Graph Neural Network (GNN)
Graph Neural Network (GNN) is a model for representation learning on graph-structured data. Its core lies in preserving the topological relationships between nodes, edges, and neighborhoods, and through message passing on graphs, node representations are updated from local neighborhoods to form representation vectors reflecting overall structure. Such models can handle both node-level and graph-level prediction problems. A graph can be represented as G=(V,E), where V is the set of nodes and E is the set of edges, with features on both nodes and edges.
The Long-Tail Problem
The Long-Tail Problem is not merely a data imbalance issue, but a theoretical phenomenon concerning the statistical structure of the real world. Events in the world are not uniformly distributed but consist of a few high-frequency events and numerous low-frequency events. A minority of events concentrate most of the probability mass, while many events scatter sparsely in the tail region. This distribution means that the part easiest for models to observe is not necessarily the most important part of the world. Rather, much real complexity often exists in the low-frequency tail. The long-tail problem is thus not just an engineering challenge in AI, but a fundamental theoretical concern.
Ontology in Knowledge Representation for Artificial Intelligence
Ontology in Artificial Intelligence is a core structure in Knowledge Representation, used to formally describe concepts, entities, and their relationships, enabling systems to perform semantic reasoning. This concept originates from philosophical ontology but transforms into a computable semantic model in computational contexts. This article explains ontology's formal definition in AI starting from philosophical context.
Knowledge Graph (KG)
Knowledge Graph is a representation method that uses graph structure to carry knowledge, where nodes correspond to entities and edges correspond to relationships. Under semantic structures provided by Ontology, knowledge graphs can be represented and reasoned about. In formal semantic contexts, knowledge graphs can be compatible with standardized data models like RDF; in broader research, they can be combined with ontologies, rules, and embedding representations.
Neural Signal Processing and Artificial Neural Models
In 'A Logical Calculus of the Ideas Immanent in Nervous Activity,' McCulloch and Pitts (1943) proposed that neural activity follows the 'all-or-none law': neurons either fire (action potential) or do not fire at any given moment, with no intermediate states. This characteristic allows neurons to be viewed as binary units.
The Significance of Latent Space in Reinforcement Learning and Agents
Latent space in reinforcement learning and agents serves as a core mechanism to transform high-dimensional observations into internal representations sufficient for decision-making. Through representation learning, agents can map observations to latent states to support policy learning, future prediction, and action selection. In model-based reinforcement learning, latent space representations bridge the gap between raw perception and decision-making.
Heuristics
Heuristics is a strategy that uses approximate evaluation to guide problem-solving. Its primary goal is to obtain sufficiently good solutions under limited time and information conditions, rather than guaranteeing global optimality. This method reduces search space through heuristic functions and knowledge of problem structure, and is widely used in artificial intelligence for decision-making and planning.
Bayesian Filtering
Bayesian filtering is a probabilistic framework for dynamic inference in partially observable environments. Its core goal is to maintain probabilistic estimates of hidden states through continuous observations over time when states cannot be directly observed. Unlike general Bayesian inference which handles parameter updates with fixed data, Bayesian filtering addresses hidden state estimation that evolves over time.
Bayesian Inference
Bayesian inference is a reasoning framework that uses probability distributions to express knowledge states and uncertainty. Its core idea is not to directly seek a single fixed answer, but to continuously update beliefs about parameters, states, or hypotheses based on observed data, presenting reasoning results as posterior distributions. Unlike frequentist approaches that view parameters as fixed, Bayesian inference treats them as uncertain quantities.
Representation and Geometric Properties of Latent Space
Latent space is the hidden variable space used in machine learning to represent the intrinsic structure of data. Its core function lies in mapping high-dimensional observational data to latent representations with semantic and statistical significance. This representation is typically based on the manifold hypothesis, making data exhibit local continuity and semantic consistency in latent space. In concrete models, Variational Autoencoders provide explicit probabilistic structure to latent space through probabilistic modeling.