Bachelor Seminar Wissenschaftliches Arbeiten

Motivation: In active learning, the learning algorithm is allowed to select the data points it wants to see labelled, for example, where it is most uncertain. The goal is to reduce the labelling effort. This is useful in applications where unlabelled data is abundant, yet labels are scarce, such as node classification in social networks, drug discovery, and autonomous driving.

Overview:

• chapter 1 “Automating inquiry” of Burr Settles’ “Active learning” (AL) book, 2012.
• introduction to active learning: Sanjoy Dasgupta - Microsoft 2016 (youtube-link)

Papers and topics:

• Bayesian active learning on graphs (Ma, Yifet, et al., “σ-optimality for active learning on gaussian random fields.” NIPS 2013)
• active search on graphs (Wang, Xuezhi, et al., “Active search on graphs” KDD 2013)
• shortest-path-based active learning (Dasarathy, et al. “S2: an efficient graph based active learning algorithm with application to nonparametric classification.” COLT 2015)

Motivation: Classical algorithms cannot be combined with neural networks as training neural networks would require the computation of a gradient which is not possible for classical algorithms. Thus, we are interested in making classical algorithms differentiable.

Papers & Projects:

• Petersen, Learning with Differentiable Algorithms, Doctoral Thesis at the University of Konstanz, arXiv, (2022)

The above thesis gives an introduction to the field of differentiable algorithms and is built upon many conference publications by the author at strong ML conferences. We recommend picking either one of the chapters or one of the underlying publications from ICML, ICLR, NeurIPS or CVPR as a basis.

Motivation: Computing a disentangled representation is a very desirable property for modern deep learning architectures. Having access to individual, disentangled factors is expected to provide significant improvements for generalisation, interpretability and explainability.

Overview:

• What is a good representation? (Bengio, et al., “Representation Learning: A Review and New Perspectives”, 2013)
• Two common architectures used for disentanglement:
• Variational Auto-Encoders (Kingma & Welling, “Auto-Encoding Variational Bayes”, 2013, and “An Introduction to Variational Autoencoders”, 2019)
• Generative Adversarial Networks (Goodfellow, et al., “Generative Adversarial Nets”, 2014)

Papers and topics:

• survey on useful Metrics (Carbonneau, et al., “Measuring Disentanglement: A Review of Metrics”, 2022; and Eastwood & Williams, “A Framework for the Quantitative Evaluation of Disentangled Representations”, 2018; and Do & Tran, “Theory and Evaluation Metrics for Learning Disentangled Representations”, 2019)
• fairness (Creager, et al., “Flexibly Fair Representation Learning by Disentanglement”, 2019)
• contrastive Learning (Cao, et al., “An Empirical Study on Disentanglement of Negative-free Contrastive Learning”, 2022)
• recommender Systems (Ma, et al., “Learning Disentangled Representations for Recommendation”, 2019)
• weakly-Supervised (Locatello, et al., “Weakly-Supervised Disentanglement Without Compromises”, 2020)
• semi-supervised (Nie, et al., “Semi-Supervised StyleGAN for Disentanglement Learning”, 2020)

Motivation: Many datastructures have an innate structure that our neural networks should respect. For example the output of a graph neural networks should not change if we permute the vertices (permutation equivariance/invariance).

Overview:

• chapter 8 “equivariant neural networks” of “Deep learning for molecules and materials” by Andrew D. White, 2021. (pdf).
• introduction to equivariance: Taco Cohen and Risi Kondor - Neurips 2020 Tutorial (first half) (slideslive-link)

Papers and topics:

• neural network that can learn on sets (Zaheer, et al. “Deep sets.” NeurIPS 2017)
• learning equivariance from data (Zhou, et al. “Meta-learning symmetries by reparameterization.” ICLR 2021)

Motivation: The ability of a model to adapt and perform well on new data is crucial. A model which generalises not only performs well on the training set, but on unseen data as well. Understanding and characterising why and how deep learning can generalise well is still an open question.

Overview:

• notes on generalisation (Prof. Roger Grosse) (link)
• generalisation and overfitting (youtube-link)

Papers and topics:

• memorisation (Arpit, et al. “A closer look at memorization in deep networks.” ICML 2017)
• double-descent (Belkin, et al. “Reconciling modern machine-learning practice and the classical bias–variance trade-off.” Proceedings of the National Academy of Sciences 2019)
• generalisation gap (Keskar, et al. “On large-batch training for deep learning: Generalization gap and sharp minima.” ICLR 2017)
• loss landscape (Fort and Jastrzebski. “Large scale structure of neural network loss landscapes.” NeurIPS 2019 and Li, et al. “Visualizing the loss landscape of neural nets.” NeurIPS 2018)

Motivation: Graphs are a very general structure and can be applied to many areas: molecules and developing medicine, geographical maps, spread of diseases. They can be used to model physical systems and solve partial differential equations. Even images and text can be seen as a special case of graphs. Thus it makes sense to develop neural networks that can work with graphs. GNNs have strong connections to many classical computer science topics (algorithmics, logic, …) while also making use of neural networks. This means that work on GNN can be very theoretical, applied or anything in between.

Overview:

• Veličković, Everything is connected: Graph neural networks, Current Opinion in Structural Biology, 2023
• Sanchez-Lengeling et al., A Gentle Introduction to Graph Neural Networks, distill.pub 2021
• Veličković, Intro to graph neural networks (ML Tech Talks), YouTube, 2021

Papers & Projects:

Note: For very long papers we do not expect you to read the entire appendix.

1. Expressiveness of GNNs
   • Zhang et al., Rethinking the Expressive Power of GNNs via Graph Biconnectivity, ICLR, 2023
   • Lim et al., Sign and Basis Invariant Networks for Spectral Graph Representation Learning, ICLR, 2023
   • Hwang et al., An Analysis of Virtual Nodes in Graph Neural Networks for Link Prediction, LoG, 2022
2. Oversmoothing & Over-Squashing
   • Keriven, Not too little, not too much: a theoretical analysis of graph (over)smoothing, NeurIPS, 2023
   • Abboud et al., Shortest Path Networks for Graph Property Prediction, LoG, 2022
   • Huang et al., You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets, LoG, 2022
3. Neural Algorithmic Reasoning / Algorithm Representation Learning
   • Numeroso et al., Dual Algorithmic Reasoning, ICLR, 2023
4. Learning from Graph Data with MLPs
   • Tian et al., Learning MLPs on Graphs: A Unified View of Effectiveness, Robustness, and Efficiency, ICLR, 2023

Motivation: Synthetic Aperture Radar (SAR) is an active microwave imaging system that provides high-resolution images day and night under all weather conditions. It has been widely used in many practical applications, such as environment, crop monitoring, and disaster detection. Using best-suited machine learning algorithms to derive useful information from these data is essential.

Overview:

• Chapter 2 “Spaceborne Synthetic Aperture Radar: Principles, Data Access, and Basic Processing Techniques” of Franz Meyer’s “SAR Handbook” book, 2019.
• “A Tutorial on Synthetic Aperture Radar” (IEEE Geoscience and remote sensing magazine 2013) by A. Moreira, et al.

Papers and topics:

• SAR Classification (Miller, et al. “Graph-based Active Learning for Semi-supervised Classification of SAR Data.” 2022)
• SAR Features (Zhang, et al. “Sparse Feature Clustering Network for Unsupervised SAR Image Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 2022)
• SAR Despeckling (Gu, et al. “A Two-Component Deep Learning Network for SAR Image Denoising.” IEEE Access 2021)
• SAR Despeckling (Yuan, et al. “Blind SAR Image Despeckling Using Self-Supervised Dense Dilated Convolutional Neural Network.” 2019)
• SAR Features (Jiang ,et al. “Unsupervised Deep Sparse Features Extraction for SAR Image Segmentation.” IEEE Transactions on Geoscience and Remote Sensing 2022)

Motivation: Machine learning systems are ubiquitous and it is necessary to make sure they behave as intended. In particular, trustworthiness can be achieved by means of privacy-preserving, robust, and explainable algorithms.

Overview:

• General: What does it mean for ML to be trustworthy? (youtube-link)
• General: Trustworthy ML (Kush R. Varshney) (link)
• Differential privacy: Chapter 2 of: Dwork, Cynthia, and Aaron Roth. “The algorithmic foundations of differential privacy.” Found. Trends Theor. Comput. Sci. 9.3-4 2014
• Explainability: Samek, Wojciech, and Klaus-Robert Müller. “Towards explainable artificial intelligence.” Explainable AI: interpreting, explaining and visualizing deep learning.” Springer, Cham, 2019

Papers and topics:

• interpreting model predictions
  • Ribeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. ““Why should i trust you?” Explaining the predictions of any classifier.” ACM SIGKDD 2016
  • Lundberg, Scott M., and Su-In Lee. “A unified approach to interpreting model predictions.” NeurIPS 2017
• reliability of explanation methods
  • Kumar, I. Elizabeth, et al. “Problems with Shapley-value-based explanations as feature importance measures.” ICML, 2020.
• robustness against attacks and adversaries
  • Jagielski, Matthew, et al. “Manipulating machine learning: Poisoning attacks and countermeasures for regression learning.” 2018 IEEE Symposium on Security and Privacy (SP). IEEE, 2018.
  • Carmon, Yair, et al. “Unlabeled data improves adversarial robustness.” NeurIPS 2019.
• differential privacy
  • Abadi, Martin, et al. “Deep learning with differential privacy.” Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. 2016.
  • Patel, Neel, Reza Shokri, and Yair Zick. “Model explanations with differential privacy.” 2022 ACM Conference on Fairness, Accountability, and Transparency. 2022.