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

ALTER

Binding and Perspective Taking as Inference in a Generative Neural Network Model, and (ii) further back onto perspective taking neurons, which rotate and translate the input features. Evaluations show that the resulting gradient-based inference process solves the perspective taking and binding problems in the considered motion domains, essentially yielding a Gestalt perception

Myelin

Dilated Residual Aggregation Network for Text-Guided Image Manipulationlluting the text-irrelevant image regions by combining triplet attention mechanism and central biasing instance normalization. Quantitative and qualitative experiments conducted on the CUB-200-2011 and Oxford-102 datasets demonstrate the superior performance of the proposed DRA.

neutral-posture

Cantankerous

crockery

Relevance-Aware Q-matrix Calibration for Knowledge Tracingnectivity between exercises and KCs for obtaining a potential KC list. Then, we propose a Q-matrix calibration method by using relevance scores between exercises and KCs to mitigate the problem of subjective bias existed in human-labeled Q-matrix. After that, the embedding of each exercise aggregate

费解

LGACN: A Light Graph Adaptive Convolution Network for Collaborative Filteringt Graph Adaptive Convolution Network), including the most important component in GCN - neighborhood aggregation and layer combination - for collaborative filtering and alter them to fit recommendations. Specifically, LGACN learns user and item embeddings by propagating their positive and negative in

Receive

HawkEye: Cross-Platform Malware Detection with Representation Learning on Graphsning-based classifier to create a malware detection system. We evaluate . by testing real samples on different platforms and operating systems, including Linux (x86, x64, and ARM-32), Windows (x86 and x64), and Android. The results outperform most of the existing works with an accuracy of 96.82% on

abysmal

BUDGE

细节