Deep neural network models implemented from scratch in PyTorch for time series forecasting
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Updated
Jun 12, 2024 - Jupyter Notebook
Deep neural network models implemented from scratch in PyTorch for time series forecasting
Hello! We are an international group of students researching deep learning applications for Alzheimer's disease. We aim to introduce a better model for AD diagnosis. Our names are Hugo Jal Hernández, Basit Oliyide, Parth Parikh & Ravi Shah.
Design of target-focused libraries by probing continuous fingerprint space with recurrent neural networks. The repository accompanies a research paper which is currently under review (08.04.24)
Exercises on Machine Learning
IMPSy - the Interactive Musical Prediction SYstem
A machine learning software for extracting information from scholarly documents
A Long Term Training On Artificial Intelligence
Welcome to quote our published papers, and the codes have been uploaded.
This repository contains a reading list of papers on Time Series Forecasting/Prediction (TSF) and Spatio-Temporal Forecasting/Prediction (STF). These papers are mainly categorized according to the type of model.
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
Image Captioning With lSTM And RNN Using Flickr8k Dataset
Official repository of the xLSTM.
A lyrics generator is a fascinating application of Recurrent Neural Networks (RNNs), where the model learns patterns in sequences of text (lyrics) and generates new, coherent sequences.
Recurrent Neural Networks (RNNs) are a class of artificial neural networks designed to recognize patterns in sequences of data, such as time series, speech, or text.
Construction of Long-Term Seismic Catalog with Deep Learning: A Workflow for Localized Self-Attention RNN (LoSAR)
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