14 results on '"Cseke, Botond"'
Search Results
2. Network of epistatic interactions within a yeast snoRNA
- Author
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Puchta, Olga, Cseke, Botond, Czaja, Hubert, Tollervey, David, Sanguinetti, Guido, and Kudla, Grzegorz
- Published
- 2016
3. Local Distance Preserving Auto-encoders using Continuous k-Nearest Neighbours Graphs
- Author
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Chen, Nutan, van der Smagt, Patrick, and Cseke, Botond
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Machine Learning (cs.LG) - Abstract
Auto-encoder models that preserve similarities in the data are a popular tool in representation learning. In this paper we introduce several auto-encoder models that preserve local distances when mapping from the data space to the latent space. We use a local distance preserving loss that is based on the continuous k-nearest neighbours graph which is known to capture topological features at all scales simultaneously. To improve training performance, we formulate learning as a constraint optimisation problem with local distance preservation as the main objective and reconstruction accuracy as a constraint. We generalise this approach to hierarchical variational auto-encoders thus learning generative models with geometrically consistent latent and data spaces. Our method provides state-of-the-art performance across several standard datasets and evaluation metrics.
- Published
- 2022
4. Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior
- Author
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van Gerven, Marcel A.J., Cseke, Botond, de Lange, Floris P., and Heskes, Tom
- Published
- 2010
- Full Text
- View/download PDF
5. Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation.
- Author
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Frank, Felix, Paraschos, Alexandros, van der Smagt, Patrick, and Cseke, Botond
- Subjects
ROBOT motion ,CONSTRAINED optimization ,GAUSSIAN distribution ,TRAJECTORY optimization ,MOBILE robots ,ROBOT kinematics ,SHARED workspaces - Abstract
Placing robotsoutside controlled conditions requires versatile movement representations that allow robots to learn new tasks and adapt them to environmental changes. The introduction of obstacles or the placement of additional robots in the workspace and the modification of the joint range due to faults or range-of-motion constraints are typical cases, where the adaptation capabilities play a key role for safely performing the robot’s task. Probabilistic movement primitives (ProMPs) have been proposed for representing adaptable movement skills, which are modeled as Gaussian distributions over trajectories. These are analytically tractable and can be learned from a small number of demonstrations. However, both the original ProMP formulation and the subsequent approaches only provide solutions to specific movement adaptation problems, e.g., obstacle avoidance, and a generic, unifying, probabilistic approach to adaptation is missing. In this article, we develop a generic probabilistic framework for adapting ProMPs. We unify previous adaptation techniques, for example, various types of obstacle avoidance, via-points, and mutual avoidance, in one single framework and combine them to solve complex robotic problems. Additionally, we derive novel adaptation techniques such as temporally unbound via-points and mutual avoidance. We formulate adaptation as a constrained optimization problem, where we minimize the Kullback–Leibler divergence between the adapted distribution and the distribution of the original primitive,while we constrain the probability mass associated with undesired trajectories to be low. We demonstrate our approach on several adaptation problems on simulated planar robot arms and seven-degree-of-freedom Franka Emika robots in a dual-robot-arm setting. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
6. Increasing the Generalisation Capacity of Conditional VAEs
- Author
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Klushyn, Alexej, Chen, Nutan, Cseke, Botond, Bayer, Justin, and van der Smagt, Patrick
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Statistics - Machine Learning ,Machine Learning (stat.ML) ,Machine Learning (cs.LG) - Abstract
We address the problem of one-to-many mappings in supervised learning, where a single instance has many different solutions of possibly equal cost. The framework of conditional variational autoencoders describes a class of methods to tackle such structured-prediction tasks by means of latent variables. We propose to incentivise informative latent representations for increasing the generalisation capacity of conditional variational autoencoders. To this end, we modify the latent variable model by defining the likelihood as a function of the latent variable only and introduce an expressive multimodal prior to enable the model for capturing semantically meaningful features of the data. To validate our approach, we train our model on the Cornell Robot Grasping dataset, and modified versions of MNIST and Fashion-MNIST obtaining results that show a significantly higher generalisation capability.
- Published
- 2019
7. Factored expectation propagation for input-output FHMM models in systems biology
- Author
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Cseke, Botond and Sanguinetti, Guido
- Subjects
FOS: Computer and information sciences ,Statistics - Machine Learning ,Machine Learning (stat.ML) - Abstract
We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications. We propose an approach based on input-output factorial hidden Markov models and propose a structured variational inference approach to infer the structure and states of the model. We start from the classical free form structured variational mean field approach and use a expectation propagation to approximate the expectations needed in the variational loop. We show that this corresponds to a factored expectation constrained approximate inference. We validate our model through extensive simulations and demonstrate its applicability on a real world bacterial data set.
- Published
- 2013
8. Sparse approximations in spatio-temporal point-process models
- Author
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Cseke, Botond, Zammit-Mangion, Andrew, Sanguinetti, Guido, and Heskes, Tom
- Subjects
stat.ML - Abstract
Analysis of spatio-temporal point patterns plays an important role in several disciplines, yet inference in these systems remains computationally challenging due to the high resolution modelling generally required by large data sets and the analytically intractable likelihood function. Here, we exploit the sparsity structure of a fully-discretised log-Gaussian Cox process model by using expectation constrained approximate inference. The resulting family of expectation propagation algorithms scale well with the state dimension and the length of the temporal horizon with moderate loss in distributional accuracy. They hence provide a flexible and faster alternative to both the filtering-smoothing type algorithms and the approaches which implement the Laplace method or expectation propagation on (block) sparse latent Gaussian models. We demonstrate the use of the proposed method in the reconstruction of conflict intensity levels in Afghanistan from a WikiLeaks data set.
- Published
- 2013
9. Approximate inference in latent Gaussian-Markov models from continuous time observations
- Author
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Cseke, Botond, Opper, Manfred, and Sanguinetti, Guido
- Abstract
We propose an approximate inference algorithm for continuous time Gaussian-Markov process models with both discrete and continuous time likelihoods. We show that the continuous time limit of the expectation propagation algorithm exists and results in a hybrid fixed point iteration consisting of (1) expectation propagation updates for the discrete time terms and (2) variational updates for the continuous time term. We introduce corrections methods that improve on the marginals of the approximation. This approach extends the classical Kalman-Bucy smoothing procedure to non-Gaussian observations, enabling continuous-time inference in a variety of models, including spiking neuronal models (state-space models with point process observations) and box likelihood models. Experimental results on real and simulated data demonstrate high distributional accuracy and significant computational savings compared to discrete-time approaches in a neural application.
- Published
- 2013
10. MMDiff: quantitative testing for shape changes in ChIP-Seq data sets.
- Author
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Schweikert, Gabriele, Cseke, Botond, Clouaire, Thomas, Bird, Adrian, and Sanguinetti, Guido
- Subjects
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GENE expression , *TRANSCRIPTION factors , *DNA-protein interactions , *NUCLEOTIDE sequence , *IMMUNOPRECIPITATION - Abstract
Background Cell-specific gene expression is controlled by epigenetic modifications and transcription factor binding. While genome-wide maps for these protein-DNA interactions have become widely available, quantitative comparison of the resulting ChIP-Seq data sets remains challenging. Current approaches to detect differentially bound or modified regions are mainly borrowed from RNA-Seq data analysis, thus focusing on total counts of fragments mapped to a region, ignoring any information encoded in the shape of the peaks. Results Here, we present MMDiff, a robust, broadly applicable method for detecting differences between sequence count data sets. Based on quantifying shape changes in signal profiles, it overcomes challenges imposed by the highly structured nature of the data and the paucity of replicates. We first use a simulated data set to compare the performance of MMDiff with results obtained by four alternative methods. We demonstrate that MMDiff excels when peak profiles change between samples. We next use MMDiff to re-analyse a recent data set of the histone modification H3K4me3 elucidating the establishment of this prominent epigenomic marker. Our empirical analysis shows that the method yields reproducible results across experiments, and is able to detect functional important changes in histone modifications. To further explore the broader applicability of MMDiff, we apply it to two ENCODE data sets: one investigating the histone modification H3K27ac and one measuring the genome-wide binding of the transcription factor CTCF. In both cases, MMDiff proves to be complementary to count-based methods. In addition, we can show that MMDiff is capable of directly detecting changes of homotypic binding events at neighbouring binding sites. MMDiff is readily available as a Bioconductor package. Conclusion Our results demonstrate that higher order features of ChIP-Seq peaks carry relevant and often complementary information to total counts, and hence are important in assessing differential histone modifications and transcription factor binding. We have developed a new computational method, MMDiff, that is capable of exploring these features and therefore closes an existing gap in the analysis of ChIPSeq data sets. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
11. Properties of Bethe Free Energies and Message Passing in Gaussian Models.
- Author
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Cseke, Botond and Heskes, Tom
- Subjects
NUMERICAL analysis ,GAUSSIAN measures ,PROBABILISTIC number theory ,MEAN field theory ,APPROXIMATION algorithms - Abstract
We address the problem of computing approximate marginals in Gaussian probabilistic models by using mean field and fractional Bethe approximations. We define the Gaussian fractional Bethe free energy in terms of the moment parameters of the approximate marginals, derive a lower and an upper bound on the fractional Bethe free energy and establish a necessary condition for the lower bound to be bounded from below. It turns out that the condition is identical to the pairwise normalizability condition, which is known to be a sufficient condition for the convergence of the message passing algorithm. We show that stable fixed points of the Gaussian message passing algorithm are local minima of the Gaussian Bethe free energy. By a counterexample, we disprove the conjecture stating that the unboundedness of the free energy implies the divergence of the message passing algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2011
- Full Text
- View/download PDF
12. Approximate Marginals in Latent Gaussian Models.
- Author
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Cseke, Botond, Heskes, Tom, and Opper, Manfred
- Subjects
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GAUSSIAN Markov random fields , *APPROXIMATION theory , *COMPUTATIONAL complexity , *MATHEMATICAL statistics , *MATHEMATICAL variables , *MATHEMATICAL models , *MACHINE theory - Abstract
We consider the problem of improving the Gaussian approximate posterior marginals computed by expectation propagation and the Laplace method in latent Gaussian models and propose methods that are similar in spirit to the Laplace approximation of Tierney and Kadane (1986). We show that in the case of sparse Gaussian models, the computational complexity of expectation propagation can be made comparable to that of the Laplace method by using a parallel updating scheme. In some cases, expectation propagation gives excellent estimates where the Laplace approximation fails. Inspired by bounds on the correct marginals, we arrive at factorized approximations, which can be applied on top of both expectation propagation and the Laplace method. The factorized approximations can give nearly indistinguishable results from the non-factorized approximations and their computational complexity scales linearly with the number of variables. We experienced that the expectation propagation based marginal approximations we introduce are typically more accurate than the methods of similar complexity proposed by Rue et al. (2009). [ABSTRACT FROM AUTHOR]
- Published
- 2011
13. MULTI-CLASS INFERENCE WITH GAUSSIAN PROCESSES.
- Author
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CSEKE, BOTOND and CSATÓ, LEHEL
- Published
- 2005
14. Efficient Low-Order Approximation of First-Passage Time Distributions.
- Author
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Schnoerr, David, Cseke, Botond, Grima, Ramon, and Sanguinetti, Guido
- Subjects
- *
BAYESIAN analysis , *TRIMERIZATION , *STOCHASTIC processes - Abstract
We consider the problem of computing first-passage time distributions for reaction processes modeled by master equations. We show that this generally intractable class of problems is equivalent to a sequential Bayesian inference problem for an auxiliary observation process. The solution can be approximated efficiently by solving a closed set of coupled ordinary differential equations (for the low-order moments of the process) whose size scales with the number of species. We apply it to an epidemic model and a trimerization process and show good agreement with stochastic simulations. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
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