Prof. dr. M. (Max) Welling
Faculty of Science
Informatics Institute
Visiting address
- Science Park 904
- Room number: L4.17
Postal address
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Postbus 94323
1090 GH Amsterdam
Contact details
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Publications
- refereed (113)
- academic (7)
- popular scientific (7)
- conference contributions (11)
- recognitions (6)
- supervised theses (31)
- working papers / preprints (1)
- datasets (1)
2025
- Dijkman, J., Dijkstra, M., Van Roij, R., Welling, M., van de Meent, J.-W., & Ensing, B. (2025). Learning Neural Free-Energy Functionals with Pair-Correlation Matching. Physical Review Letters, 134(5), Article 056103. https://doi.org/10.1103/PhysRevLett.134.056103
2024
Igashov, I., Stärk, H., Vignac, C., Schneuing, A., Satorras, V. G., Frossard, P., Welling, M., Bronstein, M., & Correia, B. (2024). Equivariant 3D-conditional diffusion model for molecular linker design. Nature Machine Intelligence, 6(4), 417–427. https://doi.org/10.1038/s42256-024-00815-9 [details]- Romijnders, R., Louizos, C., Asano, Y. M., & Welling, M. (2024). Protect Your Score: Contact-tracing With Differential Privacy Guarantees. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), Proceedings of the 38th AAAI Conference on Artificial Intelligence: AAAI-2024 (Vol. 13, pp. 14829-14837). AAAI Press. https://doi.org/10.1609/aaai.v38i13.29402 [details]
- Schneuing, A., Harris, C., Du, Y., Didi, K., Jamasb, A., Igashov, I., Du, W., Gomes, C., Blundell, T. L., Lio, P., Welling, M., Bronstein, M., & Correia, B. (2024). Structure-based drug design with equivariant diffusion models. Nature Computational Science, 4(12), 899-909. https://doi.org/10.1038/s43588-024-00737-x [details]
- Weiler, M., Forré, P., Verlinde, E., & Welling, M. (2024). Equivariant and Coordinate Independent Convolutional Networks: A Gauge Field Theory of Neural Networks. World Scientific. https://doi.org/10.1142/14143
2023
- Bakker, T., van Hoof, H., & Welling, M. (2023). Learning Objective-Specific Active Learning Strategies with Attentive Neural Processes. 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 (Vol. I, pp. 3-19). (Lecture Notes in Computer Science; Vol. 14169), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.48550/arXiv.2309.05477, https://doi.org/10.1007/978-3-031-43412-9_1 [details]
- Bondesan, R., Gavves, E., Oh, C., & Welling, M. (2023). Batch Bayesian Optimization on Permutations using Acquisition Weighted Kernels. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 10, pp. 6843-6858). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2022/hash/2d779258dd899505b56f237de66ae470-Abstract-Conference.html [details]
- Holdijk, L., Du, Y., Hooft, F., Jaini, P., Ensing, B., & Welling, M. (2023). Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths. In A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, & S. Levine (Eds.), 37th Conference on Neural Information Processing Systems, NeurIPS 2023 (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation.
- Löwe, S., Lippe, P., Locatello, F., & Welling, M. (in press). Rotating Features for Object Discovery. In 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (Advances in Neural Information Processing Systems; Vol. 36). Neural Information Processing Systems Foundation. https://doi.org/10.48550/ARXIV.2306.00600
- Romijnders, R., Asano, Y. M., Louizos, C., & Welling, M. (2023). No time to waste: practical statistical contact tracing with few low-bit messages. Proceedings of Machine Learning Research, 206, 7943-7960. https://proceedings.mlr.press/v206/romijnders23a.html [details]
2022
- Forre, P., Hoogeboom, E., Jaini, P., Nielsen, D., & Welling, M. (2022). Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), 35th Conference on Neural Information Processing Systems (NeurIPS 2021) : online, 6-14 December 2021 (Vol. 15, pp. 12454-12465). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2021/hash/67d96d458abdef21792e6d8e590244e7-Abstract.html [details]
- Kadambi, S., Behboodi, A., Soriaga, J. B., Welling, M., Amiri, R., Yerramalli, S., & Yoo, T. (2022). Neural RF SLAM for unsupervised positioning and mapping with channel state information. In ICC 2022 - IEEE International Conference on Communications: Seoul, South Korea, 16-20 May 2022 (pp. 3238-3244). IEEE. https://doi.org/10.1109/ICC45855.2022.9838367 [details]
- Keller, T. A., & Welling, M. (2022). Topographic VAEs learn Equivariant Capsules. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), 35th Conference on Neural Information Processing Systems (NeurIPS 2021) : online, 6-14 December 2021 (Vol. 34, pp. 28585-28597). (Advances in Neural Information Processing Systems; Vol. 34). Neural Information Processing Systems Foundation. https://doi.org/10.48550/arXiv.2109.01394 [details]
- Kool, W., van Hoof, H., Gromicho, J., & Welling, M. (2022). Deep Policy Dynamic Programming for Vehicle Routing Problems. In P. Schaus (Ed.), Integration of Constraint Programming, Artificial Intelligence, and Operations Research: 19th International Conference, CPAIOR 2022, Los Angeles, CA, USA, June 20-23, 2022 : proceedings (pp. 190–213). (Lecture Notes in Computer Science; Vol. 13292). Springer. https://doi.org/10.48550/arXiv.2102.11756, https://doi.org/10.1007/978-3-031-08011-1_14 [details]
- Löwe, S., Lippe, P., Rudolph, M., & Welling, M. (2022). Complex-Valued Autoencoders for Object Discovery. Transactions on Machine Learning Research, 2022, Article 428. https://openreview.net/forum?id=1PfcmFTXoa [details]
- van der Pol, E., van Hoof, H., Oliehoek, F., & Welling, M. (2022). Multi-Agent MDP Homomorphic Networks. In Proceedings of the International Conference on Learning Representations OpenReview. https://doi.org/10.48550/arXiv.2110.04495
2021
- Bakker, T., Van Hoof, H., & Welling, M. (2021). Experimental design for MRI by greedy policy search. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 23, pp. 18954-18966). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/daed210307f1dbc6f1dd9551408d999f-Abstract.html [details]
- COVID-PREDICT-werkgroep (2021). Klinisch beloop van covid-19 in Nederland: Een overzicht van 2607 ziekenhuispatiënten uit de eerste golf. Nederlands Tijdschrift voor Geneeskunde, 165(3), Article D5085. https://www.ntvg.nl/artikelen/klinisch-beloop-van-covid-19-nederland [details]
- De Haan, P., Cohen, T. S., & Welling, M. (2021). Natural Graph Networks. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 5, pp. 3636-3646). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/2517756c5a9be6ac007fe9bb7fb92611-Abstract.html [details]
- Fuchs, F., Worrall, D., Fischer, V., & Welling, M. (2021). SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 3, pp. 1970-1981). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/15231a7ce4ba789d13b722cc5c955834-Abstract.html [details]
- Hoogeboom, E., Garcia Satorras, V., Tomczak, J., & Welling, M. (2021). The Convolution Exponential and Generalized Sylvester Flows. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 22, pp. 18249-18248). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/d3f06eef2ffac7faadbe3055a70682ac-Abstract.html [details]
- Hu, S., Fridgeirsson, E. A., van Wingen, G., & Welling, M. (2021). Transformer-Based Deep Survival Analysis. Proceedings of Machine Learning Research, 146, 132-148. https://proceedings.mlr.press/v146/hu21a.html [details]
- Hu, S., Pezzotti, N., & Welling, M. (2021). Learning to Predict Error for MRI Reconstruction. In M. de Bruijne, P. C. Cattin, S. Cotin, N. Padoy, S. Speidel, Y. Zheng, & C. Essert (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021 : proceedings (Vol. III, pp. 604-613). (Lecture Notes in Computer Science; Vol. 12903). Springer. https://doi.org/10.1007/978-3-030-87199-4_57 [details]
- Keller, T. A., & Welling, M. (2021). Predictive Coding with Topographic Variational Autoencoders. In 2021 IEEE/CVF International Conference on Computer Vision Workshops: proceedings : ICCVW 2021 : 11-17 October 2021, virtual event (pp. 1086-1091). IEEE Computer Society. https://doi.org/10.1109/ICCVW54120.2021.00127 [details]
- Nielsen, D., Jaini, P., Hoogeboom, E., Winther, O., & Welling, M. (2021). SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 16, pp. 12685-12696). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/9578a63fbe545bd82cc5bbe749636af1-Abstract.html [details]
- Ottenhoff, M. C., Ramos, L. A., Potters, W., Hu, S., Thomas, R., Elbers, P., Welling, M., Simsek, S., Wiersinga, W. J., van Wingen, G. A., & The Dutch COVID-PREDICT research group (2021). Predicting mortality of individual patients with COVID-19: a multicentre Dutch cohort. BMJ Open, 11(7), Article e047347. https://doi.org/10.1136/bmjopen-2020-047347 [details]
- Van Der Pol, E., Worrall, D., Van Hoof, H., Oliehoek, F., & Welling, M. (2021). MDP homomorphic networks: Group symmetries in reinforcement learning. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), 34th Concerence on Neural Information Processing Systems (NeurIPS 2020): online, 6-12 December 2020 (Vol. 6, pp. 4199-4210). (Advances in Neural Information Processing Systems; Vol. 33). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2020/hash/2be5f9c2e3620eb73c2972d7552b6cb5-Abstract.html [details]
2020
- Akata, Z., Balliet, D., de Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H., Hung, H., Jonker, C., Monz, C., Neerincx, M., Oliehoek, F., Prakken, H., Schlobach, S., van der Gaag, L., van Harmelen, F., ... Welling, M. (2020). A Research Agenda for Hybrid Intelligence: Augmenting Human Intellect With Collaborative, Adaptive, Responsible, and Explainable Artificial Intelligence. Computer, 53(8), 18-28. https://doi.org/10.1109/MC.2020.2996587 [details]
- Hoogeboom, E., Peters, J. W. T., van den Berg, R., & Welling, M. (2020). Integer Discrete Flows and Lossless Compression. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada, 8-14 December 2019 (Vol. 16, pp. 12114-12124). (Advances in Neural Information Processing Systems; Vol. 32). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2019/hash/9e9a30b74c49d07d8150c8c83b1ccf07-Abstract.html [details]
- Kipf, T. N., van der Pol, E. E., & Welling, M. (2020). Contrastive Learning of Structured World Models. In International Conference on Learning Representations https://openreview.net/forum?id=H1gax6VtDB
- Kool, W., van Hoof, H., & Welling, M. (2020). Ancestral Gumbel-Top-k Sampling for Sampling Without Replacement. Journal of Machine Learning Research, 21, Article 47. https://jmlr.csail.mit.edu/papers/v21/19-985.html [details]
- Kool, W., van Hoof, H., & Welling, M. (2020). Estimating Gradients for Discrete Random Variables by Sampling without Replacement. In International Conference on Learning Representations
- Oh, C., Tomczak, J. M., Gavves, E., & Welling, M. (2020). Combinatorial Bayesian Optimization using the Graph Cartesian Product. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada, 8-14 December 2019 (Vol. 4, pp. 2891-2901). (Advances in Neural Information Processing Systems; Vol. 32). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2019/hash/2cb6b10338a7fc4117a80da24b582060-Abstract.html [details]
- Shang, W., van der Wal, D., van Hoof, H., & Welling, M. (2020). Stochastic Activation Actor Critic Methods. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019 : proceedings (Vol. III, pp. 103-117). (Lecture Notes in Computer Science; Vol. 11908), (Lecture Notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-46133-1_7 [details]
- Worrall, D., & Welling, M. (2020). Deep Scale-spaces: Equivariance Over Scale. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems (NeurIPS 2019): Vancouver, Canada, 8-14 December 2019 (Vol. 10, pp. 7334-7346). (Advances in Neural Information Processing Systems; Vol. 32). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2019/hash/f04cd7399b2b0128970efb6d20b5c551-Abstract.html [details]
- van der Pol, E., Kipf, T., Oliehoek, F. A., & Welling, M. (2020). Plannable Approximations to MDP Homomorphisms: Equivariance under Actions. In AAMAS'20: proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems : May 9-13, 2020, Auckland, New Zealand (pp. 1431–1439). International Foundation for Autonomous Agents and Multiagent Systems. https://dl.acm.org/doi/10.5555/3398761.3398926 [details]
2019
- Atanov, A., Ashukha, A., Struminsky, K., Vetrov, D., & Welling, M. (2019). The Deep Weight Prior. In ICLR 2019: International Conference on Learning Representations : New Orleans, Louisiana, United States, May 6-May 9, 2019 OpenReview. https://arxiv.org/abs/1810.06943 [details]
- Bertone, G., Deisenroth, M. P., Kim, J. S., Liem, S., Ruiz de Austri, R., & Welling, M. (2019). Accelerating the BSM interpretation of LHC data with machine learning. Physics of the Dark Universe, 24, Article 100293. https://doi.org/10.1016/j.dark.2019.100293 [details]
- Cohen, T. S., Weiler, M., Kicanaoglu, B., & Welling, M. (2019). Gauge Equivariant Convolutional Networks and the Icosahedral CNN. Proceedings of Machine Learning Research, 97, 1321-1330. http://proceedings.mlr.press/v97/cohen19d.html [details]
- Hoogeboom, E., van den Berg, R., & Welling, M. (2019). Emerging Convolutions for Generative Normalizing Flows. Proceedings of Machine Learning Research, 97, 2771-2780. http://proceedings.mlr.press/v97/hoogeboom19a.html [details]
- Hu, S., Worrall, D., Knegt, S., Veeling, B., Huisman, H., & Welling, M. (2019). Supervised Uncertainty Quantification for Segmentation with Multiple Annotations. In D. Shen, T. Liu, T. M. Peters, L. H. Staib, C. Essert, S. Zhou, P-T. Yap, & A. Khan (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019 : proceedings (Vol. 2, pp. 137-145). (Lecture Notes in Computer Science; Vol. 11765). Springer. https://doi.org/10.1007/978-3-030-32245-8_16 [details]
- Kool, W., van Hoof, H., & Welling, M. (2019). Attention, learn to solve routing problems! In ICLR 2019: International Conference on Learning Representations : New Orleans, Louisiana, United States, May 6-May 9, 2019 OpenReview. https://arxiv.org/abs/1803.08475 [details]
- Kool, W., van Hoof, H., & Welling, M. (2019). Buy 4 REINFORCE Samples, Get a Baseline for Free! In Deep RL Meets Structured Prediction Workshop at ICLR https://openreview.net/forum?id=r1lgTGL5DE
- Kool, W., van Hoof, H., & Welling, M. (2019). Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement. Proceedings of Machine Learning Research, 97, 3499-3508. http://proceedings.mlr.press/v97/kool19a.html [details]
- Louizos, C., Reisser, M., Blankevoort, T., Gavves, E., & Welling, M. (2019). Relaxed Quantization for Discretized Neural Networks. In ICLR 2019: International Conference on Learning Representations : New Orleans, Louisiana, United States, May 6-May 9, 2019 OpenReview. https://openreview.net/forum?id=HkxjYoCqKX [details]
- O'Connor, P., Gavves, E., & Welling, M. (2019). Initialized Equilibrium Propagation for Backprop-Free Training. In ICLR 2019: International Conference on Learning Representations : New Orleans, Louisiana, United States, May 6-May 9, 2019 OpenReview. https://openreview.net/forum?id=B1GMDsR5tm [details]
- O'Connor, P., Gavves, E., & Welling, M. (2019). Training a Spiking Neural Network with Equilibrium Propagation. Proceedings of Machine Learning Research, 89, 1516-1523. http://proceedings.mlr.press/v89/o-connor19a.html [details]
- Patrini, G., van den Berg, R., Forré, P., Carioni, M., Bhargav, S., Welling, M., Genewein, T., & Nielsen, F. (2019). Sinkhorn AutoEncoders. In A. Globerson, & R. Silva (Eds.), Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence: UAI 2019, Tel Aviv, Israel, July 22-25, 2019 Article 253 AUAI Press. https://arxiv.org/abs/1810.01118 [details]
- Weiler, M., Boomsma, W., Geiger, M., Welling, M., & Cohen, T. (2019). 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), 32nd Conference on Neural Information Processing Systems 2018 : Montreal, Canada, 3-8 December 2018 (Vol. 15, pp. 10381-10392). (Advances in Neural Information Processing Systems; Vol. 31). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper/2018/hash/488e4104520c6aab692863cc1dba45af-Abstract.html [details]
2018
- Cohen, T. S., Geiger, M., Khler, J., & Welling, M. (2018). Spherical CNNs. In International Conference for Learning Representations
- Hoogeboom, E., Peters, J. W. T., Cohen, T. S., & Welling, M. (2018). HexaConv. In International Conference for Learning Representations
- Ilse, M., Tomczak, J. M., & Welling, M. (2018). Attention-based Deep Multiple Instance Learning. Proceedings of Machine Learning Research, 80, 2127-2136. http://proceedings.mlr.press/v80/ilse18a.html [details]
- Kipf, T., Fetaya, E., Wang, K.-C., Welling, M., & Zemel, R. (2018). Neural Relational Inference for Interacting Systems. Proceedings of Machine Learning Research, 80, 2688-2697. http://proceedings.mlr.press/v80/kipf18a.html [details]
- Louizos, C., Shalit, U., Mooij, J., Sontag, D., Zemel, R., & Welling, M. (2018). Causal Effect Inference with Deep Latent-Variable Models. In U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, R. Fergus, S. V. N. Vishwanathan, & R. Garnett (Eds.), 31st Conference on Advances in Neural Information Processing Systems (NIPS 2017): Long Beach, California, USA, 4-9 December 2017 (Vol. 10, pp. 6447-6457). (Advances in Neural Information Processing Systems; Vol. 30). Neural Information Processing Systems. https://papers.nips.cc/paper/2017/file/94b5bde6de888ddf9cde6748ad2523d1-Paper.pdf [details]
- Louizos, C., Ullrich, K., & Welling, M. (2018). Bayesian Compression for Deep Learning. In U. von Luxburg, I. Guyon, S. Bengio, H. Wallach, R. Fergus, S. V. N. Vishwanathan, & R. Garnett (Eds.), 31st Conference on Advances in Neural Information Processing Systems (NIPS 2017): Long Beach, California, USA, 4-9 December 2017 (Vol. 5, pp. 3289-3299). (Advances in Neural Information Processing Systems; Vol. 30). Neural Information Processing Systems. https://papers.nips.cc/paper/6921-bayesian-compression-for-deep-learning [details]
- Louizos, C., Welling, M., & Kingma, D. P. (2018). Learning Sparse Neural Networks through L0 Regularization. In International Conference for Learning Representations
- O'Connor, P. E., Gavves, E., & Welling, M. (2018). Initialized Equilibrium Propagation for Backprop-Free Training. In International Conference on Machine Learning: Workshop on Credit Assignment in Deep Learning and Deep Reinforcement Learning
- O'Connor, P. E., Gavves, E., & Welling, M. (2018). Temporally Efficient Deep Learning with Spikes. In International Conference on Learning Representations OpenReview. https://openreview.net/forum?id=HkZy-bW0-¬eId=HkZy-bW0-
- Oh, C., Gavves, E., & Welling, M. (2018). BOCK: Bayesian Optimization with Cylindrical Kernels. Proceedings of Machine Learning Research, 80, 3868-3877. http://proceedings.mlr.press/v80/oh18a.html [details]
- Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., & Welling, M. (2018). Modeling Relational Data with Graph Convolutional Networks. In A. Gangemi, R. Navigli, M.-E. Vidal, P. Hitzler, R. Troncy, L. Hollink, A. Tordai, & M. Alam (Eds.), The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018 : proceedings (pp. 593-607). (Lecture Notes in Computer Science; Vol. 10843). Springer. https://doi.org/10.1007/978-3-319-93417-4_38 [details]
- Tomczak, J. M., & Welling, M. (2018). VAE with a VampPrior. Proceedings of Machine Learning Research, 84, 1214-1223. https://arxiv.org/abs/1705.07120 [details]
- van den Berg, R., Hasenclever, L., Tomczak, J. M., & Welling, M. (2018). Sylvester Normalizing Flows for Variational Inference. In A. Globerson, & R. Silva (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Fourth Concerence (2018) : August 6-10, 2018, Monterey, California, USA (pp. 393-402). AUAI Press. http://auai.org/uai2018/proceedings/papers/156.pdf [details]
2017
- Adel, T., Cohen, T., Caan, M., Welling, M., AGEhIV Study Group, & Alzheimer's Disease Neuroimaging Initiative (2017). 3D scattering transforms for disease classification in neuroimaging. NeuroImage: Clinical, 14, 506-517. https://doi.org/10.1016/j.nicl.2017.02.004 [details]
- Cohen, T. S., Geiger, M., & Welling, M. (2017). Convolutional Networks for Spherical Signals. In NIPS Workshops NIPS.
- Eck, A., Zintgraf, L. M., de Groot, E. F. J., de Meij, T. G. J., Cohen, T. S., Savelkoul, P. H. M., Welling, M., & Budding, A. E. (2017). Interpretation of microbiota-based diagnostics by explaining individual classifier decisions. BMC Bioinformatics, 18, Article 441. https://doi.org/10.1186/s12859-017-1843-1 [details]
- Eck, A., de Groot, E. F. J., de Meij, T. G. J., Welling, M., Savelkoul, P. H. M., & Budding, A. E. (2017). Robust Microbiota-Based Diagnostics for Inflammatory Bowel Disease. Journal of Clinical Microbiology, 55(6), 1720-1732. https://doi.org/10.1128/JCM.00162-17 [details]
- Kingma, D., Salimans, T., Josefowicz, R., Chen, X., Sutskever, I., & Welling, M. (2017). Improving Variational Autoencoders with Inverse Autoregressive Flow. In D. D. Lee, U. von Luxburg, R. Garnett, M. Sugiyama, & I. Guyon (Eds.), 30th Annual Conference on Neural Information Processing Systems 2016: Barcelona, Spain, 5-10 December 2016 (Vol. 7, pp. 4743-4751). (Advances in Neural Information Processing Systems; Vol. 29). Curran Associates. https://arxiv.org/abs/1606.04934 [details]
- Louizos, C., & Welling, M. (2017). Multiplicative Normalizing Flows for Variational Bayesian Neural Networks. Proceedings of Machine Learning Research, 70, 2218-2227. http://proceedings.mlr.press/v70/louizos17a.html [details]
- Park, M., Foulds, J., Chaudhuri, K., & Welling, M. (2017). DP-EM: Differentially Private Expectation Maximization. Proceedings of Machine Learning Research, 54, 896-904. http://proceedings.mlr.press/v54/park17c.html [details]
- Tomczak, J. M., & Welling, M. (2017). Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow. In Benelearn 2017 Benelearn. https://arxiv.org/pdf/1706.02326.pdf
2016
- Chen, Y., & Welling, M. (2016). Herding as a Learning System with Edge-of-Chaos Dynamics. In T. Hazan, G. Papandreou, & D. Tarlow (Eds.), Perturbations, Optimization, and Statistics (pp. 73-125). (Neural Information Processing series). The MIT Press. https://doi.org/10.7551/mitpress/10761.003.0005 [details]
- Chen, Y., Bornn, L., de Freitas, N., Eskelin, M., Fang, J., & Welling, M. (2016). Herded Gibbs Sampling. Journal of Machine Learning Research, 17, Article 10. http://www.jmlr.org/papers/v17/chen16a.html [details]
- Cohen, T. S., & Welling, M. (2016). Group Equivariant Convolutional Networks. JMLR Workshop and Conference Proceedings, 48, 2990-2999. http://proceedings.mlr.press/v48/cohenc16.html [details]
- El-Helw, I., Hofman, R., Li, W., Ahn, S., Welling, M., & Bal, H. (2016). Scalable Overlapping Community Detection. In 2016 IEEE 30th International Parallel and Distributed Processing Symposium Workshops : IPDPSW 2016: proceedings : 23-27 May 2016, Chicago, Illinois (pp. 1463-1472). IEEE Computer Society. https://doi.org/10.1109/IPDPSW.2016.165 [details]
- Foulds, J., Geumlek, J., Welling, M., & Chaudhuri, K. R. (2016). On the Theory and Practice of Privacy Preserving Data Analysis. In A. Ihler, & D. Janzing (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Thirty-Second Conference (2016) : June 25-29, 2016, Jersey City, New Jersey, USA (pp. 192-201). Article 45 AUAI Press. http://www.auai.org/uai2016/proceedings/papers/45.pdf [details]
- Korattikara, A., Chen, Y., & Welling, M. (2016). Sequential Tests for Large Scale Learning. Neural Computation, 28(1), 45-70. https://doi.org/10.1162/NECO_a_00226 [details]
- Li, W., Ahn, S., & Welling, M. (2016). Scalable MCMC for Mixed Membership Stochastic Blockmodels. JMLR Workshop and Conference Proceedings, 51, 723-731. http://jmlr.org/proceedings/papers/v51/li16d.html [details]
- Louizos, C., & Welling, M. (2016). Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors. JMLR Workshop and Conference Proceedings, 48, 1708-1716. http://proceedings.mlr.press/v48/louizos16.html [details]
- Louizos, C., Swersky, K., Li, Y., Welling, M., & Zemel, R. (2016). The Variational Fair Autoencoder. In ICLR 2016: International Conference on Learning Representations: May 2-4, 2016, San Juan, Puerto Rico. Accepted papers (Conference Track) Computational and Biological Learning Society. https://arxiv.org/abs/1511.00830 [details]
- Park, M. J., & Welling, M. (2016). A note on Privacy Preserving Iteratively Reweighted Least Squares. In ICML Workshop on Privacy & Machine Learning https://arxiv.org/abs/1605.07511
- Park, M., Foulds, J., Chaudhuri, K., & Welling, M. (2016). Private Topic Modeling. In Private Multi-Party Machine Learning: NIPS 2016 workshop : Barcelona, December 9 : PMPML'16 NIPS. https://arxiv.org/abs/1609.04120 [details]
- Welling, M. (2016). Marrying Graphical Models with Deep Learning. ERCIM News, 107, 20-21. https://ercim-news.ercim.eu/en107 [details]
2015
- Ahn, S., Korattikara, A., Liu, N., Rajan, S., & Welling, M. (2015). Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC. In KDD'15: proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: August 10-13, 2015, Sydney, Australia (pp. 9-18). Association for Computing Machinery. https://doi.org/10.1145/2783258.2783373 [details]
- Chiang, M., Cinquin, A., Paz, A., Meeds, E., Price, C. A., Welling, M., & Cinquin, O. (2015). Control of Caenorhabditis elegans germ-line stem-cell cycling speed meets requirements of design to minimize mutation accumulation. BMC Biology, 13, Article 51. https://doi.org/10.1186/s12915-015-0148-y [details]
- Cohen, T. S., & Welling, M. (2015). Harmonic Exponential Families on Manifolds. JMLR Workshop and Conference Proceedings, 37, 1757-1765. http://jmlr.org/proceedings/papers/v37/cohenb15.html [details]
- Cohen, T. S., & Welling, M. (2015). Transformation Properties of Learned Visual Representations. In ICLR 2015: accepted papers - Main Conference - Poster Presentations ArXiv. http://arxiv.org/abs/1412.7659 [details]
- Kingma, D. P., Rezende, D. J., Mohamed, S., & Welling, M. (2015). Semi-supervised Learning with Deep Generative Models. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), 28th Annual Conference on Neural Information Processing Systems 2014: December 8-13, 2014, Montreal, Canada (Vol. 4, pp. 3581-3589). (Advances in Neural Information Processing Systems; Vol. 27). Curran. http://papers.nips.cc/paper/5352-semi-supervised-learning-with-deep-generative-models [details]
- Kingma, D. P., Salimans, T., & Welling, M. (2015). Variational Dropout and the Local Reparameterization Trick. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, & R. Garnett (Eds.), 29th Annual Conference on Neural Information Processing Systems 2015: Montreal, Canada, 7-12 December 2015 (Vol. 3, pp. 2575-2583). (Advances in Neural Information Processing Systems; Vol. 28). Curran Associates. http://papers.nips.cc/paper/5666-variational-dropout-and-the-local-reparameterization-trick [details]
- Korattikara, A., Rathod, V., Murphy, K., & Welling, M. (2015). Bayesian Dark Knowledge. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, & R. Garnett (Eds.), 29th Annual Conference on Neural Information Processing Systems 2015: Montreal, Canada, 7-12 December 2015 (Vol. 4, pp. 3438-3446). (Advances in Neural Information Processing Systems; Vol. 28). Curran Associates. http://papers.nips.cc/paper/5965-bayesian-dark-knowledge [details]
- Meeds, E., & Welling, M. (2015). Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, & R. Garnett (Eds.), 29th Annual Conference on Neural Information Processing Systems 2015: Montreal, Canada, 7-12 December 2015 (Vol. 3, pp. 2080-2088). (Advances in Neural Information Processing Systems; Vol. 28). Curran Associates. http://papers.nips.cc/paper/5881-optimization-monte-carlo-efficient-and-embarrassingly-parallel-likelihood-free-inference [details]
- Meeds, E., Chiang, M., Lee, M., Cinquin, O., Lowengrub, J., & Welling, M. (2015). POPE: Post Optimization Posterior Evaluation of Likelihood Free Models. BMC Bioinformatics, 16, Article 264. https://doi.org/10.1186/s12859-015-0658-1 [details]
- Meeds, E., Hendriks, R., Al Faraby, S., Bruntink, M., & Welling, M. (2015). MLitB: Machine Learning in the Browser. PeerJ Computer Science, 1, Article e11. https://doi.org/10.7717/peerj-cs.11 [details]
- Meeds, E., Leenders, R., & Welling, M. (2015). Hamiltonian ABC. In M. Meila, & T. Heskes (Eds.), Uncertainty in Artificial Intelligence: proceedings of the thirty-first conference (2015): July 12-16, Amsterdam, Netherlands (pp. 582-591). AUAI Press. http://auai.org/uai2015/proceedings/papers/266.pdf [details]
- Salimans, T., Kingma, D. P., & Welling, M. (2015). Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. JMLR Workshop and Conference Proceedings, 37, 1218-1226. http://jmlr.org/proceedings/papers/v37/salimans15.html [details]
2014
- Ahn, S., Shahbaba, B., & Welling, M. (2014). Distributed Stochastic Gradient MCMC. JMLR Workshop and Conference Proceedings, 32, 1044-1052. http://jmlr.org/proceedings/papers/v32/ahn14.html [details]
- Chen, Y., Gelfand, A. E., & Welling, M. (2014). Herding for Structured Prediction. In S. Nowozin, P. V. Gehler, J. Jancsary, & C. H. Lampert (Eds.), Advanced structured prediction (pp. 187-212). (Neural information processing series). The MIT Press. https://mitpress.mit.edu/books/advanced-structured-prediction [details]
- Cohen, T., & Welling, M. (2014). Learning the Irreducible Representations of Commutative Lie Groups. JMLR Workshop and Conference Proceedings, 32, 1755-1763. http://jmlr.org/proceedings/papers/v32/cohen14.html [details]
- DuBois, C., Korattikara, A., Welling, M., & Smyth, P. (2014). Approximate Slice Sampling for Bayesian Posterior Inference. JMLR Workshop and Conference Proceedings, 33, 185-193. http://jmlr.org/proceedings/papers/v33/dubois14.html [details]
- Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Conference proceedings: papers accepted to the International Conference on Learning Representations (ICLR) 2014 ArXiv. http://arxiv.org/abs/1312.6114 [details]
- Kingma, D. P., & Welling, M. (2014). Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets. JMLR Workshop and Conference Proceedings, 32, 1782-1790. http://jmlr.org/proceedings/papers/v32/kingma14.html [details]
- Korattikara, A., Chen, Y., & Welling, M. (2014). Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget. JMLR Workshop and Conference Proceedings, 32, 181-189. http://jmlr.org/proceedings/papers/v32/korattikara14.html [details]
- Meeds, E., & Welling, M. (2014). GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation. In N. Zhang, & J. Tian (Eds.), Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence: Quebec City, Quebec, Canada: July 23-27, 2014: UAI2014 (pp. 593-602). AUAI Press. http://auai.org//uai2014/proceedings/uai-2014-proceedings.pdf [details]
- Salimans, T., Kingma, D. P., & Welling, M. (2014). Markov Chain Monte Carlo and Variational Inference: Bridging the Gap. In Accepted papers: Advances in Variational Inference: NIPS 2014 Workshop: 13 December 2014, Convention and Exhibition Center, Montreal, Canada NIPS Foundation. https://drive.google.com/file/d/0BwY-r_90KHY4d3ZTNDJpY3FYRS1rVEVVb3lUQzMzdk01Q2VV/view?pli=1 [details]
2013
- Ahn, S., Chen, Y., & Welling, M. (2013). Distributed and Adaptive Darting Monte Carlo through Regenerations. JMLR Workshop and Conference Proceedings, 31, 108-116. http://jmlr.org/proceedings/papers/v31/ahn13a.html [details]
- Bornn, L., Chen, Y., de Freitas, N., Eskelin, M., Fang, J., & Welling, M. (2013). Herded Gibbs Sampling. In International Conference on Learning Representation 2013 ArXiv. http://arxiv.org/abs/1301.4168 [details]
- Boyles, L., & Welling, M. (2013). The time-marginalized coalescent prior for hierarchical clustering. In P. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, & K. Q. Weinberger (Eds.), 26th Annual Conference on Neural Information Processing Systems 2012: December 3-6, 2012, Lake Tahoe, Nevada, USA (Vol. 4, pp. 2969-2977). (Advances in Neural Information Processing Systems; Vol. 25). Curran Associates. https://papers.nips.cc/paper/4786-the-time-marginalized-coalescent-prior-for-hierarchical-clustering [details]
- Chen, Y., & Welling, M. (2013). Evidence Estimation for Bayesian Partially Observed MRFs. JMLR Workshop and Conference Proceedings, 31, 178-186. http://jmlr.org/proceedings/papers/v31/chen13c.html [details]
- Foulds, J., Boyles, L., DuBois, C., Smyth, P., & Welling, M. (2013). Stochastic Collapsed Variational Bayesian Inference for Latent Dirichlet Allocation. In I. S. Dhillon, Y. Koren, R. Ghani, T. E. Senator, P. Bradley, R. Parekh, J. He, R. L. Grossman, & R. Uthurusamy (Eds.), KDD '13: the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: August 11-14, 2013, Chicago, Illinois, USA (pp. 446-454). ACM. https://doi.org/10.1145/2487575.2487697 [details]
- Korattikara, A., Chen, Y., & Welling, M. (2013). Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget. In 2013 JSM proceedings: papers presented at the Joint Statistical Meetings, Montréal, Québec, Canada, August 3-8, 2013, and other ASA-sponsored conferences [cd-rom] (pp. 236-250). American Statistical Association. https://www.amstat.org/meetings/jsm/2013/proceedings.cfm [details]
- Welinder, P., Welling, M., & Perona, P. (2013). A Lazy Man's Approach to Benchmarking: Semisupervised Classifier Evaluation and Recalibration. In Proceedings: 2013 IEEE Conference on Computer Vision and Pattern Recognition: CVPR 2013 : 23-28 June 2013, Portland, Oregon, USA (pp. 3262-3269). IEEE Computer Society, Conference Publishing Services. https://doi.org/10.1109/CVPR.2013.419 [details]
2012
- Ahn, S., Korattikara, A., & Welling, M. (2012). Bayesian posterior sampling via stochastic gradient Fisher scoring. In J. Langford, & J. Pineau (Eds.), Proceedings of Twenty-Ninth International Conference Machine Learning. - Vol. 2 (pp. 1591-1598). International Machine Learning Society. http://icml.cc/2012/papers/782.pdf [details]
- Chen, Y., & Welling, M. (2012). Bayesian structure learning for Markov Random Fields with a spike and slab prior. In N. de Freitas, & K. Murphy (Eds.), Uncertainty in Artificial Intelligence: proceedings of the Twenty-Eight conference (2012): August 15-17, 2012, Catalina Island, CA (pp. 174-184). AUAI Press. http://www.auai.org/uai2012/proceedings.pdf [details]
- Gelfand, A. E., & Welling, M. (2012). Generalized belief propagation on tree robust structured region graphs. In K. Murphy, & N. de Freitas (Eds.), Uncertainty in Artificial: proceedings of the Twenty-Eight conference (2012): August 15-17, 2012 Catalina Island, CA (pp. 296-305). AUAI Press. http://www.auai.org/uai2012/proceedings.pdf [details]
2022
- Ilse, M., Forré, P., Welling, M., & Mooij, J. M. (2022). Combining Observational and Interventional Data through Causal ductions. (v2 ed.) ArXiv. https://doi.org/10.48550/arXiv.2103.04786 [details]
2021
- Garcia Satorras, V., Hoogeboom, E., & Welling, M. (2021). E(n) Equivariant Graph Neural Networks. Proceedings of Machine Learning Research, 139, 9323-9332. https://proceedings.mlr.press/v139/satorras21a.html [details]
- Keller, T. A., Peters, J. W. T., Jaini, P., Hoogeboom, E., Forré, P., & Welling, M. (2021). Self Normalizing Flows. Proceedings of Machine Learning Research, 139, 5378-5387. https://arxiv.org/abs/2011.07248 [details]
- Weiler, M., Forré, P., Verlinde, E., & Welling, M. (2021). Coordinate Independent Convolutional Networks: Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2106.06020 [details]
2019
- Winkler, C., Worrall, D., Hoogeboom, E., & Welling, M. (2019). Learning Likelihoods with Conditional Normalizing Flows. In ArXiV ArXiv. https://openreview.net/forum?id=rJg3zxBYwH
2014
- Burges, C. J. C., Bottou, L., Welling, M., Ghahramani, Z., & Weinberger, K. Q. (2014). 27th Annual Conference on Neural Information Processing Systems 2013: December 5-10, Lake Tahoe, Nevada, USA. (Advances in Neural Information Processing Systems; Vol. 26). Curran. http://papers.nips.cc/book/advances-in-neural-information-processing-systems-26-2013 [details]
- Meeds, E., Hendriks, R., al Faraby, S., Bruntink, M., & Welling, M. (2014). MLitB: Machine Learning in the Browser. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.1412.2432 [details]
2015
- Welling, M. (2015). Deep Learning maakt ons bestaan smarter en kwetsbaarder. Het Financieele Dagblad. http://fd.nl/fd-outlook/1120278/deep-learning-maakt-ons-bestaan-smarter-en-kwetsbaarder
- Welling, M. (2015). Flexibel werken en voor ieder een basisinkomen. Het Financieele Dagblad. http://fd.nl/fd-outlook/1115737/flexibel-werken-en-voor-ieder-een-basisinkomen
2014
- Welling, M. (2014). Big data hoeven privacy niet aan te tasten. Folia.
- Welling, M. (2014). Hoe ontrafelen we 'wat' en 'waar' in videobeelden? Computable. http://www.computable.nl/artikel/nieuws/development/5177512/1277180/15-miljoen-voor-studies-naar-drones-en-robots.html
- Welling, M. (2014). When saving all the data captured by the antennas is simply not an option. ASTRON Newsletter.
2013
- Welling, M. (2013). Mijn data mogen ze hebben hoor. NRC Opinie.
- Welling, M. (2013). Open access wel degelijk belangrijk in economie. NRC Opinie.
2021
- Keller, T. A., Gao, Q., & Welling, M. (2021). Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders. Paper presented at 3rd Workshop on Shared Visual Representations in Human and Machine Intelligence of the Neural Information Processing Systems conference. https://arxiv.org/abs/2110.13911
2018
- Selvan, R., Kipf, T., Welling, M., Pedersen, J. H., Petersen, J., & de Bruijne, M. (2018). Extraction of Airways using Graph Neural Networks. Poster session presented at Medical Imaging with Deep Learning, Abstract Track (MIDL 2018), .
2017
- Federici, M., Ullrich, K., & Welling, M. (2017). Improved Bayesian Compression. Paper presented at Bayesian Deep Learning Workshop NIPS 2017, Long Beach, United States. http://bayesiandeeplearning.org/2017/papers/16.pdf [details]
- Hasenclever, L., Tomczak, J. M., van den Berg, R., & Welling, M. (2017). Variational Inference with Orthogonal Normalizing Flows. Paper presented at Bayesian Deep Learning Workshop NIPS 2017, Long Beach, United States. http://bayesiandeeplearning.org/2017/papers/51.pdf [details]
- Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. Paper presented at 5th International Conference on Learning Representations, Toulon, France. http://arxiv.org/abs/1609.02907
- Tomczak, J. M., Ilse, M., & Welling, M. (2017). Deep Learning with Order-invariant Operator for Multi-instance Histopathology Classification. Abstract from Medical Imaging meets NIPS Workshop NIPS 2017, Long Beach, United States. https://doi.org/10.48550/arXiv.1712.00310 [details]
- Ullrich, K., Meeds, E. W. F., & Welling, M. (2017). Soft Weight-Sharing for Neural Network Compression. Paper presented at 5th International Conference on Learning Representations, Toulon, France. https://arxiv.org/pdf/1702.04008.pdf
2016
- Kipf, T. N., & Welling, M. (2016). Variational Graph Auto-Encoders. Paper presented at Bayesian Deep Learning Workshop NIPS 2016, Barcelona, Spain. https://doi.org/10.48550/arXiv.1611.07308 [details]
- Tomczak, J. M., & Welling, M. (2016). Improving Variational Auto-Encoders using Householder Flow. Paper presented at Bayesian Deep Learning Workshop NIPS 2016, Barcelona, Spain. https://arxiv.org/abs/1611.09630 [details]
2014
- Welling, M. (2014). Exploiting the Statistics of Learning and Inference. Paper presented at NIPS 2014 Workshop on "Probabilistic Models for Big Data". https://arxiv.org/abs/1402.7025
2013
- Meeds, E., & Welling, M. (2013). Inference in Stochastic Biological Systems using Gaussian Process Surrogate ABC. Poster session presented at 2013 NIPS Workshop on Machine Learning in Computational Biology, Lake Tahoe, NV. http://www.mlcb.org/previous/mlcb2014
Media appearance
- Welling, M. (20-10-2016). Contribution to magazine ICT & Health. Een pitbull waakt voor het laaghangend fruit.
- Welling, M. (01-09-2016). Column FD. Monthly Column in Financieel Dagblad.
- Welling, M. (31-05-2016). Interview BNR Radio [Radio]. Interview BNR Radio.
- Welling, M. (30-04-2016). En toen ging de computer zelf leren” (door Bennie Mols). Interview NRC.
- Welling, M. (23-01-2015). Lerende computer-neuronen [Print] De Ingenieur. Lerende computer-neuronen. https://www.deingenieur.nl/artikel/lerende-computerneuronen
- Welling, M. (10-01-2015). Een computer met een mensenbrein [Print] Parool. Een computer met een mensenbrein.
2025
- Federici, M. (2025). Information theory for representation learning. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Ruhe, D. J. J. (2025). Structured deep learning with applications in astrophysics. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- de Haan, P. (2025). Machine learning with generalised symmetries. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
2024
- Bakker, T. B. (2024). Learning adaptive sensing and active learning. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Esmaeili, B. (2024). Learning useful representations with variational autoencoders. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- García Satorras, V. (2024). Inductive biases for graph neural networks. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Löwe, S. (2024). Learning structured representations of objects and relations. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Miller, B. K. (2024). Machine learning for scientific simulation: Inference and generative models. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Veeling, B. S. (2024). Deep learning for medical data. [Thesis, fully internal, Universiteitsbibliotheek]. [details]
- Weiler, M. (2024). Equivariant and coordinate independent convolutional networks: A gauge field theory of neural networks. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Wöhlke, J. G. (2024). Reinforcement learning and planning for autonomous agent navigation: With a focus on sparse reward settings. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
2023
- Hoogeboom, E. (2023). Normalizing flows and diffusion models for discrete and geometric data. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Keller, T. A. (2023). Natural inductive biases for artificial intelligence. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Oh, C. (2023). Bayesian optimization on non-conventional search spaces. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Putzky, P. (2023). Amortized inference in inverse problems. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- van der Pol, E. (2023). Symmetry and structure in deep reinforcement learning. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
2022
- Bongers, S. R. (2022). Causal modeling & dynamical systems: A new perspective on feedback. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Hu, S. (2022). Uncertainty, robustness and safety in artificial intelligence, with applications in healthcare. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Ilse, M. (2022). Invariance in deep representations. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Kool, W. (2022). Learning and optimization in combinatorial spaces: With a focus on deep learning for vehicle routing. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Louizos, C. (2022). Probabilistic reasoning for uncertainty & compression in deep learning. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Wang, Q. (2022). Functional representation learning for uncertainty quantification and fast skill transfer. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
2021
- Blom, T. (2021). Causality and independence in systems of equations. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Cohen, T. S. (2021). Equivariant convolutional networks. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Shang, W. (2021). Crafting deep learning models for reinforcement learning and computer vision applications. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
2020
- Kipf, T. N. (2020). Deep learning with graph-structured representations. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- O'Connor, P. (2020). Biologically plausible deep learning: Should airplanes flap their wings? [Thesis, fully internal, Universiteit van Amsterdam]. [details]
- Ullrich, K. (2020). A coding perspective on deep latent variable models. [Thesis, fully internal, Universiteit van Amsterdam]. [details]
2016
- O'Connor, P., & Welling, M. (2016). Deep Spiking Networks. ArXiv. https://arxiv.org/abs/1602.08323v2 [details]
2018
- Veeling, B., Linmans, J., Winkens, J., Cohen, T. & Welling, M. (8-6-2018). PatchCamelyon (PCam). Zenodo. https://doi.org/10.5281/zenodo.1494286
This list of publications is extracted from the UvA-Current Research Information System. Questions? Ask the library or the Pure staff of your faculty / institute. Log in to Pure to edit your publications. Log in to Personal Page Publication Selection tool to manage the visibility of your publications on this list. -
Ancillary activities
-
Canadian Institute for Advanced Research
Advies, onderzoek -
Scymax
Beleggen -
Stichting Do & Well
Goed doel opgericht door mijn vrouw en mijzelf. -
Fermioniq
Advisor -
California Institute of Technology
Distinguished Visiting Associate
-
Canadian Institute for Advanced Research
Igashov, I., Stärk, H., Vignac, C., Schneuing, A., Satorras, V. G., Frossard, P., Welling, M., Bronstein, M., & Correia, B. (2024). Equivariant 3D-conditional diffusion model for molecular linker design. Nature Machine Intelligence, 6(4), 417–427.