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ORCID28th ICANN 2019: Munich, Germany - Part II
- Igor V. Tetko
, Vera Kurková, Pavel Karpov, Fabian J. Theis:
Artificial Neural Networks and Machine Learning - ICANN 2019: Deep Learning - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part II. Lecture Notes in Computer Science 11728, Springer 2019, ISBN 978-3-030-30483-6
Feature Selection
- Sijia Niu, Pengfei Zhu, Qinghua Hu, Hong Shi:
Adaptive Graph Fusion for Unsupervised Feature Selection. 3-15 - Rui Ma, Yijie Wang, Li Cheng:
Unsupervised Feature Selection via Local Total-Order Preservation. 16-28 - Tirtharaj Dash, Ashwin Srinivasan, Ramprasad S. Joshi, A. Baskar:
Discrete Stochastic Search and Its Application to Feature-Selection for Deep Relational Machines. 29-45 - Yang Fan, Jianhua Dai, Qilai Zhang, Shuai Liu:
Joint Dictionary Learning for Unsupervised Feature Selection. 46-58 - Alexandra Degeest, Michel Verleysen, Benoît Frénay:
Comparison Between Filter Criteria for Feature Selection in Regression. 59-71 - Vadim Borisov, Johannes Haug, Gjergji Kasneci:
CancelOut: A Layer for Feature Selection in Deep Neural Networks. 72-83 - Nicomedes L. Cavalcanti, Marcelo Rodrigo Portela Ferreira, Francisco de Assis Tenório de Carvalho:
Adaptive- L_2 L 2 Batch Neural Gas. 84-95 - Hiroshi Dozono, Masafumi Tanaka:
Application of Self Organizing Map to Preprocessing Input Vectors for Convolutional Neural Network. 96-100
Augmentation Techniques
- Yuuji Ichisugi, Naoto Takahashi, Hidemoto Nakada, Takashi Sano:
Hierarchical Reinforcement Learning with Unlimited Recursive Subroutine Calls. 103-114 - Ricardo P. M. Cruz, Joaquim F. Pinto da Costa, Jaime S. Cardoso:
Automatic Augmentation by Hill Climbing. 115-124 - Shizheng Qin, Kangzheng Gu, Lecheng Wang, Lizhe Qi, Wenqiang Zhang:
Learning Camera-Invariant Representation for Person Re-identification. 125-137 - Guanghua Tan, Zijun Guo, Yi Xiao:
PA-RetinaNet: Path Augmented RetinaNet for Dense Object Detection. 138-149
Weights Initialization
- Bernhard Bermeitinger, Tomas Hrycej, Siegfried Handschuh:
Singular Value Decomposition and Neural Networks. 153-164 - Aiga Suzuki, Hidenori Sakanashi:
PCI: Principal Component Initialization for Deep Autoencoders. 165-169 - Diego Aguirre, Olac Fuentes:
Improving Weight Initialization of ReLU and Output Layers. 170-184
Parameters Optimisation
- Enzo Tartaglione, Daniele Perlo, Marco Grangetto:
Post-synaptic Potential Regularization Has Potential. 187-200 - Serdar Iplikci, Batuhan Bilgi, Ali Menemen, Bedri Bahtiyar:
A Novel Modification on the Levenberg-Marquardt Algorithm for Avoiding Overfitting in Neural Network Training. 201-207 - Konstantin Berestizshevsky, Guy Even:
Sign Based Derivative Filtering for Stochastic Gradient Descent. 208-219 - Anders Sjöberg, Magnus Önnheim, Emil Gustavsson, Mats Jirstrand:
Architecture-Aware Bayesian Optimization for Neural Network Tuning. 220-231 - Sebastian Bock, Martin Georg Weiß:
Non-convergence and Limit Cycles in the Adam Optimizer. 232-243
Pruning Networks
- Yiqun Duan, Chen Feng:
Learning Internal Dense But External Sparse Structures of Deep Convolutional Neural Network. 247-262 - Yun Li, Luyang Wang, Sifan Peng, Aakash Kumar, Baoqun Yin:
Using Feature Entropy to Guide Filter Pruning for Efficient Convolutional Networks. 263-274 - Tinghuai Wang, Lixin Fan, Huiling Wang:
Simultaneously Learning Architectures and Features of Deep Neural Networks. 275-287 - Niange Yu, Cornelius Weber, Xiaolin Hu:
Learning Sparse Hidden States in Long Short-Term Memory. 288-298 - Chuanguang Yang, Zhulin An, Chao Li, Boyu Diao, Yongjun Xu:
Multi-objective Pruning for CNNs Using Genetic Algorithm. 299-305 - Konstantin Berestizshevsky, Guy Even:
Dynamically Sacrificing Accuracy for Reduced Computation: Cascaded Inference Based on Softmax Confidence. 306-320 - Junxing Hu, Ling Li, Yijun Lin, Fengge Wu, Junsuo Zhao:
Light-Weight Edge Enhanced Network for On-orbit Semantic Segmentation. 321-333 - Yuan Liu, Xi Jia, Linlin Shen, Zhong Ming, Jinming Duan:
Local Normalization Based BN Layer Pruning. 334-346
Search for an Optimal Architecture
- Alexander Goncharenko, Andrey Denisov, Sergey Alyamkin, Evgeny Terentev:
On Practical Approach to Uniform Quantization of Non-redundant Neural Networks. 349-360 - Alexey Alexeev, Yuriy Matveev, Anton Matveev, Dmitry Pavlenko:
Residual Learning for FC Kernels of Convolutional Network. 361-372 - Aftab Anjum, Fengyang Sun, Lin Wang, Jeff Orchard:
A Novel Neural Network-Based Symbolic Regression Method: Neuro-Encoded Expression Programming. 373-386 - Sebastian Litzinger, Andreas Klos, Wolfram Schiffmann:
Compute-Efficient Neural Network Architecture Optimization by a Genetic Algorithm. 387-392 - Shota Saito, Shinichi Shirakawa:
Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures. 393-405
Confidence Estimation
- Almuth Meier, Oliver Kramer:
Predictive Uncertainty Estimation with Temporal Convolutional Networks for Dynamic Evolutionary Optimization. 409-421 - Narendhar Gugulothu, Easwar Subramanian, Sanjay P. Bhat:
Sparse Recurrent Mixture Density Networks for Forecasting High Variability Time Series with Confidence Estimates. 422-433 - Yanyun Tao, Xiang Wang, Yuzhen Zhang:
A Multitask Learning Neural Network for Short-Term Traffic Speed Prediction and Confidence Estimation. 434-449
Continual Learning
- Mingyu Liu, Yijie Wang:
Central-Diffused Instance Generation Method in Class Incremental Learning. 453-465 - Timothée Lesort, Alexander Gepperth, Andrei Stoian, David Filliat:
Marginal Replay vs Conditional Replay for Continual Learning. 466-480 - Alexander Gepperth, Florian Wiech:
Simplified Computation and Interpretation of Fisher Matrices in Incremental Learning with Deep Neural Networks. 481-494 - Christian Limberg, Kathrin Krieger, Heiko Wersing, Helge J. Ritter:
Active Learning for Image Recognition Using a Visualization-Based User Interface. 495-506 - Rudolf J. Szadkowski, Jan Drchal, Jan Faigl:
Basic Evaluation Scenarios for Incrementally Trained Classifiers. 507-517 - Tomas Kuzma, Igor Farkas:
Embedding Complexity of Learned Representations in Neural Networks. 518-528
Metric Learning
- Qinqin Nie, Bin Zhou, Pengfei Zhu, Qinghua Hu, Hao Cheng:
Joint Metric Learning on Riemannian Manifold of Global Gaussian Distributions. 531-542 - Haotian Wu, Bin Zhou, Pengfei Zhu, Qinghua Hu, Hong Shi:
Multi-task Sparse Regression Metric Learning for Heterogeneous Classification. 543-553 - Nutan Chen, Francesco Ferroni, Alexej Klushyn, Alexandros Paraschos, Justin Bayer, Patrick van der Smagt:
Fast Approximate Geodesics for Deep Generative Models. 554-566 - Xianhao He, Peng Qiao, Yong Dou, Xin Niu:
Spatial Attention Network for Few-Shot Learning. 567-578 - Paul Compagnon, Grégoire Lefebvre, Stefan Duffner, Christophe Garcia:
Routine Modeling with Time Series Metric Learning. 579-592
Domain Knowledge Incorporation
- Rajkumar Ramamurthy, Christian Bauckhage, Rafet Sifa, Jannis Schücker, Stefan Wrobel:
Leveraging Domain Knowledge for Reinforcement Learning Using MMC Architectures. 595-607 - Francesco Giannini, Marco Maggini:
Conditions for Unnecessary Logical Constraints in Kernel Machines. 608-620 - Yun-Chieh Tien, Chen-Min Hsu, Fang Yu:
HiSeqGAN: Hierarchical Sequence Synthesis and Prediction. 621-638 - Muhammad Ali Chattha, Shoaib Ahmed Siddiqui, Mohsin Munir, Muhammad Imran Malik, Ludger van Elst, Andreas Dengel, Sheraz Ahmed:
DeepEX: Bridging the Gap Between Knowledge and Data Driven Techniques for Time Series Forecasting. 639-651
Domain Adaptation Approaches
- Yingcan Wei:
Transferable Adversarial Cycle Alignment for Domain Adaption. 655-672 - Michael Schneider, Lichao Wang, Carsten Marr:
Evaluation of Domain Adaptation Approaches for Robust Classification of Heterogeneous Biological Data Sets. 673-686 - Yong Xu, Qi Lu, Muhua Zhu:
Named Entity Recognition for Chinese Social Media with Domain Adversarial Training and Language Modeling. 687-699 - Junjie Yan:
Deep Domain Knowledge Distillation for Person Re-identification. 700-713 - Monika Schak, Alexander Gepperth:
A Study on Catastrophic Forgetting in Deep LSTM Networks. 714-728
Multiclass
- Xiangyang Luo, Xiangying Ran, Wei Sun, Yunlai Xu, Chongjun Wang:
A Label-Specific Attention-Based Network with Regularized Loss for Multi-label Classification. 731-742 - Yun Hu, Mingxue Liao, Pin Lv, Changwen Zheng:
An Empirical Study of Multi-domain and Multi-task Learning in Chinese Named Entity Recognition. 743-754 - Anuran Chakraborty, Rajonya De, Agneet Chatterjee, Friedhelm Schwenker, Ram Sarkar:
Filter Method Ensemble with Neural Networks. 755-765 - Evandro J. R. Silva, Cleber Zanchettin:
Dynamic Centroid Insertion and Adjustment for Data Sets with Multiple Imbalanced Classes. 766-778 - Alexej Klushyn, Nutan Chen, Botond Cseke, Justin Bayer, Patrick van der Smagt:
Increasing the Generalisaton Capacity of Conditional VAEs. 779-791 - Mirko Polato, Guglielmo Faggioli, Ivano Lauriola, Fabio Aiolli:
Playing the Large Margin Preference Game. 792-804
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