Christian Osendorfer

We present a computationally efficient architecture for image
super-resolution that achieves state-of-the-art results on images with large spatial extend. Apart from utilizing Convolutional Neural Networks, our approach leverages recent advances in fast approximate inference for sparse coding. We empirically show that upsampling methods work much better on latent representations than in the original spatial domain. Our experiments indicate that the proposed architecture can serve as a basis for additional future improvements in image superresolution.

ABSTRACT Estimating human fingertip forces is required to understand force distribution in grasping and manipulation. Human grasping behavior can then be used to develop force-and impedance-based grasping and manipulation strategies for robotic hands. However, estimating human grip force naturally is only possible with instrumented objects or unnatural gloves, thus greatly limiting the type of objects used. In this paper we describe an approach which uses images of the human fingertip to reconstruct grip force and torque at the finger. Our approach does not use finger-mounted equipment, but instead a steady camera observing the fingers of the hand from a distance. This allows for finger force estimation without any physical interference with the hand or object itself, and is therefore universally applicable. We construct a 3-dimensional finger model from 2D images. Convolutional Neural Networks (CNN) are used to predict the 2D image to a 3D model transformation matrix. Two methods of CNN are designed for separate and combined outputs of orientation and position. After learning, our system shows an alignment accuracy over 98% on unknown data. In the final step, a Gaussian process estimates finger force and torque from the aligned images based on color changes and deformations of the nail and its surrounding skin. Experimental results shows that the accuracy achieves about 95% in the force estimation and 90% in the torque.

Unsupervised feature learning has shown impressive results for a wide range of input modalities, in particular for object classification tasks in computer vision. Using a large amount of unlabeled data, unsupervised feature learning methods are utilized to construct high-level representations that are discriminative enough for subsequently trained supervised classification algorithms. However, it has never been \emph{quantitatively} investigated yet how well unsupervised learning methods can find \emph{low-level representations} for image patches without any additional supervision. In this paper we examine the performance of pure unsupervised methods on a low-level correspondence task, a problem that is central to many Computer Vision applications. We find that a special type of Restricted Boltzmann Machines (RBMs) performs comparably to hand-crafted descriptors. Additionally, a simple binarization scheme produces compact representations that perform better than several state-of-the...

ABSTRACT Recent advances in the estimation of deep directed graphical models and recurrent networks let us contribute to the removal of a blind spot in the area of probabilistc modelling of time series. The proposed methods i) can infer distributed latent state-space trajectories with nonlinear transitions, ii) scale to large data sets thanks to the use of a stochastic objective and fast, approximate inference, iii) enable the design of rich emission models which iv) will naturally lead to structured outputs. Two different paths of introducing latent state sequences are pursued, leading to the variational recurrent auto encoder (VRAE) and the variational one step predictor (VOSP). The use of independent Wiener processes as priors on the latent state sequence is a viable compromise between efficient computation of the Kullback-Leibler divergence from the variational approximation of the posterior and maintaining a reasonable belief in the dynamics. We verify our methods empirically, obtaining results close or superior to the state of the art. We also show qualitative results for denoising and missing value imputation.

ABSTRACT Leveraging advances in variational inference, we propose to enhance recurrent neural networks with latent variables, resulting in Stochastic Recurrent Networks (STORNs). The model i) can be trained with stochastic gradient methods, ii) allows structured and multi-modal conditionals at each time step, iii) features a reliable estimator of the marginal likelihood and iv) is a generalisation of deterministic recurrent neural networks. We evaluate the method on four polyphonic musical data sets and motion capture data.

Recurrent Neural Networks (RNNs) are rich models for the processing of sequential
data. Recent work on advancing the state of the art has been focused on the
optimization or modelling of RNNs, mostly motivated by adressing the problems
of the vanishing and exploding gradients. The control of overfitting has seen considerably
less attention. This paper contributes to that by analyzing fast dropout,
a recent regularization method for generalized linear models and neural networks
from a back-propagation inspired perspective. We show that fast dropout implements
a quadratic form of an adaptive, per-parameter regularizer, which rewards
large weights in the light of underfitting, penalizes them for overconfident predictions
and vanishes at minima of an unregularized training loss. The derivatives
of that regularizer are exclusively based on the training error signal. One consequence
of this is the absence of a global weight attractor, which is particularly
appealing for RNNs, since the dynamics are not biased towards a certain regime.
We positively test the hypothesis that this improves the performance of RNNs on
four musical data sets.

Safety is one of the key issues in the use of
robots, especially when human–robot interaction is targeted.
Although unforeseen environment situations, such as collisions
or unexpected user interaction, can be handled with specially
tailored control algorithms, hard- or software failures typically
lead to situations where too large torques are controlled, which
cause an emergency state: hitting an end stop, exceeding
a torque, and so on—which often halts the robot when it
is too late. No sufficiently fast and reliable methods exist
which can early detect faults in the abundance of sensor and
controller data. This is especially difficult since, in most cases,
no anomaly data are available. In this paper we introduce a new
robot anomaly detection system (RADS) which can cope with
abundant data in which no or very little anomaly information
is present.

We investigate if a deep Convolutional Neural Network can learn representations of local image patches that are usable in the impor- tant task of keypoint matching. We examine several possible loss func- tions for this correspondance task and show emprically that a newly suggested loss formulation allows a Convolutional Neural Network to find compact local image descriptors that perform comparably to state- of-the-art approaches.