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GitHub1y
pranayreddy99/Encoder-to-Decoder-Sequence-to-Sequence-Model: A small demo for testing - GitHub
Encoder-Decoder Seq2Seq (Sequence-to-Sequence) is a type of neural network architecture used for machine translation, speech recognition, and other natural language processing tasks. The ...
GitHub5y
lkulowski/LSTM_encoder_decoder: Build a LSTM encoder-decoder using PyTorch to make sequence-to-sequence prediction for time series data - GitHub
To make sequence-to-sequence predictions using a LSTM, we use an encoder-decoder architecture. The LSTM encoder-decoder consists of two LSTMs. The first LSTM, or the encoder, processes an input ...
IEEE2y
Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture - IEEE Xplore
In this paper, we propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time. We employ the ...
Microsoft2y
SeqTrack: Sequence to Sequence Learning for Visual Object Tracking
SeqTrack only adopts a simple encoder-decoder transformer architecture. The encoder extracts visual features with a bidirectional transformer, while the decoder generates a sequence of bounding box ...
marktechpost1y
Deep Learning Architectures From CNN, RNN, GAN, and Transformers To Encoder-Decoder Architectures - MarkTechPost
Encoder-Decoder Architectures. Encoder-decoder architectures are a broad category of models used primarily for tasks that involve transforming input data into output data of a different form or ...
IEEE7y
Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture - IEEE Xplore
Abstract: In this paper, we propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time. We employ the ...