Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2021; 30th International C Igor Farka?,Paolo Masulli,Stefan Wermter Conference proc

Inflammation

松果

https://doi.org/10.1007/978-3-662-07200-4th limited information. In this paper, fused multi-embedded features are employed to enhance the representations of short texts. Then, a denoising autoencoder with an attention layer is adopted to extract low-dimensional features from the multi-embeddings against the disturbance of noisy texts. Furt

malign

A. Herbert Fritz,Günter Schulze we study the brain-like Bayesian Confidence Propagating Neural Network (BCPNN) model, recently extended to extract sparse distributed high-dimensional representations. The usefulness and class-dependent separability of the hidden representations when trained on MNIST and Fashion-MNIST datasets is s

Debate

Alfred Herbert Fritz,J?rg Schmützcircuit based on the Izhikevich neuron model is designed to reproduce various types of spikes and is optimized for low-voltage operation. Simulation results indicate that the proposed circuit successfully operates in the subthreshold region and can be utilized for reservoir computing.

STING

https://doi.org/10.1007/978-3-642-84009-8h is a promising alternative for deep neural networks (DNNs) with high energy consumption. SNNs have reached competitive results compared to DNNs in relatively simple tasks and small datasets such as image classification and MNIST/CIFAR, while few studies on more challenging vision tasks on complex

防止

CuRL: Coupled Representation Learning of Cards and Merchants to Detect Transaction Frauds nodes. Moreover, scaling graph-learning algorithms and using them for real-time fraud scoring is an open challenge..In this paper, we propose . and ., coupled representation learning methods that can effectively capture the higher-order interactions in a bipartite graph of payment entities. Instead

返老還童

Between

審問

SiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object Trackingor further improvements. SiamSNN is the first deep SNN tracker that achieves short latency and low precision loss on the visual object tracking benchmarks OTB2013/2015, VOT2016/2018, and GOT-10k. Moreover, SiamSNN achieves notably low energy consumption and real-time on Neuromorphic chip TrueNorth.

他日關(guān)稅重重

Artificial Neural Networks and Machine Learning – ICANN 202130th International C