Showing 181-240 of 314 articles
Meta-Learning
See also: Machine learning terms, Few-shot learning, Transfer learning Meta-learning, often called "learning to learn", is a branch of machine learning in...
Machine Learning
MindSpore
MindSpore is an open-source deep learning framework developed by Huawei that Huawei describes as "a new open source deep learning training/inference framework...
Chinese AIDeveloper Tools
Mixed-Precision Training
Mixed-precision training is a technique for training deep learning models using lower-precision floating-point formats for most computations while maintaining...
Machine Learning
Mixture of Block Attention (MoBA)
Mixture of Block Attention (MoBA) is a trainable block-sparse attention mechanism introduced in February 2025 by researchers at Moonshot AI, the company that...
Neural Networks
Mixture of Depths
Mixture of Depths (MoD) is a technique for dynamically allocating computation to individual tokens within transformer-based language models. Introduced by...
Machine LearningModel Architecture
Mixture of Experts (MoE)
A Mixture of Experts (MoE) is a machine learning architecture that divides a problem into subtasks, each handled by a specialized sub-network called an...
Machine LearningNeural Networks
Mixture-of-Recursions (MoR)
Mixture-of-Recursions (MoR) is a Transformer architecture, introduced in 2025, that unifies two previously separate strategies for building efficient language...
Neural Networks
MobileNet
MobileNet is a family of efficient convolutional neural network (CNN) architectures developed by Google for mobile and edge AI applications. First introduced...
AI HardwareComputer Vision
Modality
A modality in machine learning and artificial intelligence is a distinct type, form, or structure of data that a model can process, learn from, or generate....
Machine Learning
Model Parallelism
See also: Machine learning terms, Data parallelism, GPU computing, Deep learning Model parallelism is a distributed training and inference technique that...
AI InfrastructureMachine Learning
Model merging
Model merging combines the parameters of multiple trained neural networks into a single unified model without any additional training. Rather than running...
Large Language ModelsMachine Learning
Multi-Head Self-Attention
Multi-head self-attention is the core sequence-mixing mechanism of the Transformer architecture: it runs several scaled dot-product attention operations...
Machine LearningModel Architecture
Multi-head Latent Attention
Multi-head Latent Attention (MLA) is an attention mechanism for transformer models that achieves a 93.3% reduction in key-value cache size while maintaining or...
Machine LearningModel Architecture
Multimodal Model
A multimodal model is an artificial intelligence system that processes, relates, and generates information across two or more types of data, or modalities,...
Computer VisionMachine Learning
Muon (optimizer)
Muon (short for MomentUm Orthogonalized by Newton-Schulz) is a neural-network optimizer that updates the two-dimensional weight matrices of hidden layers by...
Training & Optimization
NVIDIA Blackwell
NVIDIA Blackwell is the data center and consumer GPU architecture NVIDIA unveiled on March 18, 2024 to power trillion-parameter AI, built from two...
AI HardwareNVIDIA
NVIDIA Triton Inference Server
NVIDIA Triton Inference Server is open-source model deployment software that lets teams run trained models from any machine learning or deep learning framework...
AI InferenceDeveloper Tools
NaN Trap
See also: gradient descent, backpropagation, loss function, mixed precision training, numerical stability A NaN trap (short for "Not a Number" trap) is a...
Machine LearningTraining & Optimization
Native Sparse Attention (NSA)
Native Sparse Attention (NSA) is a hardware-aligned, natively trainable sparse attention mechanism introduced in February 2025 by DeepSeek, in collaboration...
Neural Networks
NeRF
Neural Radiance Fields (NeRF) is a method for synthesizing photorealistic novel views of a 3D scene by encoding the scene as a continuous 5D function (3D...
Computer VisionNeural Networks
Neural Network
A neural network (also called an artificial neural network or ANN) is a computational model, loosely inspired by the networks of biological neurons in animal...
Machine LearningNeural Networks
Neural architecture search
Neural architecture search (NAS) is a technique for automating the design of neural network architectures. Rather than relying on human experts to hand-craft...
MLOpsMachine Learning
Nick Frosst
Nick Frosst is a Canadian artificial intelligence researcher, entrepreneur, and musician who co-founded the enterprise AI company Cohere in 2019 with Aidan...
AI CompaniesPeople
Node (TensorFlow graph)
See also: Machine learning terms, TensorFlow, Computational graph, Tensor A node in a TensorFlow graph is a single unit of computation, such as a matrix...
Developer Tools
OCR Models
OCR Models are artificial intelligence (AI) systems that convert images of typed, handwritten, or printed text into machine-readable digital text through...
Artificial IntelligenceComputer Vision
Object detection
Object detection is a computer vision task that locates and classifies every object instance in an image or video frame, returning for each one a bounding box,...
Artificial IntelligenceComputer Vision
One-Shot Learning
One-shot learning is a machine learning approach in which a model learns to recognize or classify new categories from only a single labeled example per class....
Machine Learning
Optimizer
An optimizer in machine learning is an algorithm that iteratively adjusts a model's learnable parameters to minimize (or maximize) an objective function,...
Machine LearningTraining & Optimization
Output Layer
See also: neural network, activation function, loss function, hidden layer, softmax, backpropagation The output layer is the final layer of a neural network:...
Machine LearningNeural Networks
Overfitting
Overfitting is when a machine learning model fits its training set so closely that it learns the noise and quirks of those specific examples instead of the...
Machine LearningModel Evaluation
PEER (Parameter Efficient Expert Retrieval / Mixture of a Million Experts)
PEER, short for Parameter Efficient Expert Retrieval, is a neural network layer for Transformer models that replaces the dense feed-forward block with a sparse...
Neural Networks
PaddlePaddle
PaddlePaddle (Chinese name Feijiang, 飞桨) is an open-source deep learning framework developed by the Chinese technology company Baidu, and it is generally...
Chinese AIOpen Source AI
Physical Intelligence
Physical Intelligence (also known as Pi or π) is an American artificial intelligence robotics company that builds general-purpose foundation models and...
AI CompaniesEmbodied AI
Pose estimation
Pose estimation is the computer vision task of detecting and localizing the keypoints (also called landmarks or joints) of a human body, hand, face, animal, or...
Computer VisionMachine Learning
Position Interpolation (PI)
Position Interpolation (PI) is a method for extending the context window of a pretrained large language model that uses rotary position embedding (RoPE)....
Neural Networks
Positional encoding
Positional encoding is a technique used to inject information about token order into transformer models. Because transformers process all tokens in a sequence...
Natural Language ProcessingTransformer Models
Post-training
Post-training is the stage of large language model (LLM) development that comes after pre-training and turns a raw, general-purpose base model into an aligned,...
Artificial IntelligenceLarge Language Models
Pre-Trained Model
A pre-trained model is a machine learning model that has already been trained on a large, general-purpose dataset and can then be reused, either as a fixed...
Machine LearningNatural Language Processing
Pre-training
Pre-training is the first and most compute-intensive stage of building a modern AI model: a neural network is trained on a massive, mostly unlabeled dataset...
Artificial IntelligenceComputer Vision
Prompt-to-Prompt
Prompt-to-Prompt is a training-free image editing technique for text-conditioned diffusion models that edits a generated image by manipulating the model's...
Generative AI
PyTorch
PyTorch is an open-source machine learning framework, primarily developed by Meta AI and now governed by the PyTorch Foundation under the Linux Foundation,...
Developer ToolsMachine Learning
QLoRA
QLoRA (Quantized Low-Rank Adaptation) is a parameter-efficient fine-tuning method that finetunes a 65-billion-parameter large language model on a single 48 GB...
AI InferenceLarge Language Models
Quantization
Quantization in machine learning and artificial intelligence is the process of reducing the numerical precision of a neural network's parameters (weights,...
AI InferenceMachine Learning
R-CNN (Regions with CNN features)
R-CNN (short for Regions with CNN features) is a two-stage object detection method that generates about 2,000 candidate region proposals per image with...
Computer Vision
RMSProp
RMSProp (Root Mean Square Propagation) is an adaptive learning-rate optimizer that divides each parameter's gradient by a running root-mean-square of that...
Training & Optimization
RNN
See also: Machine learning terms RNN is the standard abbreviation for recurrent neural network, a class of artificial neural network in which connections...
Neural Networks
RWKV
RWKV (pronounced "RwaKuv") is an open-source neural network architecture that combines the parallelizable training of Transformers with the constant-time,...
Machine LearningModel Architecture
ReLU
See also: Machine learning terms ReLU (Rectified Linear Unit) is an activation function used in neural networks, defined by the formula : it passes positive...
Machine LearningNeural Networks
Rectified Linear Unit (ReLU)
See also: Activation function, Deep learning, Neural network The Rectified Linear Unit (ReLU) is the most widely used activation function in deep learning,...
Machine LearningNeural Networks
Recurrent Neural Network
See also: Machine learning terms A recurrent neural network (RNN) is a class of artificial neural network designed to process sequential data by maintaining an...
Machine LearningModel Architecture
Register tokens (Vision Transformers Need Registers)
Register tokens are a small set of extra learnable tokens added to the input sequence of a Vision Transformer (ViT) so the network has a dedicated place to...
Neural Networks
Regularization
Regularization is a set of techniques used in machine learning to prevent overfitting by adding constraints or penalties during training that discourage a...
Machine LearningTraining & Optimization
Reinforcement Learning from Human Feedback (RLHF)
Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that trains artificial intelligence systems to behave according to human...
AI AlignmentMachine Learning
Reinforcement learning
Reinforcement learning (RL) is a branch of machine learning in which an agent learns to make decisions by taking actions in an environment to maximize a...
Artificial IntelligenceMachine Learning
Replay Buffer
A replay buffer (also called an experience replay buffer or replay memory) is a fixed-size memory that stores an off-policy reinforcement learning agent's past...
Machine LearningReinforcement Learning
Representation
A representation in machine learning is the format in which a data example is encoded before a model processes it: the same image can be represented as raw...
Machine Learning
ResNet
ResNet (Residual Network) is a deep convolutional neural network architecture introduced by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, then at...
Computer VisionNeural Networks
Residual connection
A residual connection (also called a skip connection or shortcut connection) is a structural element in neural networks that adds the input of a layer or block...
Machine Learning
RoBERTa
RoBERTa (Robustly Optimized BERT Pretraining Approach) is an open-source natural language processing model released in July 2019 by researchers at Facebook AI...
Machine LearningNatural Language Processing
Robot learning
Robot learning is a field at the intersection of robotics and machine learning in which robots acquire new skills, adapt to new environments, and improve their...
Machine LearningReinforcement Learning