Meta-Cognitive Monitoring
Meta-cognitive monitoring involves self-regulation and awareness of one’s cognitive processes during reasoning.
Category: General AI/ML
Backtesting Simulations
The process of testing a trading strategy using historical data to evaluate its effectiveness and profitability.
Fine-Grained Cognitive Evaluation Framework
A structured approach to assess cognitive processes and reasoning behaviors in models and humans.
Hierarchical Nesting
Hierarchical nesting refers to a cognitive strategy where information is organized in a multi-level structure to facilitate reasoning.
Symmetric k-Stochastic Block Model
A probabilistic model for community detection where nodes are partitioned into clusters with specified intra- and inter-cluster connection probabilities.
Sum-of-Squares Framework
A method in optimization and algorithm design that uses polynomial equations to derive stronger relaxations for combinatorial problems.
Linear Gradient Matching
Linear Gradient Matching is a method that optimizes synthetic images to induce gradients in a linear classifier similar to those produced by real data when processed through a pre-trained feature extractor.
Kesten–Stigum Threshold
The Kesten–Stigum threshold is a critical point in the study of community detection where the probability of correctly identifying communities transitions from zero to positive.
Minimax Optimality
Minimax optimality refers to a strategy that minimizes the maximum possible loss, often used in statistical decision theory.
Supervised Fine-Tuning
A process of further training a pre-trained model on a labeled dataset to improve performance on specific tasks.