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Praktikumsunterlagen (Slides, Übungen, etc.) für das Praktikum "Big Data and Machine Learning"
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Julia Hindel / moco-v2
Creative Commons Attribution Non Commercial 4.0 InternationalPyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722
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Fraunhofer IAO QC / SEQUOIA End-to-End / Automatic Feature Map Generation
Apache License 2.0Please add a short project description.
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Simulation env based on Brax / MuJoCo for robotic assembly tasks.
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GF7_public / IWM-GDTool
GNU General Public License v3.0 or laterConversion of MDBW data from GraphDesigner-Excel to RDF
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Fraunhofer IAO QC / SEQUOIA End-to-End / Neural Networks with Quantum Deterministic Annealing
Apache License 2.0Please add a short project description.
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Fraunhofer IAO QC / SEQUOIA End-to-End / Optimization EV Charging Schedules
Apache License 2.0Please add a short project description.
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This repository contains the python implementation from basic Machine Learning techniques to more complex ones.
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Thermal building simulation for feat pump control test bed. Can be used for development and testing heat controllers (RL, MPC, PID, heating curve) and synthetic data generation.
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Fraunhofer IAO QC / SEQUOIA End-to-End / Error Mitigation by Zero Noise Extrapolation
Apache License 2.0Demonstrator for Zero-Noise Extrapolation (ZNE) and Inverted-Circuit ZNE
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Fraunhofer IAO QC / SEQUOIA End-to-End / QC Network Resilience Analysis
Apache License 2.0The demonstrator shows the time evolution of a small network with failures.
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Fraunhofer IAO QC / SEQUOIA End-to-End / Accelerating TSP Approximation
Apache License 2.0An alternative problem encoding for QAOA is used to accelerate cost function computation
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Fraunhofer IAO QC / SEQUOIA End-to-End / PDEs Solutions with Quantum Convolutional Neural Networks
Apache License 2.0Please add a short project description.
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Fraunhofer IAO QC / SEQUOIA End-to-End / Solving LamA Problem via MILP Model
Apache License 2.0Please add a short project description.
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Fraunhofer IAO QC / SEQUOIA End-to-End / Configuration Selection and Prioritization using QAOA
Apache License 2.0This notebook illustrates using the Quantum Approximate Optimization Algorithm (QAOA) to find optimized configurations for feature models with attributed costs on a quantum computer.
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