Related papers: Self-Attentive Neural Collaborative Filtering
One of the key differences between the learning mechanism of humans and Artificial Neural Networks (ANNs) is the ability of humans to learn one task at a time. ANNs, on the other hand, can only learn multiple tasks simultaneously. Any…
Self-attention module shows outstanding competence in capturing long-range relationships while enhancing performance on vision tasks, such as image classification and image captioning. However, the self-attention module highly relies on the…
This paper has been withdrawn by the author(s), due to a crucial error in eq. 6.
Machine learning is facing a 'reproducibility crisis' where a significant number of works report failures when attempting to reproduce previously published results. We evaluate the sources of reproducibility failures using a meta-analysis…
Modern recommender systems employ various sequential modules such as self-attention to learn dynamic user interests. However, these methods are less effective in capturing collaborative and transitional signals within user interaction…
Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation performance. Current approaches to solving the problem usually…
This paper has been withdrawn by the authors due to a crucial error.
This paper has been withdrawn by the author due to the incorrect argument for the security.
This paper has been withdrawn by the corresponding author because the newest version is now published in Journal of Discrete Algorithms.
Inattentional blindness is the psychological phenomenon that causes one to miss things in plain sight. It is a consequence of the selective attention in perception that lets us remain focused on important parts of our world without…
State-of-the-art results on neural machine translation often use attentional sequence-to-sequence models with some form of convolution or recursion. Vaswani et al. (2017) propose a new architecture that avoids recurrence and convolution…
This paper has been withdrawn by the author due to a crucial sign error in equation 1
Unfortunately, after an imprudent sumbission of the paper to the e-print archive, I discovered in it many serious mistakes. So I draw back it .
In this study, we introduce Convolutional Transformer Neural Collaborative Filtering (CTNCF), a novel approach aimed at enhancing recommendation systems by effectively capturing high-order structural information in user-item interactions.…
This paper has been withdrawn by the author due to unspecified problems.
Detection of object anomalies is crucial in industrial processes, but unsupervised anomaly detection and localization is particularly important due to the difficulty of obtaining a large number of defective samples and the unpredictable…
This article is withdrawn because of a mistake in the main result of the paper.
This paper has been withdrawn due to an error found by Dana Angluin and Lev Reyzin.
This paper has been withdrawn due to a crucial theoretical and experimental error.
This paper has been withdrawn. See published paper http://arxiv.org/math.HO/0512390