Search Results for author:
Found 14 papers, 6 papers with code
Mapping-to-Parameter Nonlinear Functional Regression with Novel B-spline Free Knot Placement Algorithm
We propose a novel approach to nonlinear functional regression, called the Mapping-to-Parameter function model, which addresses complex and nonlinear functional regression problems in parameter space by employing any supervised learning technique.
Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels
Motion mode (M-mode) recording is an essential part of echocardiography to measure cardiac dimension and function.
Ranked #1 on
Real-time instance measurement
on MEIS
To transfer or not transfer: Unified transferability metric and analysis
The proposed bound relates the target risk to source model performance, domain and task differences based on Wasserstein distance.
EDU-level Extractive Summarization with Varying Summary Lengths
To fill these gaps, this paper first conducts the comparison analysis of oracle summaries based on EDUs and sentences, which provides evidence from both theoretical and experimental perspectives to justify and quantify that EDUs make summaries with higher automatic evaluation scores than sentences.
Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification
Deep learning-based melanoma classification with dermoscopic images has recently shown great potential in automatic early-stage melanoma diagnosis.
Group-Agent Reinforcement Learning
It can largely benefit the reinforcement learning (RL) process of each agent if multiple geographically distributed agents perform their separate RL tasks cooperatively.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Multiple Dynamic Pricing for Demand Response with Adaptive Clustering-based Customer Segmentation in Smart Grids
In this paper, we propose a realistic multiple dynamic pricing approach to demand response in the retail market.
UPANets: Learning from the Universal Pixel Attention Networks
Recently, from the successful development of multi-head attention in natural language processing, it is sure that now is a time of either using a Transformer-like model or hybrid CNNs with attention.
Ranked #1 on
Image Classification
on Tiny-ImageNet
Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation
Data augmentation methods have been shown to be a fundamental technique to improve generalization in tasks such as image, text and audio classification.
Evaluating data augmentation for financial time series classification
Data augmentation methods in combination with deep neural networks have been used extensively in computer vision on classification tasks, achieving great success; however, their use in time series classification is still at an early stage.
Augmenting transferred representations for stock classification
In this paper we show that using transfer learning can help with this task, by pre-training a model to extract universal features on the full universe of stocks of the S$\&$P500 index and then transferring it to another model to directly learn a trading rule.
A novel dynamic asset allocation system using Feature Saliency Hidden Markov models for smart beta investing
The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with Smart beta strategies emerging as a trend among institutional investors.
An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids
In this paper, we consider a realistic and meaningful scenario in the context of smart grids where an electricity retailer serves three different types of customers, i. e., customers with an optimal home energy management system embedded in their smart meters (C-HEMS), customers with only smart meters (C-SM), and customers without smart meters (C-NONE).
Twitter mood predicts the stock market
A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values.
