Boris Flach bio photo

Boris Flach

associate professor




  1. Sixta, T., Cao, J., Seebach, J., Schnittler, H., & Flach, B. (2020). Coupling cell detection and tracking by temporal feedback. Machine Vision and Applications, 31(4).
  2. Shekhovtsov, A., Yanush, V., & Flach, B. (2020). Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems (Vol. 33, pp. 12884–12894). Curran Associates, Inc.


  1. Shekhovtsov, A., & Flach, B. (2019). Stochastic Normalizations as Bayesian Learning. In C. V. Jawahar, H. Li, G. Mori, & K. Schindler (Eds.), Computer Vision – ACCV 2018 (pp. 463–479). Springer International Publishing.
  2. Shekhovtsov, A., & Flach, B. (2019). Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers. International Conference on Learning Representations.


  1. Schlesinger, M. I., Flach, B., & Vodolazskiy, E. (2018). Finding a Given Number of Solutions to a System of Fuzzy Constraints. Cybernetics and Systems Analysis, 54(1), 60–74.


  1. Cao, J., Ehling, M., März, S., Seebach, J., Tarbashevich, K., Sixta, T., Pitulescu, M. E., Werner, A. C., Flach, B., Montanez, E., Raz, E., Adams, R. H., & Schnittler, H. (2017). Polarized actin and VE-cadherin dynamics regulate junctional remodelling and cell migration during sprouting angiogenesis. Nature Communications, 8(1), 2210–.


  1. Vodolazskii, E. V., Flach, B., & Schlesinger, M. I. (2014). Minimax Problems of Discrete Optimization Invariant under Majority Operators. Computational Mathematics and Mathematical Physics, 54(8), 1327–1336.
  2. Quincey, D. J., Bishop, M. P., Kääb, A., Berthier, E., Flach, B., Bolch, T., Buchroithner, M., Kamp, U., Khalsa, S. J. S., Toutin, T., Haritashya, U. K., Racoviteanu, A., Shroder, J. F., & Raup, B. H. (2014). Digital Terrain Modeling and Glacier Topographic Characterization. In J. S. Kargel, G. J. Leonard, M. P. Bishop, A. Kääb, & B. H. Raup (Eds.), Global Land Ice Measurements from Space (pp. 113–144). Springer.
  3. Flach, B., & Hlavac, V. (2014). Expectation Maximization Algorithm. In K. Ikeuchi (Ed.), Computer Vision: A Reference Guide (pp. 265–268). Springer.
  4. Flach, B. (2013). A Class of Random Fields on Complete Graphs with Tractable Partition Function. Pattern Analysis and Machine Intelligence, IEEE Transactions On, 35(9), 2304–2306.
  5. Flach, B., & Sixta, T. (2013). Unsupervised (parameter) learning for MRFs on bipartite graphs. Proceedings of the British Machine Vision Conference, 72.1–72.11.
  6. Flach, B., & Schlesinger, D. (2011). Modelling composite shapes by Gibbs Random Fields. CVPR 2011: Proceedings of the 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2177–2182.
  7. Steinborn, A., Taut, S., Brendler, V., Geipel, G., & Flach, B. (2008). TRLFS: Analysing Spectra with an Expectation-Maximization (EM) Algorithm. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 71(4), 1425–1432.
  8. Schlesinger, D., & Flach, B. (2006). Transforming an arbitrary MinSum problem into a binary one (TUD-FI06-01; Number TUD-FI06-01). Dresden University of Technology.
  9. Flach, B., & Schlesinger, D. (2004). Best Labeling Search for a Class of Higher Order Gibbs Models. Journ. f. Pattern Recognition and Image Analysis, 14(2), 249–254.
  10. Flach, B., Kask, E., Schlesinger, D., & Skulish, A. (2002). Unifying Registration and Segmentation for Multi-Sensor Images. In L. V. Gool (Ed.), Pattern Recognition (Vol. 2449, pp. 190–197). Springer Verlag.
  11. Schlesinger, M. I., & Flach, B. (2000). Some solvable subclasses of structural recognition problems. In T. Svoboda (Ed.), Czech Pattern Recognition Workshop 2000 (pp. 55–61).