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 ...

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 ...

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 ...

The landscape of vision model pre-training has undergone significant evolution, especially with the rise of Large Language Models (LLMs). Traditionally, vision models operated within fixed, predefined ...

🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com. - HotDog/differences-between-autoregressive-autoencoding-and-sequence-to-sequence-models-in-machine-learning.md at main · ...

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 ...