Related papers: Self-Attentive Neural Collaborative Filtering
This paper has been withdrawn by the author due to some errors
When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This "overfitting" is greatly reduced by randomly omitting half of the feature detectors on each training case.…
This paper has been withdrawn by the author, due to a significant error in section 4.3.1.
This paper has been withdrawn by the author.
Convolutional neural networks (CNNs) have been successfully used in a range of tasks. However, CNNs are often viewed as "black-box" and lack of interpretability. One main reason is due to the filter-class entanglement -- an intricate…
This paper has been withdrawn by the author due to a crucial error in mathematical derivation and copyrights.
The design, analysis and application of a volumetric convolutional neural network (VCNN) are studied in this work. Although many CNNs have been proposed in the literature, their design is empirical. In the design of the VCNN, we propose a…
This paper has been withdrawn due to errors in the analysis of data with Carrier Access Rate control and statistical methodologies.
We consider the problem of collaborative filtering from a channel coding perspective. We model the underlying rating matrix as a finite alphabet matrix with block constant structure. The observations are obtained from this underlying matrix…
The paper has been withdrawn by the author because the observed effect has a different origin.
This paper has been withdrawn by the authors due to a fatal flaw in the central proof.
Conventional collaborative filtering techniques don't take into consideration the effect of discrepancy in users' rating perception. Some users may rarely give 5 stars to items while others almost always assign 5 stars to the chosen item.…
The paper is withdrawn. The proof has an error and it requires a different approach.
The paper has been withdrawn due to very low reproducibility. About 100 samples have been made, only 3 samples show the superconducting-like behavior. We think it may be an unlikely result.
This paper has been withdrawn by the author due to a gap in the proof of the main result.
This paper has been withdrawn since it was an inadvertant double submission. An updated version of the original submission can be found at quant-ph/0505131
This paper has been withdrawn due to a critical error discovered in Theorem 4.21. Anyone with a historical or pragamatic interest in prior "negative results", however - e.g., failed proof attempts relating to the (in)consistency of ZF or…
With the agreement of my coauthors, I Zhangyang Wang would like to withdraw the manuscript "Stacked Approximated Regression Machine: A Simple Deep Learning Approach". Some experimental procedures were not included in the manuscript, which…
Traditional self-attention mechanisms in convolutional networks tend to use only the output of the previous layer as input to the attention network, such as SENet, CBAM, etc. In this paper, we propose a new attention modification method…
This paper is withdrawn from submission due to a critical error in the proof of main theorem.