Transformers have changed the application of Machine Learning in Natural Language Processing. They have replaced LSTMs as state-of-the-art (SOTA) approaches in the wide variety of language and text ...

There is a new paper by Google and Waymo (Scaling Laws of Motion Forecasting and Planning A Technical Report that confirmed ...

To address these challenges, we propose a hybrid model named Attentions-based LSTM Encoder-Decoder network and Autoregressive model (ALEDAR), which combines neural network and statistical learning ...

Decoder-Only Transformer: Embracing the Autoregressive Nature While the original transformer architecture was designed for sequence-to-sequence tasks like machine translation, many NLP tasks, such as ...

Decoder-only models. In the last few years, large neural networks have achieved impressive results across a wide range of tasks. Models like BERT and T5 are trained with an encoder only or ...

Apple’s latest research hints that a long-forgotten AI technique could have new potential for generating images. Here’s the ...

By chaining the encoder and decoder together into one Machine Learning task, e.g. for translating using German inputs and English outputs, the encoder and decoder's weight matrices jointly learn to ...

Effective workload forecasting can provide a reference for resource scheduling in cloud data centers. Compared with the normal single data center, the multi-data center has a more complicated ...