The transformer is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism, which...

natural language processing by machines. It is based on the transformer deep learning architecture, pre-trained on large data sets of unlabeled text, and able...

Mamba is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University...

masked self-attention". Recurrent neural network seq2seq Transformer (deep learning architecture) Attention Dynamic neural network Niu, Zhaoyang; Zhong...

his pioneering contributions in the field of deep learning, most notably the development of the Transformer neural network, which he co-authored in landmark...

of 1.6 exaFLOPs. Transformer (machine learning model) Convolutional neural network Attention (machine learning) Perceiver Deep learning PyTorch TensorFlow...

Transformer (deep learning architecture), a machine learning architecture Transformer (flying car), a DARPA military project "Electronic transformer"...

a sequence of vectors using self-supervised learning. It uses the encoder-only transformer architecture. BERT dramatically improved the state-of-the-art...

Deep Learning Super Sampling (DLSS) is a suite of real-time deep learning image enhancement and upscaling technologies developed by Nvidia that are available...

"How has DeepSeek improved the Transformer architecture?". Epoch AI. Retrieved 3 February 2025. Metz, Cade (27 January 2025). "What is DeepSeek? And How...

purpose. Most modern deep learning models are based on multi-layered neural networks such as convolutional neural networks and transformers, although they can...

machine learning authored by eight scientists working at Google. The paper introduced a new deep learning architecture known as the transformer, based...

(Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. Like the original Transformer model, T5 models...

present in a photo that a human could easily spot. The transformer deep learning architecture was invented by Google Brain researchers in 2017, and explained...

Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images...

network (also referred to as a residual network or ResNet) is a deep learning architecture in which the layers learn residual functions with reference to...

recently been replaced—in some cases—by newer deep learning architectures such as the transformer. Vanishing gradients and exploding gradients, seen during...

Pre-trained Transformer 1 (GPT-1) was the first of OpenAI's large language models following Google's invention of the transformer architecture in 2017. In...

Li, Changliang; Wong, Derek F.; Chao, Lidia S. (2019-06-04), Learning Deep Transformer Models for Machine Translation, arXiv:1906.01787 Xiong, Ruibin;...

Whisper is a weakly-supervised deep learning acoustic model, made using an encoder-decoder transformer architecture. Whisper Large V2 was released on...

Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the...

transformer-based deep-learning neural network architectures. Previously, the best-performing neural NLP models commonly employed supervised learning...

A neural processing unit (NPU), also known as AI accelerator or deep learning processor, is a class of specialized hardware accelerator or computer system...

autoencoders. Self-supervised learning has since been applied to many modalities through the use of deep neural network architectures such as convolutional neural...

2024). One of the 2 blocks (mLSTM) of the architecture are parallelizable like the Transformer architecture, the other ones (sLSTM) allow state tracking...

day. AlphaChip is an reinforcement learning-based neural architecture that guides the task of chip placement. DeepMind claimed that the time needed to...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

adversarial networks (GAN) and transformers are used for content creation across numerous industries. This is because deep learning models are able to learn...

GPT-4, a generative pre-trained transformer architecture, implementing a deep neural network, specifically a transformer model, which uses attention instead...

ongoing AI spring, and further increasing interest in deep learning. The transformer architecture was first described in 2017 as a method to teach ANNs...