Search Results for author:
Found 9 papers, 6 papers with code
Generative Pretraining at Scale: Transformer-Based Encoding of Transactional Behavior for Fraud Detection
In this work, we introduce an innovative autoregressive model leveraging Generative Pretrained Transformer (GPT) architectures, tailored for fraud detection in payment systems.
Realization Scheme for Visual Cryptography with Computer-generated Holograms
This method can expand the application range of visual cryptography and further increase the security of visual cryptography.
CROLoss: Towards a Customizable Loss for Retrieval Models in Recommender Systems
In this paper, we proposed the Customizable Recall@N Optimization Loss (CROLoss), a loss function that can directly optimize the Recall@N metrics and is customizable for different choices of N. This proposed CROLoss formulation defines a more generalized loss function space, covering most of the conventional loss functions as special cases.
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search
Neural architecture search (NAS) has shown encouraging results in automating the architecture design.
Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation
Semi-supervised domain adaptation (SSDA), which aims to learn models in a partially labeled target domain with the assistance of the fully labeled source domain, attracts increasing attention in recent years.
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts
In particular, we propose a novel joint-training framework to train plain CNN by leveraging the gradients of the ResNet counterpart.
AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction
By implementing a regularized optimizer over the architecture parameters, the model can automatically identify and remove the redundant feature interactions during the training process of the model.
Ranked #29 on
Click-Through Rate Prediction
on Criteo
Hierarchical Neural Architecture Search via Operator Clustering
Recently, the efficiency of automatic neural architecture design has been significantly improved by gradient-based search methods such as DARTS.
Revisiting Knowledge Distillation via Label Smoothing Regularization
Without any extra computation cost, Tf-KD achieves up to 0. 65\% improvement on ImageNet over well-established baseline models, which is superior to label smoothing regularization.
