Abstract: A method for classifying input measurement data with respect to a given task using a given classifier. The method includes: identifying, based on the given task, a relevant subset of the input measurement data that is of a higher relevancy with respect to the given task than the rest of the input measurement data; determining, based on the input measurement data and the identified subset, an enhanced input for the given classifier, such that, in this enhanced input, a portion of the input measurement data that corresponds to the identified subset has a higher weight than other content of the input measurement data not corresponding to this identified subset; providing the enhanced input to the given classifier, thereby obtaining an output from the classifier; and determining the final classification result from this output.

Abstract: A method for reconstructing a location-dependent scalar additional quantity of a scene from an image of the scene divided into pixels. The method includes: feeding pixel values of the image to a neural network; processing the pixel values by the neural network to produce local difference information, which in each case indicates how the location-dependent scalar additional quantity of the scene changes at the position indicated by the particular pixel; ascertaining the scalar additional information for further locations indicated by pixels of the image, from the local difference information.

Abstract: A device and a computer-implemented method for machine learning. The method includes: providing an input for a model, determining with the model a first classification that indicates a class for the input, determining with the model depending on the input a likelihood that an expert determines a correct classification for the input, determining with the model depending on the input a likelihood that the input is in-distribution data or out-of-distribution data with respect to a distribution of data that the model is trained on, determining a second classification that indicates whether the input is considered as in-distribution data or out-of-distribution data with respect to the distribution of data that the model is trained on depending on the first classification and depending on the likelihoods, determining an output of the model depending on the first classification and the second classification, and outputting the output.

Abstract: An agricultural machine assembly with a driver assistance system. The agricultural machine assembly includes the driver assistance system, and an agricultural work machine and an attachment adapted to the agricultural work machine and designed as an agricultural baler. The agricultural work machine and the attachment each comprise working units which may be actuated for setting parameters (P). The driver assistance system optimizes operation and is configured to form a work machine setting device and a baler setting device. The work machine setting device and the baler setting device are configured to optimize the mode of operation of the agricultural work machine and the agricultural baler depending on each other.

Abstract: A system and method, in particular computer implemented method for determining a perturbation for attacking and/or validating an association tracker. The method includes providing digital image data that includes an object, determining with the digital image data a first feature that characterizes the object, providing in particular from a storage a second feature that characterizes a tracked object, determining the perturbation depending on a measure of a similarity between the first feature and the second feature.

Abstract: Systems and computer-implemented methods for training a machine learnable model and for using the machine learned model for inference, both of which using only limited memory resources. During training and inference, the machine learnable model uses previous state information. A state memory is provided which efficiently stores this previous state information. Instead of storing each previous state individually and integrally, for each element of the internal state, a value is stored in the state memory which is indicative of a most recent occurrence of an element of the internal state of the machine learnable model holding or transitioning to a particular binary state value. The states of the machine learnable model are representable as binary values and when states infrequently hold or transition to a particular binary state value.

Abstract: A method and system for representing an environment of a first mobile platform. The method includes: capturing features of the environment by discrete time sequences of sensor-data from at least two sensors and respective time markers; determining distances of the first mobile platform to the features of the environment; estimating semantic information of the features of the environment; transforming the semantic information of the features of the environment into a moving spatial reference system, wherein a position of the first mobile platform is at a constant site, using the respective determined distances and respective time markers; creating an input tensor using sequences of the transformed semantic information of the features of the environment, corresponding to the sequences of the sensor data of the at least two sensors; generating an output tensor that represents the environment using a deep neural network at a requested point in time and the input tensor.

Abstract: An apparatus and a computer implemented method for unsupervised representation learning. The method includes: providing an input data set comprising samples of a first domain and samples of a second domain; providing a reference assignment between pairs of one sample from the first domain and one sample from the second domain; providing an encoder that is configured to map a sample of the input data set depending on at least one parameter of the encoder to an embedding; providing a similarity kernel for determining a similarity between embeddings; determining with the encoder embeddings of samples from the first domain and embeddings of samples from the second domain; determining with the similarity kernel similarities for pairs of one embedding of a sample from the first domain and one embedding of a sample from the second domain; determining at least one parameter of the encoder depending on a loss.

Abstract: A method for training a neural network for determining a task output with respect to a given task. The method includes: providing unlabeled and/or labelled encoder training records of measurement data; training the encoder network to map encoder training records to representations towards the goal that these representations, and/or or one or more work products derived from the representations, fulfil a self-consistency condition or correspond to ground truth; providing task training records that are labelled with ground truth; and training the association network and the task head networks towards the goal that, when a task training record is mapped to a representation using the encoder network, and the representation is mapped to a task output by the combination of the association network and the task head networks, the so-obtained task output corresponds to the ground truth with which the training record is labelled, as measured by a task loss function.

Abstract: A device for operating a machine learning system. The machine learning system is assigned a predefinable rollout, which characterizes a sequence in which each of the layers ascertains an intermediate variable. When assigning the rollout, each connection or each layer is assigned a control variable, which characterizes whether the intermediate variable of each of the subsequent connected layers is ascertained according to the sequence or regardless of the sequence. A calculation of an output variable of the machine learning system as a function of an input variable of the machine learning system is controlled as a function of the predefinable rollout. Also described is a method for operating the machine learning system.

Abstract: A system and computer-implemented method are provided for enabling control of a physical system based on a state of the physical system which is inferred from sensor data. The system and method may iteratively infer the state by, in an iteration, obtaining an initial inference of the state using a mathematical model representing a prior knowledge-based modelling of the state, and by applying a learned model to the initial inference of the state and the sensor measurement, wherein the learned model has been learned to minimize an error between initial inferences provided by the mathematical model and a ground truth and to provide a correction value as output for correcting the initial inference of the state of the mathematical model. Output data may be provided to an output device to enable control of the physical system based on the inferred state.

Abstract: The disclosure relates to a computer-implemented method for carrying out a quantitative polymerase chain reaction (qPCR) process, comprising the following steps: —cyclically carrying out qPCR cycles; —measuring an intensity value of a fluorescence relating to each qPCR cycle to obtain a qPCR curve from intensity values; —analyzing the shape of the qPCR curve using a data-based classification model trained to provide a classification result depending on the shape of the qPCR curve; and—carrying out the qPCR process depending on the classification result of the analysis of the shape of the qPCR curve.

Abstract: The disclosure relates to a method for carrying out a quantitative polymerase chain reaction (qPCR) including cyclically executing a predetermined plurality of qPCR cycles, and measuring an intensity value after each of the predetermined plurality of qPCR cycles to obtain a measured portion of a qPCR curve of intensity values. The method includes estimating, after cycling the predetermined plurality of qPCR cycles, a remainder of the qPCR curve using the measured intensity values and a data-based trainable qPCR model, and selecting one of a plurality of steps of the method based on the remainder of the plot of the qPCR curve. The method includes conducting the selected one of the plurality of steps of the method.

Abstract: A device and computer-implemented method for object tracking. The method comprises providing a sequence of digital images, determining a sequence of relational graph embeddings, wherein a first relational graph embedding of the sequence comprises a first object embedding representing a first object in a first digital image of the sequence of digital images, wherein the first relational graph embedding comprises a first relation embedding of a relation for the first object embedding, wherein the first relation embedding relates the first object embedding to embeddings representing other objects of the first digital image in the first relational graph embedding and to embeddings in a second relational graph embedding of the sequence that represent objects of a second digital image of the sequence of digital images.

Abstract: An image classifier for classifying an input image x with respect to combinations of an object value o and an attribute value. The image classifier includes an encoder network that is configured to map the input image to a representation comprising multiple independent components; an object classification head network configured to map representation components of the input image to one or more object values; an attribute classification head network configured to map representation components of the input image to one or more attribute values; and an association unit configured to provide, to each classification head network, a linear combination of those representation components of the input image x that are relevant for the classification task of the respective classification head network. A method for training the image classifier is also provided.

Abstract: A method for efficiently ascertaining output signals of a sequence of output signals with the aid of a sequence of layers of a machine learning system, in particular a neural network, from a sequence of input signals. The neural network is supplied in succession with the input signals of the sequence of input signals in a sequence of discrete time increments. At the discrete time increments, signals present in the network are in each case further propagated through a layer of the sequence of layers.

Abstract: A system and method, in particular computer implemented method for determining a perturbation for attacking and/or validating an association tracker. The method includes providing digital image data that includes an object, determining with the digital image data a first feature that characterizes the object, providing in particular from a storage a second feature that characterizes a tracked object, determining the perturbation depending on a measure of a similarity between the first feature and the second feature.

Abstract: A method is described for measuring the vulnerability of an AI module to spoofing attempts, including the classification and/or regression onto which the AI module maps the update data set is ascertained as an unperturbed result for a predefined data set in the input space E; at least one perturbation S having a dimensionality d<D is applied to the predefined data set so that at least one perturbed data set results in the input space E; the classification and/or regression onto which the AI module maps the perturbed data set is ascertained as the perturbed result; the deviation of the perturbed result from the unperturbed result is ascertained using predefined metrics; in response to the deviation satisfying a predefined criterion, it is determined that the AI module with regard to the predefined data set is vulnerable to spoofing attempts having a dimensionality d.

Abstract: A computer-implemented method for frequency coding of image data from an imaging sensor. The method includes: supplying first image data of an individual image recorded by an imaging sensor, the first image data having depth values of the individual image coded as a whole number or as a floating-point number; receiving the first image data by an algorithm, which frequency codes the depth values of the individual image by a predefined number of periodic functions; and outputting second image data by the algorithm, the second image data having frequency coded depth values of the individual image. A computer-implemented method is described for supplying an algorithm of machine learning for the classification of objects included in image data of an individual image from an imaging sensor. A system for the frequency coding of image data from an imaging sensor, a computer program, and a computer-readable data carrier, are also described.

Abstract: A computer-implemented method for determining an output signal for an input signal using a classifier. The output signal characterizes a classification of the input signal. The method includes: determining a latent representation based on the input signal using an invertible factorization model comprised in the classifier, the latent representation comprises a plurality of factors; determining the output signal based on the latent representation using an internal classifier comprised in the classifier.