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Machine Learning E194-06

  • A Research Unit of The Institute of Information Systems Engineering

Our research aims to narrow the gap between theoretically well-understood and practically relevant machine learning.

Research Unit Machine Learning

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About

Our research aims to narrow the gap between theoretically well-understood and practically relevant machine learning.

Research questions concern for instance:

  • learning with non-conventional data, i.e., data that has no inherent representation in a table or Euclidean space
  • incorporation of invariances as well as expert domain knowledge in learning algorithms
  • computational, sample, query, and communication complexity of learning algorithms
  • constructive machine learning scenarios such as structured output prediction
  • learning with small labelled data sets and large unlabelled data sets
  • adverserial learning with mistake and/or regret bounds
  • parallelisation/distribution of learning algorithms
  • approximation of learning algorithms
  • scalability of learning algorithms
  • reliability of learning algorithms
  • extreme learning
  • …

To demonstrate the practical effectiveness of novel learning algorithms, we apply them in Chemistry, Material Science, Electrical Engineering, Computer Games, Humanities, etc.

The research Unit Machine Learning is part of the Institute of Information Systems Engineering.

Sabine Andergassen
Sabine Andergassen S. Andergassen

Associate Professor
Assoc.Prof. Dr.

Thomas Gärtner
Thomas Gärtner T. Gärtner

Head of Research Unit
Univ.Prof. DI(BA) Dr. / MSc

Clemens Heitzinger
Clemens Heitzinger C. Heitzinger

Associate Professor
Assoc.Prof. Dr. DI

Stefan Neumann
Stefan Neumann S. Neumann

Assistant Professor
Asst.Prof. Dr. / BSc MSc

Tamara Drucks
Tamara Drucks T. Drucks

PreDoc Researcher
DI

Kilian Fraboulet
Kilian Fraboulet K. Fraboulet

PostDoc Researcher
Dr.

Patrick Indri
Patrick Indri P. Indri

PreDoc Researcher
MSc

Fabian Jogl
Fabian Jogl F. Jogl

PreDoc Researcher
DI / BSc

Amirreza Khodadadian
Amirreza Khodadadian A. Khodadadian

PostDoc Researcher
Dr. / MSc

Ehsan Khodadadian
Ehsan Khodadadian E. Khodadadian

PreDoc Researcher
MSc

Sebastian Johannes Lüderssen
Sebastian Johannes Lüderssen S. Lüderssen

PreDoc Researcher
MSc

Sagar Malhotra
Sagar Malhotra S. Malhotra

PostDoc Researcher
MSc PhD

Maryam Parvizi
Maryam Parvizi M. Parvizi

PostDoc Researcher
Dr. / BSc MSc

Miriam Patricolo
Miriam Patricolo M. Patricolo

PreDoc Researcher
Mag.

David Penz
David Penz D. Penz

PreDoc Researcher
DI / BA

Christoph Sandrock
Christoph Sandrock C. Sandrock

PreDoc Researcher
MSc

Maximilian Thiessen
Maximilian Thiessen M. Thiessen

PreDoc Researcher
MSc

Pascal Welke
Pascal Welke P. Welke

PostDoc Researcher
Dr.

Ülkühan Kacmaz
Ülkühan Kacmaz UE. Kacmaz

Office Services

Martina Trauner
Martina Trauner M. Trauner

IT Services

Jakob aus der Schmitten
Jakob aus der Schmitten J. aus der Schmitten

Student Staff
BSc

Rudolf Mayer
Rudolf Mayer R. Mayer

Lecturer
Mag. DI

2023W

2024S

 

2024

2023

  • Improving sensor interoperability between contactless and contact-based fingerprints using pose correction and unwarping / Ruzicka, L., Dominik Söllinger, Kohn, B., Heitzinger, C., Uhl, A., & Strobl, B. (2023). Improving sensor interoperability between contactless and contact-based fingerprints using pose correction and unwarping. IET Biometrics, 2023, Article 7519499. https://doi.org/10.1049/2023/7519499
  • Maximally Expressive GNNs for Outerplanar Graphs / Bause, F., Jogl, F., Indri, P., Drucks, T., Penz, D., Kriege, N., Gärtner, T., Welke, P., & Thiessen, M. (2023). Maximally Expressive GNNs for Outerplanar Graphs. In NeurIPS 2023 Workshop: New Frontiers in Graph Learning. NeurIPS 2023 Workshop: New Frontiers in Graph Learning, New Orleans, LA, United States of America (the). OpenReview.net. https://doi.org/10.34726/5433
    Download: PDF (880 KB)
    Project: StruDL (2023–2027)
  • Extending Graph Neural Networks with Global Features / Brasoveanu, A. D., Jogl, F., Welke, P., & Thiessen, M. (2023, December 1). Extending Graph Neural Networks with Global Features [Poster Presentation]. Learning-on-Graphs Conference 2023: Local Meetup, München, Germany. https://doi.org/10.34726/5343
    Downloads: Paper (365 KB) / Poster (289 KB)
  • Maximally Expressive GNNs for Outerplanar Graphs / Bause, F., Jogl, F., Indri, P., Drucks, T., Penz, D., Kriege, N., Gärtner, T., Welke, P., & Thiessen, M. (2023, December 1). Maximally Expressive GNNs for Outerplanar Graphs [Poster Presentation]. Learning-on-Graphs Conference 2023: Local Meetup, München, Germany. https://doi.org/10.34726/5344
    Downloads: Paper (880 KB) / Poster (422 KB)
    Project: StruDL (2023–2027)
  • Extending Graph Neural Networks with Global Features / Brasoveanu, A. D., Jogl, F., Welke, P., & Thiessen, M. (2023, November 27). Extending Graph Neural Networks with Global Features [Poster Presentation]. Learning on Graphs Conference 2023, Austria. https://doi.org/10.34726/5281
    Download: Camera-ready full paper (365 KB)
  • Maximally Expressive GNNs for Outerplanar Graphs / Bause, F., Jogl, F., Welke, P., & Thiessen, M. (2023). Maximally Expressive GNNs for Outerplanar Graphs. In The Second Learning on Graphs Conference (LoG 2023). Second Learning on Graphs Conference (LoG 2023), Austria. OpenReview.net. https://doi.org/10.34726/5434
    Download: PDF (541 KB)
    Project: StruDL (2023–2027)
  • ModelRevelator: Fast phylogenetic model estimation via deep learning / Burgstaller-Muehlbacher, S., Crotty, S., Schmidt, H., Reden, F., Drucks, T., & von Haeseler, A. (2023). ModelRevelator: Fast phylogenetic model estimation via deep learning. Molecular Phylogenetics and Evolution, 188, Article 107905. https://doi.org/10.1016/j.ympev.2023.107905
    Download: PDF (5.18 MB)
  • No PAIN no Gain: More Expressive GNNs with Paths / Graziani, C., Drucks, T., Bianchini, M., Scarselli, F., & Gärtner, T. (2023). No PAIN no Gain: More Expressive GNNs with Paths. In NeurIPS 2023 Workshop: New Frontiers in Graph Learning. NeurIPS 2023 Workshop: New Frontiers in Graph Learning, New Orleans, LA, United States of America (the). OpenReview.net. https://doi.org/10.34726/5429
    Download: PDF (1.01 MB)
  • Expressivity-Preserving GNN Simulation / Jogl, F., Thiessen, M., & Gärtner, T. (2023). Expressivity-Preserving GNN Simulation. In Advances in Neural Information Processing Systems. 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, United States of America (the).
  • Correlations on all length scales in the Hubbard model and beyond / Andergassen, S. (2023, September 21). Correlations on all length scales  in the Hubbard model and beyond [Conference Presentation]. PHD-WORKSHOP FOR 5413, Germany.
  • fRG analysis of the extended Hubbard model - SBE fluctuation diagnostics of screening / Al-Eryani, A., Heinzelmann, S., & Andergassen, S. (2023, September 18). fRG analysis of the extended Hubbard model - SBE fluctuation diagnostics of screening [Poster Presentation]. Autumn School on Correlated Electrons: Orbital Physics in Correlated Matter, Germany.
  • Modular fRG Code for Lattice Fermion Systems: Challenges, Strategies and Ideas / Al-Eryani, A., & Andergassen, S. (2023, September 6). Modular fRG Code for Lattice Fermion Systems: Challenges, Strategies and Ideas [Conference Presentation]. numErical MEthods for fRG in condENsed maTter, Germany.
  • Single-boson-exchange functional renormalization group and its application to the Hubbard model / Fraboulet, K., & Andergassen, S. (2023, September 6). Single-boson-exchange functional renormalization group and its application to the Hubbard model [Conference Presentation]. numErical MEthods for fRG in condENsed maTter, Germany.
  • Single Boson Exchange fRG for Extended Hubbard Interactions / Al-Eryani, A., Scherer, M., & Andergassen, S. (2023, September 5). Single Boson Exchange fRG for Extended Hubbard Interactions [Poster Presentation]. numErical MEthods for fRG in condENsed maTter, Germany.
  • Functional Renormalization Group Analysis of the Pseudogap Opening in the Hubbard Model / Patricolo, M., & Andergassen, S. (2023, September 5). Functional Renormalization Group Analysis of the Pseudogap Opening in the Hubbard Model [Poster Presentation]. numErical MEthods for fRG in condENsed maTter, Germany.
  • Cluster Extension of DMF²RG / Krämer, M., & Andergassen, S. (2023, September 5). Cluster Extension of DMF2RG [Poster Presentation]. numErical MEthods for fRG in condENsed maTter, Germany.
  • The Mott metal-insulator transition in the two-dimensional Hubbard model - a cellular dynamical mean-field study on large clusters / Meixner, M., Klett, M., Heinzelmann, S., Wentzell, N., Hansmann, P., Andergassen, S., & Schäfer, T. (2023, July 24). The Mott metal-insulator transition in the two-dimensional Hubbard model - a cellular dynamical mean-field study on large clusters [Poster Presentation]. Workshop “Correlations in Novel Quantum Materials” 2023, Germany. http://hdl.handle.net/20.500.12708/187908
  • Efficient fRG flow equations for extended interactions and an application to the square and triangular lattices / Al-Eryani, A., Heinzelmann, S., Fraboulet, K., Scherer, M., & Andergassen, S. (2023, July 24). Efficient fRG flow equations for extended interactions and an application to the square and triangular lattices [Poster Presentation]. Workshop “Correlations in Novel Quantum Materials” 2023, Germany.
  • Single-boson-exchange functional renormalization group and its application to the Hubbard model / Fraboulet, K., Al-Eryani, A., Andergassen, S., & Heinzelmann, S. (2023, July 24). Single-boson-exchange functional renormalization group and its application to the Hubbard model [Poster Presentation]. Workshop “Correlations in Novel Quantum Materials” 2023, Germany.
  • Pseudogap opening in the Hubbard model at strong coupling / Patricolo, M., Vilardi, D., Heinzelmann, S., Andergassen, S., & Bonetti, V. (2023, July 24). Pseudogap opening in the Hubbard model at strong coupling [Poster Presentation]. Workshop “Correlations in Novel Quantum Materials” 2023, Germany.
  • Cluster extension of DMF2RG / Krämer, M., Fraboulet, K., Vilardi, D., Bonetti, P. M., Meixner, M., Schäfer, T., & Andergassen, S. (2023, July 24). Cluster extension of DMF2RG [Poster Presentation]. Workshop “Correlations in Novel Quantum Materials” 2023, Stuttgart, Germany.
  • A New Aligned Simple German Corpus / Toborek, V., Busch, M., Boßert, M., Bauckhage, C., & Welke, P. (2023). A New Aligned Simple German Corpus. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (pp. 11393–11412). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.638
    Project: StruDL (2023–2027)
  • Hidden Schema Networks / Sanchez, R., Conrads, L., Welke, P., Cvejoski, K., & Ojeda, C. (2023). Hidden Schema Networks. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 4764–4798). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.acl-long.263
    Project: StruDL (2023–2027)
  • AFEM for the fractional Laplacian / Melenk, J. M., Bahr, B., Faustmann, M., Parvizi, M., & Praetorius, D. (2023, June 12). AFEM for the fractional Laplacian [Conference Presentation]. Foundations of Computational Mathematics (FoCM 2023), Paris, France.
  • Machine learning dynamics of quantum systems / Andergassen, S. (2023, May 8). Machine learning dynamics of quantum systems [Presentation]. TACO Colloquium, Austria.
  • Pseudogap opening in the Hubbard model at strong coupling / Patricolo, M., Heinzelmann, S., Bonetti, P. M., Vilardi, D., & Andergassen, S. (2023, May 4). Pseudogap opening in the Hubbard model at strong coupling [Poster Presentation]. Exploring New Topics with Functional Renormalisation, Germany.
  • Can stochastic weight averaging improve generalization in private learning? / Patrick Indri, Tamara Drucks, & Gärtner, T. (2023). Can stochastic weight averaging improve generalization in private learning? In ICLR 2023 Workshop on Trustworthy and Reliable Large-Scale Machine Learning Models. ICLR 2023 Workshop on Trustworthy and Reliable Large-Scale Machine Learning Models, Kigali, Rwanda. https://doi.org/10.34726/5349
    Download: Main paper (366 KB)
  • Machine learning dynamics of quantum systems / Andergassen, S. (2023, April 13). Machine learning dynamics of quantum systems [Conference Presentation]. Machine Learning and (Quantum) Physics Workshop 2023, Obergurgl, Austria.
  • Krein support vector machine classification of antimicrobial peptides / Redshaw, J., Ting, D. S. J., Brown, A., Hirst, J. D., & Gärtner, T. (2023). Krein support vector machine classification of antimicrobial peptides. Digital Discovery. https://doi.org/10.1039/D3DD00004D
  • Development of a reinforcement learning algorithm to optimize corticosteroid therapy in critically ill patients with sepsis / Bologheanu, R., Kapral, L., Laxar, D., Maleczek, M., Dibiasi, C., Zeiner, S., Agibetov, A., Ercole, A., Thoral, P., Elbers, P., Clemens Heitzinger, & Kimberger, O. (2023). Development of a reinforcement learning algorithm to optimize corticosteroid therapy in critically ill patients with sepsis. Journal of Clinical Medicine, 12(4), Article 1513. https://doi.org/10.3390/jcm12041513
  • Extending Graph Neural Networks with Global Features / Brasoveanu, A. D., Jogl, F., Welke, P., & Thiessen, M. (2023). Extending Graph Neural Networks with Global Features. In The Second Learning on Graphs Conference (LoG 2023). The Second Learning on Graphs Conference (LoG 2023), online, Austria. OpenReview.net. https://doi.org/10.34726/5423
    Downloads: PDF (365 KB) / Poster (289 KB)
  • Graph Pooling Provably Improves Expressivity / Lachi, V., Moallemy-Oureh, A., Roth, A., & Welke, P. (2023). Graph Pooling Provably Improves Expressivity. In NeurIPS 2023 Workshop: New Frontiers in Graph Learning. NeurIPS 2023 Workshop: New Frontiers in Graph Learning, New Orleans, LA, United States of America (the). OpenReview.net. https://doi.org/10.34726/5432
    Downloads: PDF (245 KB) / Poster (210 KB)
    Project: StruDL (2023–2027)
  • Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions / Daniele, A., Campari, T., Malhotra, S., & Serafini, L. (2023). Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23) (pp. 3597–3605). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2023/400
  • Retention is All You Need / Mohiuddin, K., Alam, M. A., Alam, M. M., Welke, P., Martin, M., Lehmann, J., & Vahdati, S. (2023). Retention is All You Need. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 4752–4758). https://doi.org/10.1145/3583780.3615497
  • An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning / Müller, S., Toborek, V., Beckh, K., Jakobs, M., Bauckhage, C., & Welke, P. (2023). An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning. In D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis, & F. Bonchi (Eds.), Machine Learning and Knowledge Discovery in Databases: Research Track : European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part III (pp. 462–478). Springer. https://doi.org/10.1007/978-3-031-43418-1_28
    Project: StruDL (2023–2027)
  • Expectation-Complete Graph Representations with Homomorphisms / Welke, P., Thiessen, M., Jogl, F., & Gärtner, T. (2023). Expectation-Complete Graph Representations with Homomorphisms. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, & J. Scarlett (Eds.), Proceedings of the 40th International Conference on Machine Learning (pp. 36910–36925). Proceedings of Machine Learning Research.
    Project: StruDL (2023–2027)

2022

  • Generalized Laplacian Positional Encoding for Graph Representation Learning / Maskey, S., Parviz, A., Thiessen, M., Stärk, H., Sadikaj, Y., & Maron, H. (2022, December 3). Generalized Laplacian Positional Encoding for Graph Representation Learning [Poster Presentation]. NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, New Orleans, United States of America (the). https://doi.org/10.34726/3908
    Download: Paper (1.74 MB)
  • Expectation Complete Graph Representations Using Graph Homomorphisms / Welke, P., Thiessen, M., & Gärtner, T. (2022, November 30). Expectation Complete Graph Representations Using Graph Homomorphisms [Poster Presentation]. First Learning on Graphs Conference (LoG 2022), Unknown. https://doi.org/10.34726/3883
    Download: Accepted Paper (294 KB)
  • Active Learning of Classifiers with Label and Seed Queries / Bressan, M., Cesa-Bianchi, N., Lattanzi, S., Paudice, A., & Thiessen, M. (2022). Active Learning of Classifiers with Label and Seed Queries. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, Louisiana, United States of America (the). Neural information processing systems foundation. https://doi.org/10.34726/4021
    Downloads: Full paper (387 KB) / Supplemantary material (294 KB)
  • LieGG: Studying Learned Lie Group Generators / Moskalev, A., Sepliarskaia, A., Sosnovik, I., & Smeulders, A. (2022). LieGG: Studying Learned Lie Group Generators. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Advances in Neural Information Processing Systems 35 (NeurIPS 2022), New Orleans, United States of America (the).
  • Expectation Complete Graph Representations using Graph Homomorphisms / Thiessen, M., Pascal Welke, & Gärtner, T. (2022, October 25). Expectation Complete Graph Representations using Graph Homomorphisms [Presentation]. Workshop: Hot Topics in Graph Neural Networks, Kassel, Germany.
    Download: slides of invited talk (1.26 MB)
  • Expectation Complete Graph Representations Using Graph Homomorphisms / Thiessen, M., Welke, P., & Gärtner, T. (2022, October 21). Expectation Complete Graph Representations Using Graph Homomorphisms [Poster Presentation]. New Frontiers in Graph Learning (GLFrontiers) NeurIPS 2022 Workshop, New Orleans, United States of America (the). https://doi.org/10.34726/3863
    Download: Full paper (304 KB)
  • Weisfeiler and Leman Return with Graph Transformations / Jogl, F., Thiessen, M., & Gärtner, T. (2022). Weisfeiler and Leman Return with Graph Transformations. In 18th International Workshop on Mining and Learning with Graphs - Accepted Papers. 18th International Workshop on Mining and Learning with Graphs, Grenoble, France. https://doi.org/10.34726/3829
    Download: Full paper as PDF (439 KB)
  • Unlearning Protected User Attributes in Recommendations with Adversarial Training / Ganhör, C., Penz, D., Rekabsaz, N., Lesota, O., & Schedl, M. (2022). Unlearning Protected User Attributes in Recommendations with Adversarial Training. In SIGIR ’22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2142–2147). https://doi.org/10.1145/3477495.3531820
  • EmoMTB: Emotion-aware Music Tower Blocks / Melchiorre, A. B., Penz, D., Ganhör, C., Lesota, O., Fragoso, V., Friztl, F., Parada-Cabaleiro, E., Schubert, F., & Schedl, M. (2022). EmoMTB: Emotion-aware Music Tower Blocks. In ICMR ’22: Proceedings of the 2022 International Conference on Multimedia Retrieval (pp. 206–210). https://doi.org/10.1145/3512527.3531351
  • Reducing Learning on Cell Complexes to Graphs / Jogl, F., Thiessen, M., & Gärtner, T. (2022). Reducing Learning on Cell Complexes to Graphs. In ICLR 2022 Workshop on Geometrical and Topological Representation Learning. ICLR 2022 Workshop on Geometrical and Topological Representation Learning, Unknown. https://doi.org/10.34726/3421
    Download: Paper as PDF (263 KB)
  • Dojo: A Benchmark for Large Scale Multi-Task Reinforcement Learning / Schmidt, D. (2022). Dojo: A Benchmark for Large Scale Multi-Task Reinforcement Learning. In ALOE 2022. Accepted Papers. Workshop on Agent Learning in Open-Endedness (ALOE) at ICLR 2022, Unknown. https://doi.org/10.34726/4263
    Download: PDF (5 MB)
  • Kernel Methods for Predicting Yields of Chemical Reactions / Haywood, A. L., Redshaw, J., Hanson-Heine, M. W. D., Taylor, A., Brown, A., Mason, A. M., Gärtner, T., & Hirst, J. D. (2022). Kernel Methods for Predicting Yields of Chemical Reactions. Journal of Chemical Information and Modeling, 62(9), 2077–2092. https://doi.org/10.1021/acs.jcim.1c00699
  • One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Systems / Indri, P., Bartoli, A., Medvet, E., & Nenzi, L. (2022). One-Shot Learning of Ensembles of Temporal Logic Formulas for Anomaly Detection in Cyber-Physical Systems. In EuroGP 2022: Genetic Programming (pp. 34–50). Springer-Verlag. https://doi.org/10.1007/978-3-031-02056-8_3
  • Online learning of convex sets on graphs / Thiessen, M., & Gärtner, T. (2022). Online learning of convex sets on graphs. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022), Grenoble, France.
  • LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis / Schedl, M., Brandl, S., Lesota, O., Parada-Cabaleiro, E., Penz, D., & Rekabsaz, N. (2022). LFM-2b: A Dataset of Enriched Music Listening Events for Recommender Systems Research and Fairness Analysis. In ACM SIGIR Conference on Human Information Interaction and Retrieval. ACM. https://doi.org/10.1145/3498366.3505791

2021

2020

  • Efficient Reinforcement Learning via Self-supervised learning and Model-based methods / Schmied, T., & Thiessen, M. (2020). Efficient Reinforcement Learning via Self-supervised learning and Model-based methods. In Challenges of Real-World RL. NeurIPS 2020 Workshop. Accepted Papers. 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada. https://doi.org/10.34726/4524
    Download: Accepted paper (236 KB)
  • Active Learning on Graphs with Geodesically Convex Classes / Thiessen, M., & Gärtner, T. (2020). Active Learning on Graphs with Geodesically Convex Classes. In Proceedings of 16th International Workshop on Mining and Learning with Graphs (MLG’20). 16th International Workshop on Mining and Learning with Graphs, Austria. https://doi.org/10.34726/3467
    Download: author's original (729 KB)
  • Machine Learning for Chemical Synthesis / Haywood, A. L., Redshaw, J., Gärtner, T., Taylor, A., Mason, A. M., & Hirst, J. D. (2020). Machine Learning for Chemical Synthesis. In H. M. Cartwright (Ed.), Machine Learning in Chemistry : The Impact of Artificial Intelligence (pp. 169–194). The Royal Society of Chemistry. https://doi.org/10.1039/9781839160233-00169

2019

 

Soon, this page will include additional information such as reference projects, conferences, events, and other research activities.

Until then, please visit Machine Learning’s research profile in TISS .