Unified Model
Definition
A unified model refers to an artificial intelligence system capable of performing various …
Efficiency
Robotic process automation
Definition
Robotic Process Automation (RPA) employs software robots, often enhanced with AI, to …
Qwen3 5 Moe
Definition
This term refers to a specialized architecture within the Qwen family, likely leveraging …
Quantized
Definition
Quantization is a model optimization technique that reduces the numerical precision of a …
Prompt Tuning
Definition
Prompt tuning involves adding trainable soft prompts (continuous vectors) to the input …
Pruning
Definition
Pruning involves identifying and eliminating neurons, connections, or filters in a neural …
Proactive learning
Definition
In proactive learning, the AI system determines which samples would most reduce …
Prefix Tuning
Definition
Prefix Tuning is a parameter-efficient adaptation technique for pre-trained transformers. …
Phi
Definition
Phi, short for ‘Foundation models based on Teaching-Learning Paradigm’, is a …
P-Tuning
Definition
P-Tuning (Prompt Tuning) is a technique designed to adapt large pre-trained language …
Multitask optimization
Definition
Multitask optimization involves training a single model to handle several distinct but …
Multi-task Learning
Definition
This technique leverages the inductive bias shared among related tasks to enhance …
Mixture of Experts
Definition
Mixture of Experts (MoE) is a machine learning architecture designed to improve …
Model Compression
Definition
This category includes methods like pruning, quantization, and knowledge distillation …
Mixtral
Definition
Mixtral is a pioneering open-weight LLM that utilizes a Sparse Mixture of Experts (MoE) …
DeepSeek V3
Definition
DeepSeek V3 is an advanced iteration in the DeepSeek model family, characterized by its …
Business process automation
Definition
Business Process Automation (BPA) involves leveraging software and AI to streamline …
Batch Processing
Definition
Batch processing involves aggregating data inputs into a group, or batch, before …
Automated decision-making
Definition
Automated decision-making (ADM) relies on software systems to make choices that …
Automated machine learning
Definition
AutoML (Automated Machine Learning) streamlines the development of ML models by …
Active learning
Definition
Active learning reduces the amount of labeled data required by allowing the model to …
Zero-shot Learning
Definition
Zero-shot learning enables a machine learning model to classify instances of classes that …
QLoRA
Definition
QLoRA combines Low-Rank Adaptation (LoRA) with 4-bit quantization to significantly reduce …
Few-shot Learning
Definition
Few-shot learning aims to enable models to generalize from just a handful of examples, …
Adapter
Definition
Adapters are a parameter-efficient fine-tuning technique used primarily in large language …
training-free
Definition
Training-free approaches refer to techniques that modify model behavior or output without …
pre-trained
Definition
A pre-trained model is a foundational AI model that has undergone extensive training on …
low-cost
Definition
Low-cost AI focuses on efficiency, aiming to reduce the barriers to entry and operational …
Transfer Learning
Definition
Transfer learning leverages pre-trained models to improve performance and reduce training …
Mamba
Definition
Mamba represents a significant advancement in sequence modeling by introducing a …
LoRA
Definition
LoRA freezes pre-trained model weights and inserts trainable decomposition matrices into …
Direct
Definition
In AI contexts, ‘direct’ often describes architectures or inference paths …
Automated
Definition
Automation in AI involves using algorithms and systems to perform tasks that …