Funktionen

Below is a list of the topics that are available. Please, consider the specified literature as starting point for your literature research.

Topic Area 1: AI, Machine Learning and Deep Learning

1. Introduction to AI and Machine Learning (FG)
• Bommasani, On the Opportunities and Risks of Foundation Models, https://arxiv.org/abs/2108.07258?sf149288348=1, 2021
• Nathan Benaich and Ian Hogarth, State of AI Report 2021, https://www.stateof.ai/, 2021
• 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
• 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. Computer Vision: Convolutional Neural Networks and Vision Transformers (FG)
• Radford et al., Learning Transferable Visual Models From Natural Language Supervision, https://arxiv.org/abs/2103.00020, 2021
• 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
• Stanford, CS231n: Convolutional Neural Networks, 2017
• Dosovitskiy, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, https://arxiv.org/abs/2010.11929, 2020
3. Natural Language Processing and Transformer Models
• Brown et al., Language Models are Few-Shot Learners, 2020
• 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
4. Foundation Models und Transfer Learning
   • Bommasani, On the Opportunities and Risks of Foundation Models, https://arxiv.org/abs/2108.07258?sf149288348=1, 2021
   • Radford et al., Learning Transferable Visual Models From Natural Language Supervision, https://arxiv.org/pdf/2103.00020.pdf, 2021
   • Radford et al., Language Models are Unsupervised Multitask Learners, https://paperswithcode.com/paper/language-models-are-unsupervised-multitask, 2019
   • Chen et al., A Simple Framework for Contrastive Learning of Visual Representations, https://arxiv.org/pdf/2002.05709.pdf, 2020
   • Radford et al., Learning Transferable Visual Models From Natural Language Supervision, https://arxiv.org/abs/2103.00020, 2021 <
5. ML and Simulations/HPC (English) (DD)
   • Fox et al., Understanding ML driven HPC: Applications and Infrastructure, https://arxiv.org/abs/1909.02363, 2021
   • Li et al., Fourier Neural Operator for Parametric Partial Differential Equations, https://arxiv.org/abs/2010.08895, 2020
   • Pestourie et al., Active learning of deep surrogates for PDEs: application to metasurface design, https://www.nature.com/articles/s41524-020-00431-2, 2021
6. 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
   • 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
   • Gärtler, J., Kehlbeck, R., & Deussen, O. (2019). A Visual Exploration of Gaussian Processes. Distill, 4(4), e17

Topic Area 2: Systems for ML

7. Scaling Machine Learning (English) (DD)
• Narayanan et al., Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM, https://arxiv.org/abs/2104.04473, 2021
• Shoeybi, Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism, https://arxiv.org/abs/1909.08053, 2019
• Kaplan et al., Scaling Laws for Neural Language Models, https://arxiv.org/pdf/2001.08361.pdf, 2021
• Dean et al., Large Scale Distributed Deep Networks, 2012
• Alex Krizhevsky, One weird trick for parallelizing convolutional neural networks, 2014
• 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
8. AI Domain-specific Architectures
   • Hooker, The Hardware Lottery, 2020
   • GraphCore, https://www.graphcore.ai/
   • Cerabras, https://www.cerebras.net/
9. Security and Privacy in Machine Learning (SGC)
   • Papernot et al., SoK: Toward the science of security und privacy in machine learning, 2018
10. Machine Learning for Crypto-Analysis (SGC)
    • Truong et al., Machine Learning Cryptanalysis of a Quantum Random Number Generator, 2018
11. Deep Learning for Systems (MC)
• 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
12. Knowledge Graphs (MC)
    • 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

Topic Area 3: Quantum Computing

13. Quantum Machine Learning (AL)
    • 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/
14. Quantum Benchmarking (AL)
    • Cross et al., Validating quantum computers using randomized model circuits, https://arxiv.org/abs/1811.12926, 2018
    • Blume-Kohout et al., A volumetric framework for quantum computer benchmarks, https://arxiv.org/abs/1904.05546, 2020
    • Mills et al., Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack, https://arxiv.org/abs/2006.01273, 2021
    • Martiel et al., Benchmarking quantum co-processors in an application-centric, hardware-agnostic and scalable way, https://arxiv.org/abs/2102.12973, 2021
    • Lubinski et al., Application-Oriented Performance Benchmarks for Quantum Computing, https://arxiv.org/abs/2110.03137, 2021