Abstract: An image processing system is described which has a memory holding at least one image depicting at least one person previously unseen by the image processing system. The system has a trained probabilistic model which describes a relationship between image features, context, identities and a plurality of names of people, wherein at least one of the identities identifies a person depicted in the image without an associated name in the plurality of names. The system has a feature extractor which extracts features from the image, and a processor which predicts an identity of the person depicted in the image using the extracted features and the probabilistic model.

Abstract: A computer program predictor is described which has a processor configured to access a program attribute predictor; and a memory storing a search component configured to search a space of possible programs, to find a program which, given an input data instance and an output data instance, will compute the output data instance from the input data instance, the search being guided by attributes predicted by the attribute predictor given the input data instance and the output data instance.

Abstract: An image processing system is described which has a memory holding at least one image depicting at least one person previously unseen by the image processing system. The system has a trained probabilistic model which describes a relationship between image features, context, identities and a plurality of names of people, wherein at least one of the identities identifies a person depicted in the image without an associated name in the plurality of names. The system has a feature extractor which extracts features from the image, and a processor which predicts an identity of the person depicted in the image using the extracted features and the probabilistic model.

Abstract: A computer program predictor is described which has a processor configured to access a program attribute predictor; and a memory storing a search component configured to search a space of possible programs, to find a program which, given an input data instance and an output data instance, will compute the output data instance from the input data instance, the search being guided by attributes predicted by the attribute predictor given the input data instance and the output data instance.

Abstract: A computer program predictor is described which has a processor configured to access a program attribute predictor; and a memory storing a search component configured to search a space of possible programs, to find a program which, given an input data instance and an output data instance, will compute the output data instance from the input data instance, the search being guided by attributes predicted by the attribute predictor given the input data instance and the output data instance.

Abstract: An image processing system is described which has a memory holding at least one image depicting at least one person previously unseen by the image processing system. The system has a trained probabilistic model which describes a relationship between image features, context, identities and a plurality of names of people, wherein at least one of the identities identifies a person depicted in the image without an associated name in the plurality of names. The system has a feature extractor which extracts features from the image, and a processor which predicts an identity of the person depicted in the image using the extracted features and the probabilistic model.

Abstract: A computer program predictor is described which has a processor configured to access a program attribute predictor; and a memory storing a search component configured to search a space of possible programs, to find a program which, given an input data instance and an output data instance, will compute the output data instance from the input data instance, the search being guided by attributes predicted by the attribute predictor given the input data instance and the output data instance.

Abstract: A data compression apparatus is described which has an encoder configured to receive an input data item and to compress the data item into an encoding comprising a plurality of numerical values. The numerical values are grouped at least according to whether they relate to content of the input data item or style of the input data item. The encoder has been trained using a plurality of groups of training data items grouped according to the content and where training data items within individual ones of the groups vary with respect to the style. The encoder has been trained using a training objective which takes into account the groups.

Abstract: In various examples there is a data retrieval apparatus. The apparatus has a processor configured to receive a data retrieval request associated with a user. The apparatus also has a machine learning system configured to compute an affinity matrix of users for data items. The affinity matrix has a plurality of observed ratings of data items, and a plurality of predicted ratings of data items. The processor is configured to output a ranked list of data items for the user according to contents of the affinity matrix.

Abstract: An image processing system is described which has a memory holding at least one image depicting at least one person previously unseen by the image processing system. The system has a trained probabilistic model which describes a relationship between image features, context, identities and a plurality of names of people, wherein at least one of the identities identifies a person depicted in the image without an associated name in the plurality of names. The system has a feature extractor which extracts features from the image, and a processor which predicts an identity of the person depicted in the image using the extracted features and the probabilistic model.

Abstract: A system to predict a location of a feature point of an articulated object from a plurality of data points relating to the articulated object of which some possess and some are missing 2D location data. The data points are input into a machine learning model that is trained to predict 2D location data for each feature point of the articulated object that was missing location data.

Abstract: A data compression apparatus is described which has an encoder configured to receive an input data item and to compress the data item into an encoding comprising a plurality of numerical values. The numerical values are grouped at least according to whether they relate to content of the input data item or style of the input data item. The encoder has been trained using a plurality of groups of training data items grouped according to the content and where training data items within individual ones of the groups vary with respect to the style. The encoder has been trained using a training objective which takes into account the groups.

Abstract: A depth detection apparatus is described which has a memory storing raw time-of-flight sensor data received from a time-of-flight sensor. The depth detection apparatus also has a trained machine learning component having been trained using training data pairs. A training data pair comprises at least one simulated raw time-of-flight sensor data value and a corresponding simulated ground truth depth value. The trained machine learning component is configured to compute in a single stage, for an item of the stored raw time-of-flight sensor data, a depth value of a surface depicted by the item, by pushing the item through the trained machine learning component.

Abstract: An image processing system is described which has a memory holding at least one image depicting at least one person previously unseen by the image processing system. The system has a trained probabilistic model which describes a relationship between image features, context, identities and a plurality of names of people, wherein at least one of the identities identifies a person depicted in the image without an associated name in the plurality of names. The system has a feature extractor which extracts features from the image, and a processor which predicts an identity of the person depicted in the image using the extracted features and the probabilistic model.

Abstract: A depth detection apparatus is described which has a memory and a computation logic. The memory stores frames of raw time-of-flight sensor data received from a time-of-flight sensor, the frames having been captured by a time-of-flight camera in the presence of motion such that different ones of the frames were captured using different locations of the camera and/or with different locations of an object in a scene depicted in the frames. The computation logic has functionality to compute a plurality of depth maps from the stream of frames, whereby each frame of raw time-of-flight sensor data contributes to more than one depth map.

Abstract: A computer program predictor is described which has a processor configured to access a program attribute predictor; and a memory storing a search component configured to search a space of possible programs, to find a program which, given an input data instance and an output data instance, will compute the output data instance from the input data instance, the search being guided by attributes predicted by the attribute predictor given the input data instance and the output data instance.

Abstract: A data compression apparatus is described which has an encoder configured to receive an input data item and to compress the data item into an encoding comprising a plurality of numerical values. The numerical values are grouped at least according to whether they relate to content of the input data item or style of the input data item. The encoder has been trained using a plurality of groups of training data items grouped according to the content and where training data items within individual ones of the groups vary with respect to the style. The encoder has been trained using a training objective which takes into account the groups.

Abstract: A data compression apparatus is described which has an encoder configured to receive an input data item and to compress the data item into an encoding comprising a plurality of numerical values. The numerical values are grouped at least according to whether they relate to content of the input data item or style of the input data item. The encoder has been trained using a plurality of groups of training data items grouped according to the content and where training data items within individual ones of the groups vary with respect to the style. The encoder has been trained using a training objective which takes into account the groups.

Abstract: Examples of time-of-flight (“TOF”) simulation of multipath light phenomena are described. For example, in addition to recording light intensity for a pixel during rendering, a graphics tool records the lengths (or times) and segment counts for light paths arriving at the pixel. Such multipath information can provide a characterization of the temporal light density of light that arrives at the pixel in response to one or more pulses of light. The graphics tool can use stratification and/or priority sampling to reduce variance in recorded light path samples. Realistic, physically-accurate simulation of multipath light phenomena can, in turn, help calibrate a TOF camera so that it more accurately estimates the depths of real world objects observed using the TOF camera. Various ways to improve the process of inferring imaging conditions such as depth, reflectivity, and ambient light based on images captured using a TOF camera are also described.

Abstract: A depth detection apparatus is described which has a memory storing raw time-of-flight sensor data received from a time-of-flight sensor. The depth detection apparatus also has a trained machine learning component having been trained using training data pairs. A training data pair comprises at least one simulated raw time-of-flight sensor data value and a corresponding simulated ground truth depth value. The trained machine learning component is configured to compute in a single stage, for an item of the stored raw time-of-flight sensor data, a depth value of a surface depicted by the item, by pushing the item through the trained machine learning component.