스킬 검색.../

Cerebrum | Skills Pool

스킬 파일

Cerebrum

Proficient in developing and applying cognitive modeling frameworks that integrate linguistic case systems and Bayesian representations for enhanced intelligence production.

docxology0 스타2026. 4. 1.

직업
카테고리: 머신러닝

스킬 내용

CEREBRUM: Case-Enabled Reasoning Engine with Bayesian Representations for Unified Modeling

Daniel Ari Friedman (2025) · Active Inference

Instructions

Use this skill when working with topics related to CEREBRUM, linguistic case systems, Active Inference, Bayesian modeling.

When applying this skill:

Apply linguistic case systems as computational frameworks for model management
Design model lifecycle management using CEREBRUM's case-based approach
Formalize inter-model communication using linguistic case grammar

Key Concepts

CEREBRUM
linguistic case systems
Active Inference
Bayesian modeling
model management
case grammar

관련 스킬

빠른 설치

Cerebrum

npx skills add docxology/docxology

Skill 다운로드 저장소 열기

작성자: docxology
스타: 0
업데이트: 2026. 4. 1.
직업

이 페이지의 내용

01CEREBRUM: Case-Enabled Reasoning Engine with Bayesian Representations for Unified Modeling

unified modeling

Methods & Techniques

Application of category theory to cognitive modeling.
Utilization of the Free Energy Principle in model descriptions.
Development of a formal framework for cognitive models as case-bearing entities.
Implementation of hierarchical message passing for dynamic adaptive processes.

Key Findings

CEREBRUM enables structured representation of model relationships and transformations.
Cognitive models can exist in different 'cases' based on their functional roles.
The framework enhances adaptability and coordination among analytical models.
Emergence of novel case functions for improved cognitive ecosystem management.

Prerequisites

Probability theory and Bayesian inference basics
Free Energy Principle and generative models

🎯 Consulting & Tutoring

Daniel Ari Friedman, PhD is available for AI Research Consulting and Tutoring related to this skill.

Related Skills

See BIBLIOGRAPHY.md for the complete publication catalog and related papers.

04Methods & Techniques

06Prerequisites

07🎯 Consulting & Tutoring

08Related Skills

데이터 과학자

Continuous Learning V2

Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.

Continuous Learning V2

Hook'lar aracılığıyla oturumları gözlemleyen, güven skorlaması ile atomik instinct'ler oluşturan ve bunları skill/command/agent'lara evriltiren instinct tabanlı öğrenme sistemi. v2.1 çapraz proje kontaminasyonunu önlemek için proje kapsamlı instinct'ler ekler.

Continuous Learning V2

훅을 통해 세션을 관찰하고, 신뢰도 점수가 있는 원자적 본능을 생성하며, 이를 스킬/명령어/에이전트로 진화시키는 본능 기반 학습 시스템. v2.1에서는 프로젝트 간 오염을 방지하기 위한 프로젝트 범위 본능이 추가되었습니다.

Continuous Learning

Claude Codeセッションから再利用可能なパターンを自動的に抽出し、将来の使用のために学習済みスキルとして保存します。

Continuous Learning

Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.

Pytorch Patterns

PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.

데이터 과학자