Soft BPR Loss for Dynamic Hard Negative Sampling in Recommender Systems

In recommender systems, leveraging Graph Neural Networks (GNNs) to formulate the bipartite relation between users and items is a promising way. However, powerful negative sampling methods that is adapted to GNN-based recommenders still requires a lot of efforts. One critical gap is that it is rather tough to distinguish real negatives from massive unobserved items during hard negative sampling. Towards this problem, this paper develops a novel hard negative sampling method for GNN-based recommendation systems by simply reformulating the loss function. We conduct various experiments on three datasets, demonstrating that the method proposed outperforms a set of state-of-the-art benchmarks.Comment: 9 pages, 16 figure

Data Upcycling Knowledge Distillation for Image Super-Resolution

Knowledge distillation (KD) emerges as a challenging yet promising technique for compressing deep learning models, characterized by the transmission of extensive learning representations from proficient and computationally intensive teacher models to compact student models. However, only a handful of studies have endeavored to compress the models for single image super-resolution (SISR) through KD, with their effects on student model enhancement remaining marginal. In this paper, we put forth an approach from the perspective of efficient data utilization, namely, the Data Upcycling Knowledge Distillation (DUKD) which facilitates the student model by the prior knowledge teacher provided via upcycled in-domain data derived from their inputs. This upcycling process is realized through two efficient image zooming operations and invertible data augmentations which introduce the label consistency regularization to the field of KD for SISR and substantially boosts student model's generalization. The DUKD, due to its versatility, can be applied across a broad spectrum of teacher-student architectures. Comprehensive experiments across diverse benchmarks demonstrate that our proposed DUKD method significantly outperforms previous art, exemplified by an increase of up to 0.5dB in PSNR over baselines methods, and a 67% parameters reduced RCAN model's performance remaining on par with that of the RCAN teacher model

Social Cognitive Role of Schizophrenia Candidate Gene GABRB2

10.1371/journal.pone.0062322PLoS ONE84

A semi-supervised density peaks clustering algorithm

Density peaks clustering (DPC) is a density-based unsupervised clustering algorithm with the advantages of fast clustering capacity for arbitrary shape data and easy implementation without iteration. However, in practice, a small amount of label information might be partially available but not sufficient to be used to generate supervised learning. Semi-supervised clustering is often adopted to incorporate such partial information. In this paper, a novel semisupervised density peaks clustering algorithm (SS-DPC) is proposed to extend the classical density peaks clustering algorithm to the semi-supervised clustering. In contrast to DPC, SS-DPC uses prior information in the form of class labels to guide the learning process for improved clustering. SS-DPC is a semi-supervised clustering that can handle data with a small number of labels. First, SS-DPC identifies possible cluster centers based on labeled and unlabeled data automatically. Then, to incorporate partial information, virtual labels are brought in to integrate the partial information with identified centers in a uniform framework. Moreover, labeled data are used to initialize the semi-supervised clustering process to maintain the correctness of prior information in the clustering procedure. Subsequently, the nearest-point-based method is used to detect the labels of non-center unlabeled data. Finally, a step-by-step mergence strategy is introduced to generate more reasonable results. Experiments on eight UCI datasets illustrate that the proposed semi-supervised clustering algorithm yields promising clustering results. © 2023, Statistics and its Interface. All Rights Reserved

On the bootstrap saddlepoint approximations

We compare saddlepoint approximations to the exact distributions of a studentized mean and to its bootstrap approximation. We show that, on bounded sets, these empirical saddlepoint approximations achieve second order relative errors uniformly. We also consider the relative errors for larger deviations. It follows that the studentized-t bootstrap p-value and the coverage of the bootstrap confidence interval have second order relative errors