Abstract: A system comprising a processing circuitry configured to: obtain one or more MLIPs, each comprised of a sequence of one or more Data Processing Elements (DPEs), and each having (a) at least one input provided to the respective DPE, and (b) at least one output provided by the respective DPE, wherein the output of a given DPE of the DPEs, is the input of a subsequent DPE of the sequence, and wherein at least one of the DPEs is a trained machine learning model; generate, for each of the MLIPs, a respective pipeline representation comprising representations of the sequence, based on the DPEs, the inputs of the DPEs, and the outputs of the DPEs; merge the plurality of MLIP representations into a common representation; optimize the common representation; and generate, based on the common representation, a target model consuming less resources than the MLIPs.

Abstract: A model configuration selection system, the model configuration selection system comprising a processing circuitry configured to: (A) obtain: (a) one or more model configurations, each model configuration includes a set of parameters utilized to generate respective models, and (b) a training data-set comprising a plurality of unlabeled records, each unlabeled record including a collection of features describing a given state of a physical entity; (B) cluster the training data-set into two or more training data-set clusters using a clustering algorithm; (C) label (a) the unlabeled records of a subset of the training data-set clusters with a synthetic normal label, giving rise to a normal training data-set, and (b) the unlabeled records of the training data-set clusters not included in the subset with a synthetic abnormal label; (D) train, for each model configuration, using the normal training data-set, a corresponding model utilizing the corresponding set of parameters, each model capable of receiving the unl

Abstract: A system comprising a processing circuitry configured to: obtain: (a) one or more metadata representations, each representing metadata relating to a signal type of one or more signal types; (b) one or more signal sequences, each signal sequence is an ordered sequence of values associated with a given signal type of the one or more signal types; and (c) at least one new signal sequence, being a new ordered sequence of values, each associated with a label, the at least one new signal sequence is associated with corresponding at least one new signal type, not included in the one or more signal types; train a meta learner autoencoder, capable of mapping at least one given signal sequence and at least one respective metadata representation, being the metadata representation representing the signal type of the given signal sequence into a meta representation vector, wherein the trained meta learner autoencoder comprises a meta learner encoder and a meta learner decoder; determine, based on the metadata representati

Abstract: A model configuration selection system, the model configuration selection system comprising a processing circuitry configured to: (A) obtain: (a) one or more model configurations, each model configuration includes a set of parameters utilized to generate respective models, and (b) a training data-set comprising a plurality of unlabeled records, each unlabeled record including a collection of features describing a given state of a physical entity; (B) cluster the training data-set into two or more training data-set clusters using a clustering algorithm; (C) label (a) the unlabeled records of a subset of the training data-set clusters with a synthetic normal label, giving rise to a normal training data-set, and (b) the unlabeled records of the training data-set clusters not included in the subset with a synthetic abnormal label; (D) train, for each model configuration, using the normal training data-set, a corresponding model utilizing the corresponding set of parameters, each model capable of receiving the unl

Abstract: A physics-based model machine learning system, the physics-based model machine learning system comprising a processing circuitry configured to: obtain: (a) a training data-set, the training data-set comprising a plurality of training records, each training record including a collection of features describing a given allowed state of a physical entity, and (b) one or more physical models, modeling allowed physical patterns associated with the physical entity; enrich the training data-set by determining values of one or more unobservable features for one or more given training records of the training records, wherein the unobservable features are determined utilizing at least one of the physical models and at least one of the features of the respective given training records, giving rise to an enriched training data-set; train, using the enriched training data-set, a machine learning model capable of receiving one or more inference records, and determining, for each of the inference records, a corresponding nor

Abstract: A feature selection recommendation system, the feature selection recommendation system comprising a processing circuitry configured to: obtain: (a) a training data-set, the training data-set comprising a plurality of records, each record including a collection of features describing a given allowed state of a physical entity, and (b) a selection of one or more selected features of the features; generate, using a causality discovery model, for a plurality of pairs of the features of the training data-set, a respective causality score, the causality score being indicative of an influence between the features of the respective pair; identify additional recommended features, being one or more features that comply with a recommendation condition based on the plurality of pairs and the causality scores generated for the pairs; and provide a user of the feature selection recommendation system with an indication of the additional recommended features.

Abstract: A method and apparatus for finding correspondence between portions of two images that first subjects the two images to segmentation by weighted aggregation (10), then constructs directed acylic graphs (16,18) from the output of the segmentation by weighted aggregation to obtain hierarchical graphs of aggregates (20,22), and finally applies a maximally weighted subgraph isomorphism to the hierarchical graphs of aggregates to find matches between them (24). Two algorithms are described; one seeks a one-to-one matching between regions, and the other computes a soft matching, in which is an aggregate may have more than one corresponding aggregate. A method and apparatus for image segmentation based on motion cues. Motion provides a strong cue for segmentation. The method begins with local, ambiguous optical flow measurements. It uses a process of aggregation to resolve the ambiguities and reach reliable estimates of the motion.

Abstract: A search mechanism for users of search engines includes a back-end information retrieval system which accepts terms and weights thereof as input set from a front-end and processes said set. A front-end system interacting with said back-end information retrieval system. A database that is searchable by the backend information retrieval system. The search mechanism further includes a visual search interface module (VSI) implemented through the front-end system, where the graphic user interface module is used to change suggested-terms and refine query of multimedia search.

Abstract: A method and apparatus for finding correspondence between portions of two images that first subjects the two images to segmentation by weighted aggregation (10), then constructs directed acylic graphs (16,18) from the output of the segmentation by weighted aggregation to obtain hierarchical graphs of aggregates (20,22), and finally applies a maximally weighted subgraph isomorphism to the hierarchical graphs of aggregates to find matches between them (24). Two algorithms are described; one seeks a one-to-one matching between regions, and the other computes a soft matching, in which is an aggregate may have more than one corresponding aggregate. A method and apparatus for image segmentation based on motion cues. Motion provides a strong cue for segmentation. The method begins with local, ambiguous optical flow measurements. It uses a process of aggregation to resolve the ambiguities and reach reliable estimates of the motion.