Found 40 repositories(showing 30)
shashwattrivedi
No description available
whyisshizai
A Hybrid LSTM-Transformer Network for Local and Global Planning
abnewaz
This is an attempt for the unofficial code for the paper titled "Hybrid LSTM-Transformer Model for Emotion Recognition From Speech Audio Files"
Time series forcasting using RNN&LSTM&Transformer
call-me-anna
No description available
Sample codes in area of SimpleRNN, GRU, LSTM, Timeseries, Transformers
No description available
Time Series Forecasting using PyTorch LSTM with synthetic data generation, baseline comparison (ARIMA & ETS), and model explainability using SHAP.
This project includes two Transformer models and one LSTM model to predict the pandemic data of COVID-19.
anithasakaray24-crypto
Developed a Multi-modal speech emotion recognition (SER) system integrating speech, facial expression, and EEG data using CNN, LSTM, and Transformer architectures.
kavi0215
Developed an advanced time series forecasting model using LSTM and Transformer architectures to predict stock prices accurately. Integrated explainability techniques like SHAP to interpret model predictions and understand feature contributions.
Liming547
No description available
anamaria-rusu
No description available
Ravindra3609
No description available
Rushikeshiname
No description available
No description available
arshiyaakishore
No description available
I generated a random walk using Euler-Maruyama integration (from Stochastic Processes for Physicists by Kurt Jacobs) and created LSTM and Masked Transformer models with the same window size/Sequence length. Their performance is compared to a linear regression baseline and a drift-based baseline.
No description available
No description available
No description available
No description available
Manimaranarockiyadoss
No description available
No description available
No description available
No description available
rajiveerasamybt-ops
No description available
vigneshdilli1998-byte
No description available
archanavisu02-pixel
No description available
“This project builds an advanced multivariate time series forecasting system using LSTM and Transformer models. It predicts future demand from historical features and includes preprocessing, model training, evaluation, and SHAP-based explainability for feature importance.”