Author name cluster

Lehel Csató

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6 papers

2 author rows

ECAI Conference 2020 Conference Paper

Pruning CNN's with Linear Filter Ensembles

Csanád Sándor
Szabolcs Pável
Lehel Csató

Despite the promising results of convolutional neural networks (CNNs), their application on devices with limited resources is still a big challenge; this is mainly due to the huge memory and computation requirements of the CNN. To counter the limitation imposed by the network size, we use pruning to reduce the network size and – implicitly – the number of floating point operations (FLOPs). Contrary to the filter norm method – used in “conventional” network pruning – based on the assumption that a smaller norm implies “less importance” to its associated component, we develop a novel filter importance norm that is based on the change in the empirical loss caused by the presence or removal of a component from the network architecture. Since there are too many individual possibilities for filter configuration, we repeatedly sample from these architectural components and measure the system performance in the respective state of components being active or disabled. The result is a collection of filter ensembles – filter masks – and associated performance values. We rank the filters based on a linear and additive model and remove the least important ones such that the drop in network accuracy is minimal. We evaluate our method on a fully connected network, as well as on the ResNet architecture trained on the CIFAR-10 dataset. Using our pruning method, we managed to remove 60% of the parameters and 64% of the FLOPs from the ResNet with an accuracy drop of less than 0. 6%.

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ICRA Conference 2012 Conference Paper

Learning tracking control with forward models

Botond Bocsi
Philipp Hennig
Lehel Csató
Jan Peters 0001

Performing task-space tracking control on redundant robot manipulators is a difficult problem. When the physical model of the robot is too complex or not available, standard methods fail and machine learning algorithms can have advantages. We propose an adaptive learning algorithm for tracking control of underactuated or non-rigid robots where the physical model of the robot is unavailable. The control method is based on the fact that forward models are relatively straightforward to learn and local inversions can be obtained via local optimization. We use sparse online Gaussian process inference to obtain a flexible probabilistic forward model and second order optimization to find the inverse mapping. Physical experiments indicate that this approach can outperform state-of-the-art tracking control algorithms in this context.

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IROS Conference 2011 Conference Paper

Learning inverse kinematics with structured prediction

Botond Bocsi
Duy Nguyen-Tuong
Lehel Csató
Bernhard Schölkopf
Jan Peters 0001

Learning inverse kinematics of robots with redundant degrees of freedom (DoF) is a difficult problem in robot learning. The difficulty lies in the non-uniqueness of the inverse kinematics function. Existing methods tackle non-uniqueness by segmenting the configuration space and building a global solution from local experts. The usage of local experts implies the definition of an oracle, which governs the global consistency of the local models; the definition of this oracle is difficult. We propose an algorithm suitable to learn the inverse kinematics function in a single global model despite its multivalued nature. Inverse kinematics is approximated from examples using structured output learning methods. Unlike most of the existing methods, which estimate inverse kinematics on velocity level, we address the learning of the direct function on position level. This problem is a significantly harder. To support the proposed method, we conducted real world experiments on a tracking control task and tested our algorithms on these models.

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NeurIPS Conference 2001 Conference Paper

TAP Gibbs Free Energy, Belief Propagation and Sparsity

Lehel Csató
Manfred Opper
Ole Winther

The adaptive TAP Gibbs free energy for a general densely connected probabilistic model with quadratic interactions and arbritary single site constraints is derived. We show how a speciﬁc sequential minimization of the free energy leads to a generalization of Minka’s expectation propa- gation. Lastly, we derive a sparse representation version of the sequential algorithm. The usefulness of the approach is demonstrated on classiﬁca- tion and density estimation with Gaussian processes and on an indepen- dent component analysis problem.

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NeurIPS Conference 2000 Conference Paper

Sparse Representation for Gaussian Process Models

Lehel Csató
Manfred Opper

We develop an approach for a sparse representation for Gaussian Process (GP) models in order to overcome the limitations of GPs caused by large data sets. The method is based on a combination of a Bayesian online al(cid: 173) gorithm together with a sequential construction of a relevant subsample of the data which fully specifies the prediction of the model. Experi(cid: 173) mental results on toy examples and large real-world data sets indicate the efficiency of the approach.

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NeurIPS Conference 1999 Conference Paper

Efficient Approaches to Gaussian Process Classification

Lehel Csató
Ernest Fokoué
Manfred Opper
Bernhard Schottky
Ole Winther

We present three simple approximations for the calculation of the posterior mean in Gaussian Process classification. The first two methods are related to mean field ideas known in Statistical Physics. The third approach is based on Bayesian online approach which was motivated by recent results in the Statistical Mechanics of Neural Networks. We present simulation results showing: 1. that the mean field Bayesian evidence may be used for hyperparameter tuning and 2. that the online approach may achieve a low training error fast.

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