In deep learning, transformer is an architecture based on the multi-head attention mechanism, in which text is converted to numerical representations...

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

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

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...

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

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

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

(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...

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...

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...

Deep reinforcement learning (DRL) is a subfield of machine learning that combines principles of reinforcement learning (RL) and deep learning. It involves...

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...

deep recurrent neural networks. These early NMT systems used LSTM-based encoder-decoder architectures, as they preceded the invention of transformers...

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...

gradually expanded to support 35 languages. Its algorithm uses the transformer architecture. It offers a paid subscription for additional features and access...

Jingbo; Li, Changliang; Wong, Derek F.; Chao, Lidia S. (2019). "Learning Deep Transformer Models for Machine Translation". arXiv:1906.01787 [cs.CL]. Xiong...

source-available DeepSeek License. The architecture was essentially the same as the Llama series. They used the pre-norm decoder-only Transformer with RMSNorm...

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

Intel. May 20, 2024. "AMD XDNA Architecture". "Deploying Transformers on the Apple Neural Engine". Apple Machine Learning Research. Retrieved August 24...

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

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

to the field of artificial intelligence and deep learning, particularly in the development of transformer models and natural language processing. Noam...

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

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

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

Q-learning algorithm. In 2014, Google DeepMind patented an application of Q-learning to deep learning, titled "deep reinforcement learning" or "deep Q-learning"...

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...