Popular GitHub Repositories for Alexander Suvorov Paradigms

Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.

Control GenAI interactions with power, precision, and consistency using LLM-native Conversation Design paradigms

?An open-source survey of the latest video reasoning tasks, paradigms, and benchmarks.

Materials for the course Principles of AI: LLMs at UPenn (Stat 9911, Spring 2025). LLM architectures, training paradigms (pre- and post-training, alignment), test-time computation, reasoning, safety and robustness (jailbreaking, oversight, uncertainty), representations, interpretability (circuits), etc.

Code for the paper 'On Learning Paradigms for the Travelling Salesman Problem' (NeurIPS 2019 Graph Representation Learning Workshop)

A comprehensive paper list of deep learning for crack detection, in terms of learning paradigms, generalizability and datasets.

The code repository for "Model Spider: Learning to Rank Pre-Trained Models Efficiently"

This repository contains code and data download instructions for the workshop paper "Improving Hierarchical Product Classification using Domain-specific Language Modelling" by Alexander Brinkmann and Christian Bizer.

Outline Driven Development – Beyond "specs" or "vibes" using AST analysis and Modern CLI Tools w/ context engineering; batteries-included

Text2Sim MCP Server is a simulation engine that supports multiple modeling paradigms, including Discrete-Event Simulation (DES) and System Dynamics (SD). It integrates with LLMs using the Model Context Protocol (MCP), enabling powerful simulation capabilities within natural language environments like Claude Desktop.

(2021) Robust Deepfake Detection project for the Deep Learning course at ETH. Authors: David Kamm, Nicolas Muntwyler, Alexander Timans, Moritz Vandenhirtz

A meticulously curated collection of hands-on Jupyter notebooks, designed to illuminate the comprehensive application of MLflow across a spectrum ranging from foundational Machine Learning principles to pioneering Generative AI paradigms.

Erg?nzende Begleitmaterialien für das Buch "KI für Content Creation" von Alexander Loth

Position-Candidate-Hypothesis (PCH) is a theoretical paradigm for structural-statistical analysis of NP-complete problems.

A JAX implementation to unify machine learning paradigms into a single design pattern

Qualitative benchmark suite for evaluating AI coding agents and orchestration paradigms on realistic, complex development tasks

This repository exemplifies a robust ML workflow, leveraging MLflow for experiment tracking, Docker for containerization, TensorFlow Serving for model deployment, and GitHub Actions for CI/CD. It embodies a comprehensive system designed to predict diabetes progression using advanced machine learning paradigms.

sets a roundtable of reasoning paradigms to respond to a query with stated available resources.

A revolutionary synthetic intelligence system using the sacred geometric pattern of the Flower of Life. This system features 37 AI nodes arranged in a geometric pattern, each capable of inference with real-time ripple echo feedback loops, resonance, and harmonics.