Computing.- Continuous Blood Pressure Estimation through Optimized Echo State Networks.- Reservoir Topology in Deep Echo State Networks.- Multiple Pattern Generations and Chaotic Itinerant dynamics in Reservoir Computing.- Echo State Network with Adversarial Training.- Hyper-spherical reservoirs for Echo State Networks.- Echo State vs. LSTM Networks for Word Sense Disambiguation.- Echo State Networks for Named Entity Recognition.- Efficient Cross-Validation of Echo State Networks.- Echo State Property of Neuronal Cell Cultures.- Overview on the PHRESCO project: PHotonic REServoir COmputing.- Classification of Human Actions in Videos with a Large-Scale Photonic Reservoir Computer.- A power-effcient architecture for on-chip reservoir computing.- Time Series Processing with VCSEL-based Reservoir Computer.- Optoelectronic reservoir computing using a mixed digital-analog hardware implementation.- Comparison of Feature Extraction Techniques for Handwritten Digit Recognition with a Photonic Reservoir Computer.- Polarization dynamics of VCSELs improves reservoir computing performance..- Reservoir-size dependent learning in analogue neural networks.- Transferring reservoir computing: formulation and application to fluid physics.- Investigation of EEG-based Graph-theoretic Analysis for Automatic Diagnosis of Alcohol Use Disorder .- EchoQuan-Net: Direct Quantification of Echo Sequence for Left Ventricle Multidimensional Indices via Global-Local Learning, Geometric Adjustment, and multi-target relation learning.- An attention-based ID-CNNs-CRF model for named entity recognition on clinical electronic medical records.- Deep Text Prior: Weakly Supervised Learning for Assertion Classification.- Inter-region Synchronization Analysis based on Heterogeneous Matrix Similarity Measurement.- Bi-ResNet: Fully automated classification of unregistered contralateral mammograms.- Pattern Recognition for COPD Diagnostics Using an Artificial Neural Network and Its Potential Integration on Hardware-based Neuromorphic Platforms.- Quantifying Structural Heterogeneity of Healthy and Cancerous Mitochondria using a Combined Segmentation and Classification USK-Net.- Breast Cancer Classification on Histopathological Images Affected by Data Imbalance Using Active Learning and Deep Convolutional Neural Network.- Measuring the Angle of Hallux Valgus Using Segmentation of Bones on X-ray Images.- Human Body Posture Recognition Using Wearable Devices.- Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction.- On Chow-Liu forest based regularization of deep belief networks.- Prototypes within Minimum Enclosing Balls.- Exploring Local Transformation Shared Weights in Convolutional Neural Networks.- The Good, the Bad and the Ugly: augmenting a black-box model with expert knowledge.- Hierarchical Attentional Hybrid Neural Networks for Document Classification.- Reinforcement learning informed by optimal control.- Explainable Anomaly Detection via Feature-Based Localization.- Bayesian Automatic Relevance Determination for Feature selection in Credit Default Modelling.- TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features.- DeepMimic: Mentor-Student Unlabeled Data Based Training.- Evaluation of tag clusterings for user profiling in movie recommendation.- A Sparse Filtering-based Approach for Non-Blind Deep Image Denoising.- Hybrid Attention Driven Text-to-Image Synthesis via Generative Adversarial Networks.- Hypernetwork functional image representation.- Instance-based Segmentation for Boundary Detection of Neuropathic Ulcers through Mask-RCNN.- Capsule Networks for attention under occlusion.- IP-GAN: Learning Identity and Pose Disentanglement in Generative Adversarial Networks.- Hypernetwork Knowledge Graph Embeddings.- Signed Graph Attention Networks.- Graph Classification with 2D Convolutional Neural Networks.- Community Detection via Joint Graph Convolutional Network Embedding in Attribute Network.- Temporal Coding of Neural Stimuli.- Heterogeneous Information Network Embedding with Meta-path-based Graph Attention Networks.- Dual-FOFE-net Neural Models for Entity Linking with PageRank.- Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition.- Graph Convolutional Networks Improve the Prediction of Cancer Driver Genes.- CNN-Based Semantic Change Detection in Satellite Imagery.- Axiomatic Kernels on Graphs for Support Vector Machines.- Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network.- Neural Network Guided Tree-Search Policies for Synthesis Planning .- LSTM and 1-D Convolutional Neural Networks for predictive monitoring of the anaerobic digestion process.- Progressive Docking - a Deep Learning Based Approach for Accelerated Virtual Screening.- Predictive Power of Time-series Based Machine Learning Models for DMPK Measurements in Drug Discovery.- Improving Deep Generative Models with Randomized SMILES.- Attention and Edge Memory Convolution for Bioactivity Prediction.- Application of materials informatics tools to the analysis of combinatorial libraries of all metal-oxides photovoltaic cells.- Analysis and Modelling of False Positives in GPCR Assays.- Characterization of Quantum Derived Electronic Properties of Molecules: A Computational Intelligence Approach.- Using an Autoencoder for Dimensionality Reduction in Quantum Dynamics.- Conformational Oversampling as Data Augmentation for Molecules.- Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks.- Deep Neural Network Architecture for Drug-Target Interaction Prediction.- Mol-CycleGAN - a generative model for molecular optimization.- A TRANSFORMER MODEL FOR RETROSYNTHESIS.- Augmentation is What You Need!.- Diversify Libraries Using Generative Topographic Mapping.- Detection of Frequent-Hitters across various HTS Technologies.- Message Passing Neural Networks scoring functions for structure-based drug discovery.

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