Lehrstuhl für Technische Informatik - Rechnernetze

Im Folgenden finden Sie eine Aufstellung der zur Verfügung stehenden Themen. Die angegebene Literatur versteht sich als Startlektüre und weitere Literatur sollte selbstständig recherchiert wertden.

Themenblock 1: Data Science und Machine Learning

1. Data Science and Machine Learning (English)
• Peng, Matsui, The Art of Data Science, 2017
• Hey, The Fourth Paradigm of Scientific Discovery, 2009
• Cao, Data Science: Challenges and Directions, Communications of the ACM, 2017
• Donoho, 50 Years of Data Science, 2015
• Benaich, Hogarth, State of AI Report, 2020
• Scikit-Learn, 2017
• Machine Learning Frameworks (Github), 2017
• Dominigos, A Few Useful Things to Know about Machine Learning, KDD, 2014
• Sculley et al., Machine Learning: The High Interest Credit Card of Technical Debt, NIPS, 2014
2. Distributed Machine Learning (English)
• Dean et al., Large Scale Distributed Deep Networks, 2012
• Li et al., Scaling Distributed Machine Learning with the Parameter Server, OSDI, 2014
• Xing et al., Petuum: A New Platform for Distributed Machine Learning on Big Data, KDD, 2015
• Meng et al., MLlib: Machine Learning in Apache Spark, Journal of Machine Learning Research, 2016
• PyTorch: Writing Distributed Applications with PyTorch, https://pytorch.org/tutorials/intermediate/dist_tuto.html
3. Model Management and Deployment
• Zaharia, Accelerating the Machine Learning Lifecycle with MLflow, 2018
• Baylor et al., TFX: A TensorFlow-Based Production-Scale Machine Learning Platform, 2017
• Sun et al., Gallery: A Machine Learning Model Management System at Uber, 2020
• Cloud
• Crankshaw et al., Clipper: A Low-Latency Online Prediction Serving System, NSDI, 2017
• Tensorflow Serve, https://www.tensorflow.org/serving/, 2017
• Seldon, https://github.com/SeldonIO/seldon-core, 2020
• Edge Inference
• Apple CoreML, https://developer.apple.com/documentation/coreml, 2017
• Nvidia, TensorRT, https://developer.nvidia.com/tensorrt, 2017

Themenblock 2: Deep Learning and AI

4. Deep Learning: Convolutional Neural Networks
• LeCun, Bengio, Hinton, Deep Learning, Nature, 2015
• Krizhevsky et al., ImageNet Classification with Deep Convolutional Neural Networks, 2012
• Ujjwal Karn, An intuitive explanation of convolutional neural networks, 2016
• Alex Krizhevsky, One weird trick for parallelizing convolutional neural networks, 2014
• Stanford, CS231n: Convolutional Neural Networks, 2017
5. Deep Learning: Natural Language Processing and Transformer Models
• Devlin et al., BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, 2019
• Vaswani et al., Attention is all you need, https://arxiv.org/abs/1706.03762
• Brown et al., Language Models are Few-Shot Learners, 2020
6. AutoML
   • Elsken et al., Neural Architecture Search: A Survey, https://arxiv.org/abs/1808.05377, 2019
   • Zoph et al., Neural Architecture Search with Reinforcement Learning, ICLR, 2017
7. Reinforcement Learning
• Mnih et al., Playing Atari with Deep Reinforcement Learning, 2013
• Silver et al., Mastering the game of Go without human knowledge, 2017
8. Deep Learning Frameworks: Caffe, Torch/PyTorch, Tensorflow, CNTK, MXNet
• Jia et al., Caffe: Convolutional Architecture for Fast Feature Embedding, MM, 2014
• PyTorch, 2017
• Abadi et al., TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems, White Paper, 2015
• Seide et al., CNTK: Microsoft's Open Source Deep-Learning Toolkit, White Paper, 2016
• Chen et al., MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems, 2015
9. Visualizing AI
   • Li, M., Zhao, Z., & Scheidegger, C. (2020). Visualizing Neural Networks with the Grand Tour. Distill, 5(3), e25.
   • Smith, E. M., Smith, J., Legg, P., & Francis, S. Visualising state space representations of LSTM networks. Presented at Workshop on Visualization for AI Explainability
   • Görtler, J., Kehlbeck, R., & Deussen, O. (2019). A Visual Exploration of Gaussian Processes. Distill, 4(4), e17
10. Explainable AI
    • Explaining Explanations: An Overview of Interpretability of Machine Learning: https://arxiv.org/abs/1806.00069v3
    • Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead: https://www.nature.com/articles/s42256-019-0048-x
    • The challenge of crafting intelligible intelligence: https://dl.acm.org/doi/10.1145/3282486

Themenblock 3: Emerging Topics

11. Quantum Machine Learning
    • Lloyd et al., Quantum machine learning, https://www.nature.com/articles/nature23474, 2016
    • Schuld et al., An introduction to quantum machine learning, https://arxiv.org/pdf/1409.3097.pdf
    • Hubregtsen et al., Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability, 2020
    • PennyLane, https://pennylane.ai/
12. AI Domain-specific Architectures
    • Hooker, The Hardware Lottery, 2020
    • GraphCore, https://www.graphcore.ai/
    • Cerabras, https://www.cerebras.net/
13. Security and Privacy in Machine Learning
    • Papernot et al., SoK: Toward the science of security und privacy in machine learning, 2018
14. Machine Learning for Crypto-Analysis
    • Truong et al., Machine Learning Cryptanalysis of a Quantum Random Number Generator, 2018
15. Fault management based on machine learning (English)
    • Velasco, Luis, and Danish Rafique. "Fault management based on machine learning." 2019 Optical Fiber Communications Conference and Exhibition (OFC). IEEE, 2019
    • Mulvey, David, et al. "Cell fault management using machine learning techniques." IEEE Access 7 (2019): 124514-124539
    • Ferreira, Vinicius C., et al. "Fault detection and diagnosis for solar-powered wireless mesh networks using machine learning." 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM). IEEE, 2017
16. Deep Learning for Systems
• Mirhoseini et al., Chip Placement with Deep Reinforcement Learning, https://arxiv.org/pdf/2004.10746.pdf, 2020
• Mirhoseini et al., Gdp: Generalized Device Placement for Dataflow Graphs, https://arxiv.org/pdf/1910.01578.pdf, 2019
• Tim Kraska et al., The Case for Learned Index Structures, https://dl.acm.org/doi/10.1145/3183713.3196909, 2018
17. Knowledge Graphs
    • Ehrlinger et al., Towards a Definition of Knowledge Graphs, 2016
    • Petroni et al., Language Models as Knowledge Bases, https://arxiv.org/abs/1909.01066, 2019
    • Lenat, Eurisko: A Program That Learns New Heuristics and Domain Concepts, https://users.cs.northwestern.edu/~mek802/papers/not-mine/Lenat_EURISKO.pdf, 1983