Abstract: A method for data-center management includes, in a data center including multiple components, monitoring a plurality of performance measures of the components. A set of composite metrics is automatically defined, each composite metric including a respective weighted combination of two or more performance measures from among the performance measures. Baseline values are established for the composite metrics. An anomalous deviation is detected of one or more of the composite metrics from the respective baseline values.

Abstract: A network device, system-on-a-chip, and method of performing packet handling are described. A packet is received, and data associated with the packet is processed, using a configurable artificial intelligence engine, to generate a size classification for a flow associated with the packet. An action is performed based, at least in part, on the size classification for the flow associated with the packet.

Abstract: Apparatuses, systems, and techniques of using one or more circuits (e.g., of a network interface) to obtain assembly code for one or more machine code segments loaded and/or injected into a process, and determine whether the assembly code is likely to perform at least one unauthorized task.

Abstract: A system, method, and apparatus of power management for computing systems are included herein that optimize individual frequencies of components of the computing systems using machine learning. The computing systems can be tightly integrated systems that consider an overall operating budget that is shared between the components of the computing system while adjusting the frequencies of the individual components. An example of an automated method of power management includes: (1) learning, using a power management (PM) agent, frequency settings for different components of a computing system during execution of a repetitive application, and (2) adjusting the frequency settings of the different components using the PM agent, wherein the adjusting is based on the repetitive application and one or more limitations corresponding to a shared operating budget for the computing system.

Abstract: In various examples, a motion planner include an analytical function to predict motion plans for a machine based on predicted trajectories of actors in an environment, where the predictions are differentiable with respect to parameters of a neural network of a motion predictor used to predict the trajectories. The analytical function may be used to determine candidate trajectories for the machine based on a predicted trajectory, to compute cost values for the candidate trajectories, and to select a reference trajectory from the candidate trajectories. For differentiability, a term of the analytical function may correspond to the predicted trajectory. A motion controller may use the reference trajectory to predict a control sequence for the machine using an analytical function trained to generate predictions that are differentiable with respect to at least one parameter of the analytical function used to compute the cost values.

Abstract: In various embodiments, a congestion control modelling application automatically controls congestion in data transmission networks. The congestion control modelling application executes a trained neural network in conjunction with a simulated data transmission network to generate a training dataset. The trained neural network has been trained to control congestion in the simulated data transmission network. The congestion control modelling application generates a first trained decision tree model based on an initial loss for an initial model relative to the training dataset. The congestion control modelling application generates a final tree-based model based on the first trained decision tree model and at least a second trained decision tree model. The congestion control modelling application executes the final tree-based model in conjunction with a data transmission network to control congestion within the data transmission network.

Abstract: A method is performed by an agent operating in an environment. The method comprises computing a first value associated with each state of a number of states in the environment, determining a lookahead horizon for each state of the number of states in the environment based on the computed first value for each state of the number of states, applying a first policy to compute a second value associated with each state of at least one state in the number of states in the environment for the at least one state in the number of states based on the determined lookahead horizons for the number of states, and determining a second policy based on the first policy and the second value for each state of the number of states in the environment.

Abstract: A method for contracting a tensor network is provided. The method comprises generating a graph representation of the tensor network, processing the graph representation to determine a contraction for the tensor network by an agent that implements a reinforcement learning algorithm, and processing the tensor network in accordance with the contraction to generate a contracted tensor network.

Abstract: System and method for detecting cable anomalies including collecting a first set cable measurement data. The first set of cable measurement data may be used to create a model including one or more groups based on the collected first set of cable measurement data. Collecting a second set of cable measurement data and determine a probability of anomaly for cable measurement data of the second set of cable measurement data, the probability of anomaly based on the deviation of the cable measurement data from one or more groups of the model.

Abstract: Introduced herein is a technique that uses ML to autonomously find a cache management policy that achieves an optimal execution of a given workload of an application. Leveraging ML such as reinforcement learning, the technique trains an agent in an ML environment over multiple episodes of a stabilization process. For each time step in these training episodes, the agent executes the application while making an incremental change to the current policy, i.e., cache-residency statuses of memory address space associated with the workload, until the application can be executed at a stable level. The stable level of execution, for example, can be indicated by performance variations, such as standard deviations, between a certain number of neighboring measurement periods remaining within a certain threshold. The agent, who has been trained in the training episodes, infers the final cache management policy during the final, inferring episode.

Abstract: A system, method, and apparatus of power management for computing systems are included herein that optimize individual frequencies of components of the computing systems using machine learning. The computing systems can be tightly integrated systems that consider an overall operating budget that is shared between the components of the computing system while adjusting the frequencies of the individual components. An example of an automated method of power management includes: (1) learning, using a power management (PM) agent, frequency settings for different components of a computing system during execution of a repetitive application, and (2) adjusting the frequency settings of the different components using the PM agent, wherein the adjusting is based on the repetitive application and one or more limitations corresponding to a shared operating budget for the computing system.

Abstract: A reinforcement learning agent learns a congestion control policy using a deep neural network and a distributed training component. The training component enables the agent to interact with a vast set of environments in parallel. These environments simulate real world benchmarks and real hardware. During a learning process, the agent learns how maximize an objective function. A simulator may enable parallel interaction with various scenarios. As the trained agent encounters a diverse set of problems it is more likely to generalize well to new and unseen environments. In addition, an operating point can be selected during training which may enable configuration of the required behavior of the agent.

Abstract: A method for performing a Tree-Search (TS) on an environment is provided. The method comprises generating a tree for a current state of the environment based on a TS policy, determining a corrected TS policy, and determining an action to apply to the environment based on the corrected TS policy. The tree comprises a plurality of nodes including a root node among the plurality of nodes corresponding to the current state of the environment. Each node other than the root node among the plurality of nodes corresponding to an estimated future state of the environment. The plurality of nodes in the tree are connected by a plurality of edges. Each edge among the plurality of edges is associated with an action causing a transition from a first state to a different sate of the environment.

Abstract: A method for data-center management includes, in a data center including multiple components, monitoring a plurality of performance measures of the components. A set of composite metrics is automatically defined, each composite metric including a respective weighted combination of two or more performance measures from among the performance measures. Baseline values are established for the composite metrics. An anomalous deviation is detected of one or more of the composite metrics from the respective baseline values.

Abstract: A reinforcement learning agent learns a congestion control policy using a deep neural network and a distributed training component. The training component enables the agent to interact with a vast set of environments in parallel. These environments simulate real world benchmarks and real hardware. During a learning process, the agent learns how maximize an objective function. A simulator may enable parallel interaction with various scenarios. As the trained agent encounters a diverse set of problems it is more likely to generalize well to new and unseen environments. In addition, an operating point can be selected during training which may enable configuration of the required behavior of the agent.

Abstract: The method of monitoring an operation includes acquiring data from sensors including images of a workspace in which the operation is to be performed, identifying a human operator and a controlled element within the workspace using the acquired images, determining whether the operation has initiated based on a known activation trigger, estimating pose of the human operator using the images, monitoring state of the controlled element based on acquired data, and determining whether an abnormality occurred based on the estimated pose, the state of the controlled element, a duration of the operation, or a combination thereof.

Abstract: The method of monitoring an operation includes acquiring data from sensors including images of a workspace in which the operation is to be performed, identifying a human operator and a controlled element within the workspace using the acquired images, determining whether the operation has initiated based on a known activation trigger, estimating pose of the human operator using the images, monitoring state of the controlled element based on acquired data, and determining whether an abnormality occurred based on the estimated pose, the state of the controlled element, a duration of the operation, or a combination thereof.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of training a source classifier with labeled source training data of a first product category from a website of an online retailer, clustering target data for a second product category into a plurality of clusters, inserting into each cluster labeled source training data of the first product category, assigning a domain discriminator score to each cluster, determining whether each cluster comprises an agreement cluster or a disagreement cluster using the domain discriminator score, receiving a product search request for a product of the second category from a user of the web site, and coordinating a display of the product on the web site to promote the product.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of receiving an online search query entered into a search field of an online ecommerce website by a user using the online ecommerce website, determining a query response to the online search query by combining a nonparametric bootstrap distribution and a mixture sequential probability ratio test, the query response comprising one or more products, and coordinating a display of the query response to the user using the online ecommerce website. The query response can be based on one of a query success rate per user session of a plurality of previous user sessions or a revenue per user session of the plurality of previous user sessions.

Abstract: A multi-modal computer classification network system for use in classifying data records is described herein. The system includes a memory device, a first classification computer server, a second classification computer server, and a policy computer server. The memory device includes an item records database and a labeling database. The first classification computer server includes a first classifier program that is configured to select an item record from the item database and generate a first classification record including a first ranked list of class labels. The second classification computer server includes a second classifier program that is configured to generate a second classification record including a second ranked list of class labels. The policy computer server includes a policy network that is programmed to determine a predicted class label based on the first and second ranked lists of class labels.