Difference between revisions of "User:Cnapun"
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** No description of how crossover and mutate operations work; I referred to [http://davidmontana.net/papers/hybrid.pdf "Neural Network Weight Selection Using Genetic Algorithms"] for a more complete explanation | ** No description of how crossover and mutate operations work; I referred to [http://davidmontana.net/papers/hybrid.pdf "Neural Network Weight Selection Using Genetic Algorithms"] for a more complete explanation | ||
=== Hybrid Methods === | === Hybrid Methods === | ||
| − | + | * [https://openreview.net/pdf?id=ByD6xlrFe "Hybrid Neural Networks Over Time Series For Trend Forecasting"] | |
| − | + | * [http://www.sciencedirect.com/science/article/pii/S0925231201007020 "Time series forecasting using a hybrid ARIMA and neural network model"] | |
| − | + | * [http://ieeexplore.ieee.org/document/5433249/ "Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting"] | |
| − | + | * [http://ieeexplore.ieee.org/document/5340640/ "Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks"] | |
=== Review Papers === | === Review Papers === | ||
| − | + | * [http://ieeexplore.ieee.org/document/7581373/ "Short-Term Load Forecasting Methods: A Review"] | |
| − | + | * [https://arxiv.org/abs/1705.04378 "An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting"], which gives a good, current overview of methods | |
=== LSTMs, Training, and Possible Improvements === | === LSTMs, Training, and Possible Improvements === | ||
| − | + | * [https://arxiv.org/abs/1409.2329 "Recurrent Neural Network Regularization"] | |
| − | + | * [https://arxiv.org/abs/1512.05287 "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks"] | |
| − | + | * [https://arxiv.org/abs/1609.07959 "Multiplicative LSTM for sequence modelling"] | |
| − | + | * [https://arxiv.org/abs/1610.09513 "Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences"] | |
| − | + | * [https://arxiv.org/abs/1511.01432 "Semi-supervised Sequence Learning"] | |
Revision as of 13:03, 13 June 2017
Contents
Personal Information
- Case Western Reserve University Class of 2019
- Applied Mathematics and Computer Science Major
Weekly Log
Week 0 (30 May - 2 Jun)
- Met Dr. Povinelli 4 times: to discuss possible project topics, to get oriented with the lab, to decide on a project topic, and to establish weekly milestones
- Obtained MU and MSCS account logins and ID card
- Read most of "Learning Deep Architectures for AI"
- Read section on Sequence Modeling (Ch 10) of The Deep Learning book
- Read various other papers
Week 1 (5 Jun - 9 Jun)
- Attended GasDay camp and learned about what GasDay does
- Attended responsible conduct of research training
- Continued to read papers
- Decided to use TensorFlow and Keras for now
- Got Anaconda, TensorFlow, and Keras installed on a couple lab computers
- Learned how to access customer data
Week 2 (12 Jun - 16 Jun)
- Started playing with TensorFlow, mostly using the GEFCom2014-E dataset to allow me to continue working when not at the lab
- Got single layer encoder-decoder architecture to compile in TensorFlow
Reading
Here are some of the papers I have read, skimmed, and partially read:
Forecasting with Deep Learning
- "Building Energy Load Forecasting using Deep Neural Networks"
- Discusses the direct application of LSTMs to load forecasting
- "Training Recurrent Networks by Evolino"
- Describes the use of genetic algorithms to train RNNs
- Should offer better performance than Echo State Networks (ESNs)
- No description of how crossover and mutate operations work; I referred to "Neural Network Weight Selection Using Genetic Algorithms" for a more complete explanation
Hybrid Methods
- "Hybrid Neural Networks Over Time Series For Trend Forecasting"
- "Time series forecasting using a hybrid ARIMA and neural network model"
- "Intelligent Hybrid Wavelet Models for Short-Term Load Forecasting"
- "Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks"
Review Papers
- "Short-Term Load Forecasting Methods: A Review"
- "An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting", which gives a good, current overview of methods
