Chang Xu

Reinforcement Learning for Learning Rate Control

2017 · arXiv (Cornell University)

Stochastic gradient descent (SGD), which updates the model parameters by adding a local gradient times a learning rate at each step, is widely used in model training of machine learning algorithms such as neural networks. …

Online Reputation Fraud Campaign Detection in User Ratings

2017

Reputation fraud campaigns (RFCs) distort the reputations of rated items, by generating fake ratings through multiple spammers. One effective way of detecting RFCs is to characterize their collective behaviors based on rating histories.However, these campaigns …

Cross-Target Stance Classification with Self-Attention Networks

2018 · arXiv (Cornell University)

In stance classification, the target on which the stance is made defines the boundary of the task, and a classifier is usually trained for prediction on the same target. In this work, we explore the …

Enhancing the Robustness of Neural Collaborative Filtering Systems Under Malicious Attacks

2018 · IEEE Transactions on Multimedia

Recommendation systems have become ubiquitous in online shopping in recent decades due to their power in reducing excessive choices of customers and industries. Recent collaborative filtering methods based on the deep neural network are studied …

A Semi-Supervised Assessor of Neural Architectures

2020 · arXiv (Cornell University)

Neural architecture search (NAS) aims to automatically design deep neural networks of satisfactory performance. Wherein, architecture performance predictor is critical to efficiently value an intermediate neural architecture. But for the training of this predictor, a …

Universal Adder Neural Networks

2021 · arXiv (Cornell University)

Compared with cheap addition operation, multiplication operation is of much higher computation complexity. The widely-used convolutions in deep neural networks are exactly cross-correlation to measure the similarity between input feature and convolution filters, which involves …

PTN: A Poisson Transfer Network for Semi-supervised Few-shot Learning

2020 · arXiv (Cornell University)

The predicament in semi-supervised few-shot learning (SSFSL) is to maximize the value of the extra unlabeled data to boost the few-shot learner. In this paper, we propose a Poisson Transfer Network (PTN) to mine the …

A Wind Turbine Fault Classification Model Using Broad Learning System Optimized by Improved Pelican Optimization Algorithm

2022 · Machines

As a classification model, a broad learning system is widely used in wind turbine fault diagnosis. However, the setting of hyperparameters for the models directly affects the classification accuracy of the models and it generally …