Abstract: Systems and methods are provided for distributed video storage and search with edge computing. The method may comprise caching a first portion of data on a first device. The method may further comprise determining, at a second device, whether the first device has the first portion of data. The determining may be based on whether the first piece of data satisfies a specified criterion. The method may further comprise sending the data, or a portion of the data, and/or a representation of the data from the first device to a third device.

Abstract: Hyper-parameters are selected for training a deep convolutional network by selecting a number of network architectures as part of a database. Each of the network architectures includes one or more local logistic regression layer and is trained to generate a corresponding validation error that is stored in the database. A threshold error for identifying a good set of network architectures and a bad set of network architectures may be estimated based on validation errors in the database. The method also includes choosing a next potential hyper-parameter, corresponding to a next network architecture, based on a metric that is a function of the good set of network architectures. The method further includes selecting a network architecture, from among next network architectures, with a lowest validation error.

Abstract: A method for selecting bit widths for a fixed point machine learning model includes evaluating a sensitivity of model accuracy to bit widths at each computational stage of the model. The method also includes selecting a bit width for parameters, and/or intermediate calculations in the computational stages of the mode. The bit width for the parameters and the bit width for the intermediate calculations may be different. The selected bit width may be determined based on the sensitivity evaluation.

Abstract: Compressing a machine learning network, such as a neural network, includes replacing one layer in the neural network with compressed layers to produce the compressed network. The compressed network may be fine-tuned by updating weight values in the compressed layer(s).

Abstract: Systems and methods are provided for embedded data inference. The systems and methods may process camera and other sensor data in by leveraging processing and storage capacity of one or more devices nearby or in the cloud to augment or update the sensor processing of an embedded device. The joint processing may be used in stationary cameras or in vehicular systems such as cars and drones, and may improve crop assessments, navigation, and safety.

Abstract: A method of online training of a classifier includes determining a distance from one or more feature vectors of an object to a first predetermined decision boundary established during off-line training for the classifier. The method also includes updating a decision rule as a function of the distance. The method further includes classifying a future example based on the updated decision rule.

Abstract: The present disclosure provides systems and methods for mapping a determined path of travel. The path of travel may be mapped to a camera view of a camera affixed to a vehicle. In some embodiments, the path of travel may be mapped to another view that is based on a camera, such as a bird's eye view anchored to the camera's position at a given time. These systems and methods may determine the path of travel by incorporating data from later points in time.

Abstract: Systems and methods are provided for distributed video storage and search over edge computing devices having a short-term memory and a long-term memory. The method may comprise caching a first portion of data on a first device. The method may further comprise determining, at a second device, whether the first device has the first portion of data. The determining may be based on whether the first piece of data satisfies a specified criterion. The method may further comprise sending the data, or a portion of the data, and/or a representation of the data from the first device to a third device.

Abstract: Methods and apparatus are provided for processing in an artificial nervous system. According to certain aspects, resolution of one or more functions performed by processing units of a neuron model may be reduced, based at least in part on availability of computational resources or a power target or budget. The reduction in resolution may be compensated for by adjusting one or more network weights.

Abstract: Systems, devices, and methods are provided for distributed search with edge computing. Enabled systems and devices may overcome challenges associated with searching data captured by one or more connected devices, including privacy, security, bandwidth, backhaul, and memory storage.

Abstract: A method for image processing includes determining features of multiple stored images from a pre-trained deep convolutional network. The method also includes clustering each image of the multiple stored images based on the determined features.

Abstract: Various operations may be performed based on a distance-related function associated with two or more devices. For example, an association procedure for two or more devices may be based on one or more determined distances. Similarly, presence management may be based on one or more determined distances. A distance-related function may take various form including, for example, a distance between devices, two or more distances between devices, a rate of change in a relative distance between devices, relative acceleration between devices, or some combination of two or more of the these distance-related functions.

Abstract: Certain aspects of the present disclosure support assigning neurons and/or synapses to group tags where group tags have an associated set of parameters. By using group tags, neurons or synapses in a population can be assigned a group tag. Then, by changing a parameter associated with the group tag, all synapses or neurons in the group may have that parameter changed.

Abstract: A data source sends a synchronization signal and information to several data sinks that use the synchronization signal and a specified parameter to determine when to process the information. The data source and data sinks may comprise wireless nodes such as, for example, a wireless audio source that sends multi-channel data to a pair of wireless earpieces. The wireless earpieces use the synchronization signal and a latency interval to determine the appropriate time to output audio based on the audio channels.

Abstract: Systems and methods provide, implement, and use using a computer-vision based methods of context-sensitive monitoring and characterization of driver behavior. Additional systems and methods are provided for unsupervised learning of action values, monitoring of a driver's environment, and transmitting visual information from a client to a server.

Abstract: A method of training for image classification includes labelling a crop from an image including an object of interest. The crop may be labelled with an indication of whether the object of interest is framed, partially framed or not present in the crop. The method may also include assigning a fully framed class to the labelled crop, including the object of interest, if the object of interest is framed. A labelled crop may be assigned a partially framed class if the object of interest is partially framed. A background class may be assigned to a labelled crop if the object of interest is not present in the crop.

Abstract: Systems and methods for controlling a delay associated with paging a remote device. In one implementation, a paging device arranges page operations such that if one of two consecutive page scans performed by a remote device occurs during a response scan, the other page scan occurs during the transmission of a page. In another implementation, a device scanning for a page arranges page scans such that if one of two consecutive page scans occurs during a page response scan, the other page scan occurs during the transmission of a page. In another implementation, a device scanning for a page performs a series of sets of separate page scans, wherein the time interval between two of separate scans in a set is less than the time interval between adjacent sets. The page scans in a set are configured to ensure that one of them occurs during the transmission of a page.

Abstract: The balance of training data between classes for a machine learning model is modified. Adjustments are made at the gradient stage where selective backpropagation is utilized to modify a cost function to adjust or selectively apply the gradient based on the class example frequency in the data sets. The factor for modifying the gradient may be determined based on a ratio of the number of examples of the class with a fewest members to the number of examples of a present class. The gradient associated with the present class is modified based on the above determined factor.

Abstract: Various operations may be performed based on a distance-related function associated with two or more devices. For example, an association procedure for two or more devices may be based on one or more determined distances. Similarly, presence management may be based on one or more determined distances. A distance-related function may take various form including, for example, a distance between devices, two or more distances between devices, a rate of change in a relative distance between devices, relative acceleration between devices, or some combination of two or more of the these distance-related functions.