framework
active
framework:supervised-learning

Supervised Learning

Learning through physical changes in mechanical networks, as an example of learning outside neural systems.

Neighborhood — ranked by edge-count

Papers (1)

paper

Methods (2)

method

Concepts (1)

concept
  • Loss Function
    implements
    In machine learning, a function measuring the distance between current and desired output; analogous to stress.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

  • Supervised Fine-Tuningconcept0.831
    First post-training stage; shown to suppress only Impolite persona while boosting others
  • Unsupervised Learningmethod0.819
    Learning that builds a low-dimensional model of input data without error signals or rewards; Hebbian learning is an example.
  • Learningconcept0.809
    Inference of parameters encoding contingencies of the world (e.g., likelihood matrix A) at slower timescale than perception.
  • Supervised Learning Constitutional AIframework0.797
    The supervised learning stage of CAI where a model critiques and revises its responses, then finetunes on revisions.
  • Associative Learningconcept0.796
    Sentience criterion; capacity occurs even in gene regulatory networks and non-neural morphogenetic agents.
  • Perceptual Learningconcept0.790
    Process of inferring causes of sensory information; unified with value learning as integral aspects of free energy minimization.
  • Reinforcement Learningframework0.786
    Alternative framework for agent behavior; based on reward maximization rather than free energy minimization.
  • Multi-Task Learningconcept0.785
    Learning paradigm that jointly learns multiple related tasks using a single model