Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc

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Improved Multi-hop Reasoning Through Sampling and?Aggregatingeasoning jumps. However, existing approaches still face the challenges of noise and sparsity. This is due to the fact that this issue it is difficult to identify head and tail entities along long and complex paths. To address this issue, we propose a novel multi-hop reasoning model based on Dual Sam

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Learning Solutions of?Stochastic Optimization Problems with?Bayesian Neural Networksameters are unknown or uncertain. Recent research focuses on predicting the value of these unknown parameters using available contextual features, aiming to decrease decision . by adopting end-to-end learning approaches. However, these approaches disregard prediction uncertainty and therefore make t

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Test-Time Augmentation for?Traveling Salesperson Problemson Problem. In general, deep learning models possessing the property of invariance, where the output is uniquely determined regardless of the node indices, have been proposed to learn graph structures efficiently. In contrast, we interpret the permutation of node indices, which exchanges the elemen

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Towards a?Model of?Associative Memory with?Learned Distributed Representationsassociative memory inspired by continuous Modern Hopfield networks. The proposed learning procedure produces distributed representations of the fragments of input data which collectively represent the stored memory patterns, governed by the activation dynamics of the network. This allows for effecti

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Recent Research on Child Neglect observation vector. In practice, when the system is unknown and noisy, an “approximate” nullspace is obtained with a data-driven approach using eigenvalue or singular value decomposition. We tested the classifier on synthetic and real datasets. Results demonstrate the applicability of the method. T

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Handbook of Child Psychopathologysets to adaptively select discriminative query descriptors for specific tasks. Extensive experiments demonstrate that our method outperforms state-of-the-art methods on both general and fine-grained datasets.