A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper "Attention...

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

paper "Attention Is All You Need" which introduced the Transformer model, a novel architecture that uses a self-attention mechanism and has since become...

a {\displaystyle W_{a}} is a learnable weight matrix. Transformer (deep learning architecture) § Efficient implementation Rumelhart, David E.; Mcclelland...

supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, recurrent neural networks,...

used in natural language processing tasks. GPTs are based on the transformer architecture, pre-trained on large data sets of unlabelled text, and able to...

Multimodal learning, in the context of machine learning, is a type of deep learning using a combination of various modalities of data, such as text, audio...

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

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

made available for TensorFlow. Transformer (machine learning model) Attention (machine learning) Perceiver Deep learning PyTorch TensorFlow Dosovitskiy...

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...

An AI accelerator, deep learning processor, or neural processing unit (NPU) is a class of specialized hardware accelerator or computer system designed...

T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI. Introduced in 2019, T5 models are trained on a massive...

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

mechanisms proposed by Bahdanau et al. into a new deep learning architecture known as the transformer. The paper is considered by some to be a founding...

capable, as of March 2024[update], are built with a decoder-only transformer-based architecture. Up to 2020, fine tuning was the only way a model could be adapted...

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

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

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

Things, and pharmaceuticals. Federated learning aims at training a machine learning algorithm, for instance deep neural networks, on multiple local datasets...

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

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

Generative Pre-trained Transformer 4 (GPT-4) is a multimodal large language model created by OpenAI, and the fourth in its series of GPT foundation models...

Bidirectional Encoder Representations from Transformers (BERT) is a language model based on the transformer architecture, notable for its dramatic improvement...

modalities through the use of deep neural network architectures such as CNNs and transformers. Supervised feature learning is learning features from labeled data...

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...

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

successes of deep learning being applied to language modelling by Yoshua Bengio with co-authors. In 2017, modern transformer architectures has been introduced...

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

performance of their model. If deep learning is used, the architecture of the neural network must also be chosen by the machine learning expert. Each of these...