Abstract: Certain aspects of the present disclosure provide techniques and apparatus for performing federated learning. One example method generally includes sending model update data to a server, generating training metadata using a trained local machine learning model and local validation data, and sending the training metadata to the server. The trained local machine learning model generally incorporates the model update data and global model data defining a global machine learning model, and the training metadata generally includes data bout the trained local machine learning model used to determine when to discontinue federated learning operations for training the global machine learning model. Another example method generally includes sending a global model to a federated learning client device and receiving training metadata from the federated learning client device.

Abstract: A machine learning model is configured to detect objects from video images. A system monitors video images to identify particular objects. A deep learning process is utilized to learn a baseline pattern. A change due to movement within a field of view is autonomously detected using the deep learning processing. An action is performed based on the detected change.

Abstract: A machine learning model is configured to detect objects from video images. A system monitors video images to identify particular objects. A deep learning process is utilized to learn a baseline pattern. A change due to movement within a field of view is autonomously detected using the deep learning processing. An action is performed based on the detected change.

Abstract: Multi-label classification is improved by determining thresholds and/or scale factors. Selecting thresholds for multi-label classification includes sorting a set of label scores associated with a first label to create an ordered list. Precision and recall values are calculated corresponding to a set of candidate thresholds from score values. The threshold is selected from the candidate thresholds for the first label based on target precision values or recall values. A scale factor is also selected for an activation function for multi-label classification where a metric of scores within a range is calculated. The scale factor is adjusted when the metric of scores are not within the range.

Abstract: A method for managing synapse plasticity in a neural network includes converting a first set of synapses from a plastic synapse type to a fixed synapse type. The method may also include converting a second set of synapses from the fixed synapse type to the plastic synapse type.

Abstract: A method for managing synapse plasticity in a neural network includes converting a first set of synapses from a plastic synapse type to a fixed synapse type. The method may also include converting a second set of synapses from the fixed synapse type to the plastic synapse type.

Abstract: Aspects of the present disclosure provide methods and apparatus for remotely controlling and monitoring neural model execution (e.g., such as execution of the neural models described above) remotely, such as via the Internet. According to certain aspects, a client at a remote location (e.g., a webclient), may establish a connection with a server on which the neural model is running (or at least capable of controlling and monitoring the execution).