Monte Carlo methods are a sampling-based learning technique in reinforcement learning that learns policies by simulating stochastic processes in the environment. Monte Carlo methods can directly collect samples from interactions with the environment...
Neuro-Symbolic Integration is an artificial intelligence technique that combines the learning capabilities of neural networks with the logical reasoning abilities of symbolic artificial intelligence. It leverages neural networks to handle data-intensive tasks...
Embedding learning is a technique that maps data to a low-dimensional vector space, making similar data points appear close to each other in the vector space. It is widely used in Natural Language Processing (NLP), Computer Vision, etc.
Sparse coding is a data representation method that aims to describe data and extract its key features using a small number of non-zero elements. It is widely used in image processing and machine learning, by constructing an overcomplete dictionary...
Distributed representations are a method for mapping words or objects to high-dimensional vector spaces, where each dimension represents a different feature attribute. Distributed representations can capture the similarity between words...
Graph Neural Networks (GNNs) are deep learning models specifically designed for processing graph-structured data. They learn the representation of each node in a graph through the relationships between nodes and edges, and are widely used in node classification,...
Explainable AI (XAI) refers to the design of intelligent systems that make their decision-making processes transparent and understandable to human users. This means XAI can provide clear explanations of how the input data leads to the final decision...
Deep reinforcement learning is an exciting interdisciplinary field in artificial intelligence that combines the perceptual capabilities of deep learning with the decision-making abilities of reinforcement learning. It involves intelligent agents interacting with the environment...
Self-attention is an advanced attention mechanism that allows models to correlate and weight different parts within a sequence when processing sequential data, thereby capturing long-distance dependencies within the sequence.
Sequence modeling is a modeling method in natural language processing and time series analysis used to process data with sequential dependencies. It can capture the temporal or sequential relationships between data points, such as in text...
Collaborative filtering is a recommender system technique that predicts items a user might like by analyzing the similarity between users or user ratings of items. This technique can be based on user (...
A knowledge graph is a structured semantic knowledge base that graphically represents the relationships between entities. It consists of nodes (representing entities) and edges (representing the relationships between entities), and can store and process...
Embedding vectors are a technique for mapping data (such as text, images, and user information) to numerical vectors in a high-dimensional space. They can capture the similarities and relationships between data points, allowing similar data points to be mapped...
Adversarial training is a machine learning technique designed to improve a model's robustness against malicious attacks. By introducing small perturbations that may cause misclassification during training, the model learns to recognize...
Weakly supervised learning is a machine learning paradigm designed to address the problem of incomplete or inaccurate labeled data. It includes incomplete supervision (partially labeled data), imprecise supervision (data is not fully labeled), and so on.
A recurrent neural network (RNN) is a type of neural network with internal memory. This memory allows the network to process and analyze sequential data by considering information from previous steps.
Embedding representations are the process of mapping entities (such as words, images, or users) to a continuous vector space that captures the entities' intrinsic properties and relationships. In natural language processing...
LSTM, short for Long Short-Term Memory, was proposed by Sepp Hochreiter and Jürgen Schmidhuber in 1997. It is a network technology capable of remembering long-term information and using it for future computations...
A chatbot is a computer program that simulates human conversation, using Natural Language Processing (NLP) technology to understand user input and provide automated responses. It is widely used in customer service, online interaction, and information services, and can...
Diffusion models are a type of deep generative model that simulates the diffusion process of data from an ordered state to a disordered state, and conversely, the reverse diffusion process from a disordered state back to an ordered state.
Knowledge Graph Question Answering (KGQA) is a system that combines knowledge graphs and natural language processing technologies, aiming to retrieve and generate accurate answers from structured knowledge graphs by understanding users' natural language questions.
Vector databases are database systems specifically designed for storing and querying vectors. They are used to represent multi-dimensional data points, such as data used in machine learning and artificial intelligence. In a vector database, data is represented as vectors, which...
Data mining is the process of discovering patterns and other valuable information from large datasets. It utilizes machine learning and statistical analysis techniques to extract useful information from massive amounts of data, helping organizations make more informed decisions...
Retrieval-Augmented Generation (RAG) is a technique that combines Information Retrieval (IR) and Natural Language Generation (NLG). It enhances the generation of natural language by retrieving relevant information from external knowledge bases...