Abstract: A computerized method of cleaning a data set, configured for training of a machine learning model, includes data instances, each including a label. It comprises: computing coreset(s) from the data set, generating an importance measure for each coreset sample, indicative of an importance/weight value; selecting, from the coresets, a coreset(s) (a representative sub-set) that is representative of the data set; and selecting a number of samples, based on importance-related criteria, indicative of corresponding importance measures of the selected coreset(s) having high importance/low weight values. The set of selected samples has high probability of high importance/low weight values, compared to a probability associated with selecting data instances from the data set. The set facilitates determining whether each sample requires an action. The action gives an increased-quality representative sub-set. This facilitates quicker/higher-accuracy training of the model.

Abstract: A computerized method of cleaning a data set comprises: (a) providing the data set, configured for training of a machine learning model, and comprising data instances. Each instance comprises a label; (b) selecting a number of data instances from the set based on importance-related criteria, indicative of corresponding high importance values associated with the data instances. This generates high importance set(s), giving rise to a set of selected data instances having a high probability of high importance values, as compared to a second probability associated with selecting data instances from the data set. The selected set facilitates determining, for each data instance of, whether it requires an action. This facilitates performing the action, which brings about increased quality data set(s), which facilitate quicker and/or higher-accuracy training of the machine learning model, as compared to a second training of the machine learning model performed utilizing the data set.

Abstract: A method of machine learning model training comprises: (a) providing a data set, configured for model training, comprising data sub-sets, each comprising data instances; (b) providing a coreset tree, computed from the data set, comprising coresets, wherein each leaf coreset based on a corresponding data sub-set; (c) removing data instance(s) from data sub-set(s), generating reduced sub-set(s); (d) recomputing coreset(s) that is an ancestor of the reduced sub-set(s), to reflect removal of instance(s), giving a modified coreset tree; (e) selecting, from the modified tree, coresets that are representative of the data set exclusive of the removed instance(s); (f) training the model utilizing the selected coresets, giving a trained model; (g) performing prediction(s) for the removed instance(s), utilizing the model; (g) evaluating model predictability metric(s), associated with the prediction(s), indicative of the model's ability to correctly predict the removed instance(s).

Abstract: An image classification neural network is trained based on images that are the presented to an observer as a visual stimulus while collecting neurophysiological signals from a brain of the observer. The neurophysiological signals are processes to identify a neurophysiological event indicative of a detection of a target by the observer in one or more of the images, and the image classification neural network is trained to identify the target in the image based on the identification of the neurophysiological event.

Abstract: A method of training an image classification neural network comprises: presenting a first plurality of images to an observer as a visual stimulus, while collecting neurophysiological signals from a brain of the observer; processing the neurophysiological signals to identify a neurophysiological event indicative of a detection of a target by the observer in at least one image of the first plurality of images; training the image classification neural network to identify the target in the image, based on the identification of the neurophysiological event; and storing the trained image classification neural network in a computer-readable storage medium.

Abstract: A method of training an image classification neural network comprises: presenting a first plurality of images to an observer as a visual stimulus, while collecting neurophysiological signals from a brain of the observer; processing the neurophysiological signals to identify a neurophysiological event indicative of a detection of a target by the observer in at least one image of the first plurality of images; training the image classification neural network to identify the target in the image, based on the identification of the neurophysiological event; and storing the trained image classification neural network in a computer-readable storage medium.

Abstract: A method of classifying an image is disclosed. The method comprises: applying a computer vision procedure to the image to detect therein candidate image regions suspected as being occupied by a target; presenting to an observer each candidate image region as a visual stimulus, while collecting neurophysiological signals from a brain of the observer; processing the neurophysiological signals to identify a neurophysiological event indicative of a detection of the target by the observer; and determining an existence of the target in the image is based, at least in part, on the identification of the neurophysiological event.

Abstract: A method of classifying an image is disclosed. The method comprises: applying a computer vision procedure to the image to detect therein candidate image regions suspected as being occupied by a target; presenting to an observer each candidate image region as a visual stimulus, while collecting neurophysiological signals from a brain of the observer; processing the neurophysiological signals to identify a neurophysiological event indicative of a detection of the target by the observer; and determining an existence of the target in the image is based, at least in part, on the identification of the neurophysiological event.

Abstract: A method of classifying an image is disclosed. The method comprises: applying a computer vision procedure to the image to detect therein candidate image regions suspected as being occupied by a target; presenting to an observer each candidate image region as a visual stimulus, while collecting neurophysiological signals from a brain of the observer; processing the neurophysiological signals to identify a neurophysiological event indicative of a detection of the target by the observer; and determining an existence of the target in the image is based, at least in part, on the identification of the neurophysiological event.

Abstract: A method of classifying an image is disclosed. The method comprises: applying a computer vision procedure to the image to detect therein candidate image regions suspected as being occupied by a target; presenting to an observer each candidate image region as a visual stimulus, while collecting neurophysiological signals from a brain of the observer; processing the neurophysiological signals to identify a neurophysiological event indicative of a detection of the target by the observer; and determining an existence of the target in the image is based, at least in part, on the identification of the neurophysiological event.