TY - THES AB - Algorithms in computational 3-manifold topology typically take a triangulation as an input and return topological information about the underlying 3-manifold. However, extracting the desired information from a triangulation (e.g., evaluating an invariant) is often computationally very expensive. In recent years this complexity barrier has been successfully tackled in some cases by importing ideas from the theory of parameterized algorithms into the realm of 3-manifolds. Various computationally hard problems were shown to be efficiently solvable for input triangulations that are sufficiently “tree-like.” In this thesis we focus on the key combinatorial parameter in the above context: we consider the treewidth of a compact, orientable 3-manifold, i.e., the smallest treewidth of the dual graph of any triangulation thereof. By building on the work of Scharlemann–Thompson and Scharlemann–Schultens–Saito on generalized Heegaard splittings, and on the work of Jaco–Rubinstein on layered triangulations, we establish quantitative relations between the treewidth and classical topological invariants of a 3-manifold. In particular, among other results, we show that the treewidth of a closed, orientable, irreducible, non-Haken 3-manifold is always within a constant factor of its Heegaard genus. AU - Huszár, Kristóf ID - 8032 SN - 2663-337X TI - Combinatorial width parameters for 3-dimensional manifolds ER - TY - CONF AB - This paper presents a foundation for refining concurrent programs with structured control flow. The verification problem is decomposed into subproblems that aid interactive program development, proof reuse, and automation. The formalization in this paper is the basis of a new design and implementation of the Civl verifier. AU - Kragl, Bernhard AU - Qadeer, Shaz AU - Henzinger, Thomas A ID - 8195 SN - 0302-9743 T2 - Computer Aided Verification TI - Refinement for structured concurrent programs VL - 12224 ER - TY - CONF AB - Asynchronous programs are notoriously difficult to reason about because they spawn computation tasks which take effect asynchronously in a nondeterministic way. Devising inductive invariants for such programs requires understanding and stating complex relationships between an unbounded number of computation tasks in arbitrarily long executions. In this paper, we introduce inductive sequentialization, a new proof rule that sidesteps this complexity via a sequential reduction, a sequential program that captures every behavior of the original program up to reordering of coarse-grained commutative actions. A sequential reduction of a concurrent program is easy to reason about since it corresponds to a simple execution of the program in an idealized synchronous environment, where processes act in a fixed order and at the same speed. We have implemented and integrated our proof rule in the CIVL verifier, allowing us to provably derive fine-grained implementations of asynchronous programs. We have successfully applied our proof rule to a diverse set of message-passing protocols, including leader election protocols, two-phase commit, and Paxos. AU - Kragl, Bernhard AU - Enea, Constantin AU - Henzinger, Thomas A AU - Mutluergil, Suha Orhun AU - Qadeer, Shaz ID - 8012 SN - 9781450376136 T2 - Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation TI - Inductive sequentialization of asynchronous programs ER - TY - THES AB - During bacterial cell division, the tubulin-homolog FtsZ forms a ring-like structure at the center of the cell. This so-called Z-ring acts as a scaffold recruiting several division-related proteins to mid-cell and plays a key role in distributing proteins at the division site, a feature driven by the treadmilling motion of FtsZ filaments around the septum. What regulates the architecture, dynamics and stability of the Z-ring is still poorly understood, but FtsZ-associated proteins (Zaps) are known to play an important role. Advances in fluorescence microscopy and in vitro reconstitution experiments have helped to shed light into some of the dynamic properties of these complex systems, but methods that allow to collect and analyze large quantitative data sets of the underlying polymer dynamics are still missing. Here, using an in vitro reconstitution approach, we studied how different Zaps affect FtsZ filament dynamics and organization into large-scale patterns, giving special emphasis to the role of the well-conserved protein ZapA. For this purpose, we use high-resolution fluorescence microscopy combined with novel image analysis workfows to study pattern organization and polymerization dynamics of active filaments. We quantified the influence of Zaps on FtsZ on three diferent spatial scales: the large-scale organization of the membrane-bound filament network, the underlying polymerization dynamics and the behavior of single molecules. We found that ZapA cooperatively increases the spatial order of the filament network, binds only transiently to FtsZ filaments and has no effect on filament length and treadmilling velocity. Our data provides a model for how FtsZ-associated proteins can increase the precision and stability of the bacterial cell division machinery in a switch-like manner, without compromising filament dynamics. Furthermore, we believe that our automated quantitative methods can be used to analyze a large variety of dynamic cytoskeletal systems, using standard time-lapse movies of homogeneously labeled proteins obtained from experiments in vitro or even inside the living cell. AU - Dos Santos Caldas, Paulo R ID - 8358 SN - 2663-337X TI - Organization and dynamics of treadmilling filaments in cytoskeletal networks of FtsZ and its crosslinkers ER - TY - CONF AB - Even though Delaunay originally introduced his famous triangulations in the case of infinite point sets with translational periodicity, a software that computes such triangulations in the general case is not yet available, to the best of our knowledge. Combining and generalizing previous work, we present a practical algorithm for computing such triangulations. The algorithm has been implemented and experiments show that its performance is as good as the one of the CGAL package, which is restricted to cubic periodicity. AU - Osang, Georg F AU - Rouxel-Labbé, Mael AU - Teillaud, Monique ID - 8703 SN - 18688969 T2 - 28th Annual European Symposium on Algorithms TI - Generalizing CGAL periodic Delaunay triangulations VL - 173 ER - TY - CONF AB - We address the following question: How redundant is the parameterisation of ReLU networks? Specifically, we consider transformations of the weight space which leave the function implemented by the network intact. Two such transformations are known for feed-forward architectures: permutation of neurons within a layer, and positive scaling of all incoming weights of a neuron coupled with inverse scaling of its outgoing weights. In this work, we show for architectures with non-increasing widths that permutation and scaling are in fact the only function-preserving weight transformations. For any eligible architecture we give an explicit construction of a neural network such that any other network that implements the same function can be obtained from the original one by the application of permutations and rescaling. The proof relies on a geometric understanding of boundaries between linear regions of ReLU networks, and we hope the developed mathematical tools are of independent interest. AU - Bui Thi Mai, Phuong AU - Lampert, Christoph ID - 7481 T2 - 8th International Conference on Learning Representations TI - Functional vs. parametric equivalence of ReLU networks ER - TY - JOUR AB - We consider the Pekar functional on a ball in ℝ3. We prove uniqueness of minimizers, and a quadratic lower bound in terms of the distance to the minimizer. The latter follows from nondegeneracy of the Hessian at the minimum. AU - Feliciangeli, Dario AU - Seiringer, Robert ID - 9781 IS - 1 JF - SIAM Journal on Mathematical Analysis KW - Applied Mathematics KW - Computational Mathematics KW - Analysis SN - 0036-1410 TI - Uniqueness and nondegeneracy of minimizers of the Pekar functional on a ball VL - 52 ER - TY - JOUR AB - In the present work, we consider the evolution of two fluids separated by a sharp interface in the presence of surface tension—like, for example, the evolution of oil bubbles in water. Our main result is a weak–strong uniqueness principle for the corresponding free boundary problem for the incompressible Navier–Stokes equation: as long as a strong solution exists, any varifold solution must coincide with it. In particular, in the absence of physical singularities, the concept of varifold solutions—whose global in time existence has been shown by Abels (Interfaces Free Bound 9(1):31–65, 2007) for general initial data—does not introduce a mechanism for non-uniqueness. The key ingredient of our approach is the construction of a relative entropy functional capable of controlling the interface error. If the viscosities of the two fluids do not coincide, even for classical (strong) solutions the gradient of the velocity field becomes discontinuous at the interface, introducing the need for a careful additional adaption of the relative entropy. AU - Fischer, Julian L AU - Hensel, Sebastian ID - 7489 JF - Archive for Rational Mechanics and Analysis SN - 00039527 TI - Weak–strong uniqueness for the Navier–Stokes equation for two fluids with surface tension VL - 236 ER - TY - GEN AB - We prove that in the absence of topological changes, the notion of BV solutions to planar multiphase mean curvature flow does not allow for a mechanism for (unphysical) non-uniqueness. Our approach is based on the local structure of the energy landscape near a classical evolution by mean curvature. Mean curvature flow being the gradient flow of the surface energy functional, we develop a gradient-flow analogue of the notion of calibrations. Just like the existence of a calibration guarantees that one has reached a global minimum in the energy landscape, the existence of a "gradient flow calibration" ensures that the route of steepest descent in the energy landscape is unique and stable. AU - Fischer, Julian L AU - Hensel, Sebastian AU - Laux, Tim AU - Simon, Thilo ID - 10012 T2 - arXiv TI - The local structure of the energy landscape in multiphase mean curvature flow: weak-strong uniqueness and stability of evolutions ER - TY - JOUR AB - The superconducting circuit community has recently discovered the promising potential of superinductors. These circuit elements have a characteristic impedance exceeding the resistance quantum RQ ≈ 6.45 kΩ which leads to a suppression of ground state charge fluctuations. Applications include the realization of hardware protected qubits for fault tolerant quantum computing, improved coupling to small dipole moment objects and defining a new quantum metrology standard for the ampere. In this work we refute the widespread notion that superinductors can only be implemented based on kinetic inductance, i.e. using disordered superconductors or Josephson junction arrays. We present modeling, fabrication and characterization of 104 planar aluminum coil resonators with a characteristic impedance up to 30.9 kΩ at 5.6 GHz and a capacitance down to ≤ 1 fF, with lowloss and a power handling reaching 108 intra-cavity photons. Geometric superinductors are free of uncontrolled tunneling events and offer high reproducibility, linearity and the ability to couple magnetically - properties that significantly broaden the scope of future quantum circuits. AU - Peruzzo, Matilda AU - Trioni, Andrea AU - Hassani, Farid AU - Zemlicka, Martin AU - Fink, Johannes M ID - 8755 IS - 4 JF - Physical Review Applied TI - Surpassing the resistance quantum with a geometric superinductor VL - 14 ER - TY - JOUR AB - This paper deals with dynamical optimal transport metrics defined by spatial discretisation of the Benamou–Benamou formula for the Kantorovich metric . Such metrics appear naturally in discretisations of -gradient flow formulations for dissipative PDE. However, it has recently been shown that these metrics do not in general converge to , unless strong geometric constraints are imposed on the discrete mesh. In this paper we prove that, in a 1-dimensional periodic setting, discrete transport metrics converge to a limiting transport metric with a non-trivial effective mobility. This mobility depends sensitively on the geometry of the mesh and on the non-local mobility at the discrete level. Our result quantifies to what extent discrete transport can make use of microstructure in the mesh to reduce the cost of transport. AU - Gladbach, Peter AU - Kopfer, Eva AU - Maas, Jan AU - Portinale, Lorenzo ID - 7573 IS - 7 JF - Journal de Mathematiques Pures et Appliquees SN - 00217824 TI - Homogenisation of one-dimensional discrete optimal transport VL - 139 ER - TY - GEN AB - We consider finite-volume approximations of Fokker-Planck equations on bounded convex domains in R^d and study the corresponding gradient flow structures. We reprove the convergence of the discrete to continuous Fokker-Planck equation via the method of Evolutionary Γ-convergence, i.e., we pass to the limit at the level of the gradient flow structures, generalising the one-dimensional result obtained by Disser and Liero. The proof is of variational nature and relies on a Mosco convergence result for functionals in the discrete-to-continuum limit that is of independent interest. Our results apply to arbitrary regular meshes, even though the associated discrete transport distances may fail to converge to the Wasserstein distance in this generality. AU - Forkert, Dominik L AU - Maas, Jan AU - Portinale, Lorenzo ID - 10022 T2 - arXiv TI - Evolutionary Γ-convergence of entropic gradient flow structures for Fokker-Planck equations in multiple dimensions ER - TY - CONF AB - We study the problem of learning from multiple untrusted data sources, a scenario of increasing practical relevance given the recent emergence of crowdsourcing and collaborative learning paradigms. Specifically, we analyze the situation in which a learning system obtains datasets from multiple sources, some of which might be biased or even adversarially perturbed. It is known that in the single-source case, an adversary with the power to corrupt a fixed fraction of the training data can prevent PAC-learnability, that is, even in the limit of infinitely much training data, no learning system can approach the optimal test error. In this work we show that, surprisingly, the same is not true in the multi-source setting, where the adversary can arbitrarily corrupt a fixed fraction of the data sources. Our main results are a generalization bound that provides finite-sample guarantees for this learning setting, as well as corresponding lower bounds. Besides establishing PAC-learnability our results also show that in a cooperative learning setting sharing data with other parties has provable benefits, even if some participants are malicious. AU - Konstantinov, Nikola H AU - Frantar, Elias AU - Alistarh, Dan-Adrian AU - Lampert, Christoph ID - 8724 SN - 2640-3498 T2 - Proceedings of the 37th International Conference on Machine Learning TI - On the sample complexity of adversarial multi-source PAC learning VL - 119 ER - TY - JOUR AB - Determining the phase diagram of systems consisting of smaller subsystems 'connected' via a tunable coupling is a challenging task relevant for a variety of physical settings. A general question is whether new phases, not present in the uncoupled limit, may arise. We use machine learning and a suitable quasidistance between different points of the phase diagram to study layered spin models, in which the spin variables constituting each of the uncoupled systems (to which we refer as layers) are coupled to each other via an interlayer coupling. In such systems, in general, composite order parameters involving spins of different layers may emerge as a consequence of the interlayer coupling. We focus on the layered Ising and Ashkin–Teller models as a paradigmatic case study, determining their phase diagram via the application of a machine learning algorithm to the Monte Carlo data. Remarkably our technique is able to correctly characterize all the system phases also in the case of hidden order parameters, i.e. order parameters whose expression in terms of the microscopic configurations would require additional preprocessing of the data fed to the algorithm. We correctly retrieve the three known phases of the Ashkin–Teller model with ferromagnetic couplings, including the phase described by a composite order parameter. For the bilayer and trilayer Ising models the phases we find are only the ferromagnetic and the paramagnetic ones. Within the approach we introduce, owing to the construction of convolutional neural networks, naturally suitable for layered image-like data with arbitrary number of layers, no preprocessing of the Monte Carlo data is needed, also with regard to its spatial structure. The physical meaning of our results is discussed and compared with analytical data, where available. Yet, the method can be used without any a priori knowledge of the phases one seeks to find and can be applied to other models and structures. AU - Rzadkowski, Wojciech AU - Defenu, N AU - Chiacchiera, S AU - Trombettoni, A AU - Bighin, Giacomo ID - 8644 IS - 9 JF - New Journal of Physics SN - 13672630 TI - Detecting composite orders in layered models via machine learning VL - 22 ER - TY - JOUR AB - We consider the quantum mechanical many-body problem of a single impurity particle immersed in a weakly interacting Bose gas. The impurity interacts with the bosons via a two-body potential. We study the Hamiltonian of this system in the mean-field limit and rigorously show that, at low energies, the problem is well described by the Fröhlich polaron model. AU - Mysliwy, Krzysztof AU - Seiringer, Robert ID - 8705 IS - 12 JF - Annales Henri Poincare SN - 1424-0637 TI - Microscopic derivation of the Fröhlich Hamiltonian for the Bose polaron in the mean-field limit VL - 21 ER - TY - JOUR AB - Motivation: Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results: We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an autoencoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 90% and 78% cell-matching accuracy for each one of the samples, respectively. AU - Stark, Stefan G AU - Ficek, Joanna AU - Locatello, Francesco AU - Bonilla, Ximena AU - Chevrier, Stéphane AU - Singer, Franziska AU - Aebersold, Rudolf AU - Al-Quaddoomi, Faisal S AU - Albinus, Jonas AU - Alborelli, Ilaria AU - Andani, Sonali AU - Attinger, Per-Olof AU - Bacac, Marina AU - Baumhoer, Daniel AU - Beck-Schimmer, Beatrice AU - Beerenwinkel, Niko AU - Beisel, Christian AU - Bernasconi, Lara AU - Bertolini, Anne AU - Bodenmiller, Bernd AU - Bonilla, Ximena AU - Casanova, Ruben AU - Chevrier, Stéphane AU - Chicherova, Natalia AU - D'Costa, Maya AU - Danenberg, Esther AU - Davidson, Natalie AU - gan, Monica-Andreea Dră AU - Dummer, Reinhard AU - Engler, Stefanie AU - Erkens, Martin AU - Eschbach, Katja AU - Esposito, Cinzia AU - Fedier, André AU - Ferreira, Pedro AU - Ficek, Joanna AU - Frei, Anja L AU - Frey, Bruno AU - Goetze, Sandra AU - Grob, Linda AU - Gut, Gabriele AU - Günther, Detlef AU - Haberecker, Martina AU - Haeuptle, Pirmin AU - Heinzelmann-Schwarz, Viola AU - Herter, Sylvia AU - Holtackers, Rene AU - Huesser, Tamara AU - Irmisch, Anja AU - Jacob, Francis AU - Jacobs, Andrea AU - Jaeger, Tim M AU - Jahn, Katharina AU - James, Alva R AU - Jermann, Philip M AU - Kahles, André AU - Kahraman, Abdullah AU - Koelzer, Viktor H AU - Kuebler, Werner AU - Kuipers, Jack AU - Kunze, Christian P AU - Kurzeder, Christian AU - Lehmann, Kjong-Van AU - Levesque, Mitchell AU - Lugert, Sebastian AU - Maass, Gerd AU - Manz, Markus AU - Markolin, Philipp AU - Mena, Julien AU - Menzel, Ulrike AU - Metzler, Julian M AU - Miglino, Nicola AU - Milani, Emanuela S AU - Moch, Holger AU - Muenst, Simone AU - Murri, Riccardo AU - Ng, Charlotte KY AU - Nicolet, Stefan AU - Nowak, Marta AU - Pedrioli, Patrick GA AU - Pelkmans, Lucas AU - Piscuoglio, Salvatore AU - Prummer, Michael AU - Ritter, Mathilde AU - Rommel, Christian AU - Rosano-González, María L AU - Rätsch, Gunnar AU - Santacroce, Natascha AU - Castillo, Jacobo Sarabia del AU - Schlenker, Ramona AU - Schwalie, Petra C AU - Schwan, Severin AU - Schär, Tobias AU - Senti, Gabriela AU - Singer, Franziska AU - Sivapatham, Sujana AU - Snijder, Berend AU - Sobottka, Bettina AU - Sreedharan, Vipin T AU - Stark, Stefan AU - Stekhoven, Daniel J AU - Theocharides, Alexandre PA AU - Thomas, Tinu M AU - Tolnay, Markus AU - Tosevski, Vinko AU - Toussaint, Nora C AU - Tuncel, Mustafa A AU - Tusup, Marina AU - Drogen, Audrey Van AU - Vetter, Marcus AU - Vlajnic, Tatjana AU - Weber, Sandra AU - Weber, Walter P AU - Wegmann, Rebekka AU - Weller, Michael AU - Wendt, Fabian AU - Wey, Norbert AU - Wicki, Andreas AU - Wollscheid, Bernd AU - Yu, Shuqing AU - Ziegler, Johanna AU - Zimmermann, Marc AU - Zoche, Martin AU - Zuend, Gregor AU - Rätsch, Gunnar AU - Lehmann, Kjong-Van ID - 14125 IS - Supplement_2 JF - Bioinformatics KW - Computational Mathematics KW - Computational Theory and Mathematics KW - Computer Science Applications KW - Molecular Biology KW - Biochemistry KW - Statistics and Probability TI - SCIM: Universal single-cell matching with unpaired feature sets VL - 36 ER - TY - CONF AB - The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In this paper, we summarize the results of Locatello et al., 2019, and focus on their implications for practitioners. We discuss the theoretical result showing that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases and the practical challenges it entails. Finally, we comment on our experimental findings, highlighting the limitations of state-of-the-art approaches and directions for future research. AU - Locatello, Francesco AU - Bauer, Stefan AU - Lucic, Mario AU - Rätsch, Gunnar AU - Gelly, Sylvain AU - Schölkopf, Bernhard AU - Bachem, Olivier ID - 14186 IS - 9 SN - 9781577358350 T2 - The 34th AAAI Conference on Artificial Intelligence TI - A commentary on the unsupervised learning of disentangled representations VL - 34 ER - TY - CONF AB - Intelligent agents should be able to learn useful representations by observing changes in their environment. We model such observations as pairs of non-i.i.d. images sharing at least one of the underlying factors of variation. First, we theoretically show that only knowing how many factors have changed, but not which ones, is sufficient to learn disentangled representations. Second, we provide practical algorithms that learn disentangled representations from pairs of images without requiring annotation of groups, individual factors, or the number of factors that have changed. Third, we perform a large-scale empirical study and show that such pairs of observations are sufficient to reliably learn disentangled representations on several benchmark data sets. Finally, we evaluate our learned representations and find that they are simultaneously useful on a diverse suite of tasks, including generalization under covariate shifts, fairness, and abstract reasoning. Overall, our results demonstrate that weak supervision enables learning of useful disentangled representations in realistic scenarios. AU - Locatello, Francesco AU - Poole, Ben AU - Rätsch, Gunnar AU - Schölkopf, Bernhard AU - Bachem, Olivier AU - Tschannen, Michael ID - 14188 T2 - Proceedings of the 37th International Conference on Machine Learning TI - Weakly-supervised disentanglement without compromises VL - 119 ER - TY - CONF AB - We propose a novel Stochastic Frank-Wolfe (a.k.a. conditional gradient) algorithm for constrained smooth finite-sum minimization with a generalized linear prediction/structure. This class of problems includes empirical risk minimization with sparse, low-rank, or other structured constraints. The proposed method is simple to implement, does not require step-size tuning, and has a constant per-iteration cost that is independent of the dataset size. Furthermore, as a byproduct of the method we obtain a stochastic estimator of the Frank-Wolfe gap that can be used as a stopping criterion. Depending on the setting, the proposed method matches or improves on the best computational guarantees for Stochastic Frank-Wolfe algorithms. Benchmarks on several datasets highlight different regimes in which the proposed method exhibits a faster empirical convergence than related methods. Finally, we provide an implementation of all considered methods in an open-source package. AU - Négiar, Geoffrey AU - Dresdner, Gideon AU - Tsai, Alicia AU - Ghaoui, Laurent El AU - Locatello, Francesco AU - Freund, Robert M. AU - Pedregosa, Fabian ID - 14187 T2 - Proceedings of the 37th International Conference on Machine Learning TI - Stochastic Frank-Wolfe for constrained finite-sum minimization VL - 119 ER - TY - JOUR AB - The idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train over 14000 models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on eight data sets. We observe that while the different methods successfully enforce properties “encouraged” by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, different evaluation metrics do not always agree on what should be considered “disentangled” and exhibit systematic differences in the estimation. Finally, increased disentanglement does not seem to necessarily lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets. AU - Locatello, Francesco AU - Bauer, Stefan AU - Lucic, Mario AU - Rätsch, Gunnar AU - Gelly, Sylvain AU - Schölkopf, Bernhard AU - Bachem, Olivier ID - 14195 JF - Journal of Machine Learning Research TI - A sober look at the unsupervised learning of disentangled representations and their evaluation VL - 21 ER -