37 research outputs found
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
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
Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions
Color is an important feature of vehicles, and it plays a key role in intelligent traffic management and criminal investigation. Existing algorithms for vehicle color recognition are typically trained on data under good weather conditions and have poor robustness for outdoor visual tasks. Fine vehicle color recognition under rainy conditions is still a challenging problem. In this paper, an algorithm for jointly deraining and recognizing vehicle color, (JADAR), is proposed, where three layers of UNet are embedded into RetinaNet-50 to obtain joint semantic fusion information. More precisely, the UNet subnet is used for deraining, and the feature maps of the recovered clean image and the extracted feature maps of the input image are cascaded into the Feature Pyramid Net (FPN) module to achieve joint semantic learning. The joint feature maps are then fed into the class and box subnets to classify and locate objects. The RainVehicleColor-24 dataset is used to train the JADAR for vehicle color recognition under rainy conditions, and extensive experiments are conducted. Since the deraining and detecting modules share the feature extraction layers, our algorithm maintains the test time of RetinaNet-50 while improving its robustness. Testing on self-built and public real datasets, the mean average precision (mAP) of vehicle color recognition reaches 72.07%, which beats both sate-of-the-art algorithms for vehicle color recognition and popular target detection algorithms
Saddlepoint approximations to the trimmed mean
Saddlepoint approximations for the trimmed mean and the studentized trimmed mean are established. Some numerical evidence on the quality of our saddlepoint approximations is also included
Joint Semantic Intelligent Detection of Vehicle Color under Rainy Conditions
Color is an important feature of vehicles, and it plays a key role in intelligent traffic management and criminal investigation. Existing algorithms for vehicle color recognition are typically trained on data under good weather conditions and have poor robustness for outdoor visual tasks. Fine vehicle color recognition under rainy conditions is still a challenging problem. In this paper, an algorithm for jointly deraining and recognizing vehicle color, (JADAR), is proposed, where three layers of UNet are embedded into RetinaNet-50 to obtain joint semantic fusion information. More precisely, the UNet subnet is used for deraining, and the feature maps of the recovered clean image and the extracted feature maps of the input image are cascaded into the Feature Pyramid Net (FPN) module to achieve joint semantic learning. The joint feature maps are then fed into the class and box subnets to classify and locate objects. The RainVehicleColor-24 dataset is used to train the JADAR for vehicle color recognition under rainy conditions, and extensive experiments are conducted. Since the deraining and detecting modules share the feature extraction layers, our algorithm maintains the test time of RetinaNet-50 while improving its robustness. Testing on self-built and public real datasets, the mean average precision (mAP) of vehicle color recognition reaches 72.07%, which beats both sate-of-the-art algorithms for vehicle color recognition and popular target detection algorithms