Provided is a reservoir computing system including a reservoir having a random laser for emitting a non-linear optical signal with respect to an input signal. The reservoir computing system also includes a converter for converting the non-linear optical signal into an output signal by applying a conversion function. The conversion function is trained by using a training input signal and a target output signal.

One or more multi-stage optimization iterations are performed with respect to a compression algorithm. A given iteration comprises a first stage in which hyper-parameters of a perceptual quality algorithm are tuned independently of the compression algorithm. A second stage of the iteration comprises tuning hyper-parameters of the compression algorithm using a set of perceptual quality scores generated by the tuned perceptual quality algorithm. The final stage of the iteration comprises performing a compression quality evaluation test on the tuned compression algorithm.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium that validates labels associated with sensor measurements of a scene in an environment. One of the methods includes receiving data representing a sensor measurement of a scene in an environment generated by one or more sensors. The sensor measurement can be associated with one or more labels, and each label can identify a portion of the sensor measurement that has been classified as measuring an object in the environment. For each of the labels, a determination can be made as to whether the label satisfies each of the validation criteria. Each validation criterion can measure whether one or more characteristics of the label are consistent with one or more characteristics of real-world objects in the environment. In response to determining that a particular label of the one or more labels does not satisfy one or more of the validation criteria, a notification can be generated indicating that the particular label is not a valid label for any real-world object in the scene of the environment.

A computer-implemented method of generating metadata from an image may comprise sending the image to an object detection service, which generates detections metadata from the image. The image may also be sent to a visual features extractor, which extracts visual features metadata from the image. The generated detections metadata may then be sent to an uncertainty score calculator, which computes an uncertainty score from the detections metadata. The uncertainty score may be related to a level of uncertainty within the detections metadata. The image, the visual features metadata, the detections metadata and the uncertainty score may then be stored in a database accessible over a computer network.

An autonomous vehicle collects sensor data of an environment surrounding the autonomous vehicle including traffic entities such as pedestrians, bicyclists, or other vehicles. The sensor data is provided to a machine learning based model along with an expected turn direction of the autonomous vehicle to determine a hidden context attribute of a traffic entity given the expected turn direction of the autonomous vehicle. The hidden context attribute of the traffic entity represents factors that affect the behavior of the traffic entity, and the hidden context attribute is used to predict future behavior of the traffic entity. Instructions to control the autonomous vehicle are generated based on the hidden context attribute.

A discriminative captioning system generates captions for digital images that can be used to tell two digital images apart. The discriminative captioning system includes a machine learning system that is trained by a discriminative captioning training system that includes a retrieval machine learning system. For training, a digital image is input to the caption generation machine learning system, which generates a caption for the digital image. The digital image and the generated caption, as well as a set of additional images, are input to the retrieval machine learning system. The retrieval machine learning system generates a discriminability loss that indicates how well the retrieval machine learning system is able to use the caption to discriminate between the digital image and each image in the set of additional digital images. This discriminability loss is used to train the caption generation machine learning system.

An artificial intelligence (AI) system that utilizes a machine learning algorithm, such as deep learning, etc. and an application of the AI system is provided. A method, performed by a server, of integrating and managing a plurality of databases (DBs) includes obtaining a plurality of knowledge graphs related to DBs generated from the plurality of DBs having different structures from one another, inputting the plurality of knowledge graphs related to DBs into a learning model related to DB for determining a correlation between data in the plurality of DBs, and obtaining a virtual integrated knowledge graph output from the learning model related to DB and including information about a correlation extracted from the plurality of knowledge graphs related to DBs.

Embodiments may exclude portions of input data in order to improve the accuracy and explanatory quality of the output of machine learning models by disregarding parts of the input during the optimization process by masking them during backpropagation. For example, in an embodiment, a method may be implemented in a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method may comprise receiving, at the computer system, input data and a machine learning model to generate a prediction based on the input data, generating, at the computer system, a mask indicating portions of the input data to be disregarded during backpropagation of the machine learning model, and modifying, at the computer system, the generated mask to improve the prediction of the machine learning model.

Methods, systems, and apparatus for motion-based human video detection are disclosed. A method includes generating a representation of a difference between two frames of a video; providing, to an object detector, a particular frame of the two frames and the representation of the difference between two frames of the video; receiving an indication that the object detector detected an object in the particular frame; determining that detection of the object in the particular frame was a false positive detection; determining an amount of motion energy where the object was detected in the particular frame; and training the object detector based on penalization of the false positive detection in accordance with the amount of motion energy where the object was detected in the particular frame.

The objective of the present invention is to provide an image classification device and a method therefor with which suitable teaching data can be created. An image classification device that carries out image classification using images which are in a class to be classified and include teaching information, and images which are in a class not to be classified and to which teaching information has not been assigned, said image classification device being characterized by being provided with: an image group input unit for receiving inputs of an image group belonging to a class to be classified and an image group belonging to a class not to be classified; and a subclassification unit for extracting a feature amount for each image in an image group, clustering the feature amounts of the images in the image group belonging to a class not to be classified, and thereby dividing the images into sub-classes.