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

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

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...

In deep learning, a multilayer perceptron (MLP) is a name for a modern feedforward neural network consisting of fully connected neurons with nonlinear...

that learns features via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different...

using autoencoders, which are a type of neural network architecture used for representation learning. Autoencoders consist of an encoder network that maps...

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"...

explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical...

regarding local vs non-local learning, as well as shallow vs deep architecture. Biological brains use both shallow and deep circuits as reported by brain...

Kerala, India Mamba Point, zone in Monrovia, Liberia, north of Cape Mesurado Kobe Bryant Mamba (deep learning), a deep learning architecture Mamba (website)...

nanometers. Activation normalization, on the other hand, is specific to deep learning, and includes methods that rescale the activation of hidden neurons...

is widely used in generative AI chatbots. GPTs are based on a deep learning architecture called the transformer. They are pre-trained on large data sets...

Jian, Cheng-Yuan; Yang, Yong-Cheng; Lin, Chun-Liang (2023-02-14). "Deep learning based atomic defect detection framework for two-dimensional materials"...

In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling. It...

self-attention". Recurrent neural network seq2seq Transformer (deep learning architecture) Attention Dynamic neural network Cherry, E. Colin (1953). "Some...

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

Ian; Bengio, Yoshua; Courville, Aaron (2016). Deep learning. Adaptive computation and machine learning. Cambridge, Mass: The MIT press. ISBN 978-0-262-03561-3...

Topological deep learning (TDL) is a research field that extends deep learning to handle complex, non-Euclidean data structures. Traditional deep learning models...

Multi-relational decision tree learning (MRDTL) uses a supervised algorithm that is similar to a decision tree. Deep Feature Synthesis uses simpler methods...

an open-source machine learning library based on the Torch library, used for applications such as computer vision, deep learning research and natural language...

large language models following Google's invention of the transformer architecture in 2017. In June 2018, OpenAI released a paper entitled "Improving Language...

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

In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple...

Realistic artificially generated media Deep learning – Branch of machine learning Diffusion model – Deep learning algorithm Generative artificial intelligence –...

Decade of Deep Learning / Outlook on the 2020s". AI Blog. IDSIA, Switzerland. Retrieved 2022-04-30. Calin, Ovidiu (14 February 2020). Deep Learning Architectures...

training and inference of neural networks. It is one of the most popular deep learning frameworks, alongside others such as PyTorch. It is free and open-source...

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

the field of machine learning. NAS has been used to design networks that are on par with or outperform hand-designed architectures. Methods for NAS can...

previous section described MoE as it was used before the era of deep learning. After deep learning, MoE found applications in running the largest models, as...