Abstract: One embodiment of a method for controlling a character includes receiving a state of the character, a path to follow, and first information about a scene, generating, via a trained machine learning model and based on the state of the character, the path, and the first information, a first action for the character to perform, wherein the first action comprises a first type of motion included in a plurality of types of motions for which the trained machine learning model is trained to generate actions, and causing the character to perform the first action.

Abstract: One embodiment of a method for controlling a character includes receiving a state of the character, a path to follow, and first information about a scene, generating, via a trained machine learning model and based on the state of the character, the path, and the first information, a first action for the character to perform, wherein the first action comprises a first type of motion included in a plurality of types of motions for which the trained machine learning model is trained to generate actions, and causing the character to perform the first action.

Abstract: In one embodiment, a method includes receiving data of a set of configurations of preprocessor engines, receiving measurements of performance of a device executing benchmark applications while changing a configuration of preprocessor engines selected from the set of configurations of preprocessor engines, defining an order of at least some of the configurations based on the measurements, and providing a pruned set of configurations based on the defined order of the at least some configurations.

Abstract: In one embodiment, a system includes a processor to control a resource according to policies selected by a multi-armed bandit machine learning agent in exploration phases and in exploitation phases, and execute the multi-armed bandit machine learning agent to select from the policies to control the resource in the exploration phases according to probabilities to explore corresponding one of the policies, wherein the probabilities include different probabilities, perform measurements on the system during execution of the multi-armed bandit machine learning agent, and execute the multi-armed bandit machine learning agent to select from the policies to maximize potential rewards from controlling the resource in exploitation phases based on the performed measurements, and a memory to store data used by the processor.

Abstract: In one embodiment, a method includes finding an impact on performance of a device from changing settings of preprocessor engines applied to benchmark applications being executed by the device, defining groups of the preprocessor engines responsively to the impact on the performance of the device from changing the settings of the preprocessor engines, and providing different preprocessor engine configurations based on the settings to be applied to the preprocessor engines such that for each one of the defined groups a respective setting is to be applied equally to the preprocessor engines of the one group, thereby reducing a number of the preprocessor engine configurations available for selection by a machine learning agent.

Abstract: In one embodiment, a system includes a processor to receive machine learning training data including label scores based on measurements of device performance during execution of benchmark applications for different prefetcher engine configurations, and corresponding device hardware states, and train configuration specific machine learning regression models based on the received machine learning training data to provide corresponding configuration specific device performance predictions based on given device hardware states, and a memory to store data used by the processor.

Abstract: In one embodiment, a system includes prefetcher engines to predict next memory access addresses of a memory from which to load data to a cache during execution of a software application, and load the data from the predicted next memory access addresses to the cache during execution of the software application, and a processor to control the prefetcher engines according to configurations of the prefetcher engines selected by a machine learning agent in exploration phases and in exploitation phases during execution of the software application, and execute the machine learning agent to select from a pruned set of configurations to control the prefetcher engines in the exploration phases, perform measurements on the system during execution of the machine learning agent, and execute the machine learning agent to select from the configurations to maximize potential rewards from controlling the prefetcher engines in the exploitation phases based on the performed measurements.

Abstract: A system includes a processing device to receive a video content, a quality metric, and a target bit rate for encoding the video content. The system includes encoding hardware to perform frame encoding on the video content and a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the quality metric and the target bit rate. The controller is further programmed to provide the first QP values to the encoding hardware for rate control of the frame encoding.

Abstract: A technique for solving combinatorial problems, such as vehicle routing for multiple vehicles integrates evolutionary algorithms and reinforcement learning. A genetic algorithm maintains a set of solutions for the problem and improves the solutions using mutation (modify a solution) and crossover (combine two solutions). The best solution is selected from the improved set of solutions. A system that integrates evolutionary algorithms, such as a genetic algorithm, and reinforcement learning comprises two components. A first component is a beam search technique for generating solutions using a reinforcement learning model. A second component augments a genetic algorithm using learning-based solutions that are generated by the reinforcement learning model. The learning-based solutions improve the diversity of the set which, in turn, improves the quality of the solutions computed by the genetic algorithm.

Abstract: Apparatuses, systems, and techniques to identify one or more modifications to objects within an environment. In at least one embodiment, objects are identified in an image, based on extracted feedback information, using one or more machine learning models, for example, using direct and/or implicit feedback of user interaction with one or more objects in an environment.

Abstract: One embodiment of a method for controlling a system includes generating a plurality of initializations using a trained machine learning model, performing a plurality of instances of an iterative technique based on the plurality of initializations to generate a plurality of results, generating a control signal based on one or more results included in the plurality of results, and transmitting the control signal to the system to cause the system to perform one or more operations.

Abstract: A network monitor may execute, or communicate with, one or more stored machine learning models that are trained to predict a failure probability for one or more ports and/or links within a network fabric. Systems and methods may monitor a set of ports and/or links to generate predictions for failure probabilities using a first trained model and low frequency telemetry data. For a subset of ports and/or links with failure probabilities exceeding a first threshold, high speed telemetry data may be used by a second trained model to generate predictions for failure probabilities for the subset of ports. Suspicious ports may then be isolated and undergo various remediation and/or monitoring actions prior to de-isolating the isolated ports.

Abstract: A conditional adversarial latent model (CALM) process can be used to generate reference motions from a set of original reference movements to create a library of new movements for an agent. The agent can be a virtual representation various types of characters, animals, or objects. The CALM process can receive a set of reference movements and a requested movement. An encoder can be used to map the requested movement onto a latent space. A low-level policy can be employed to produce a series of latent space joint movements for the agent. A conditional discriminator can be used to provide feedback to the low-level policy to produce stationary distributions over the states of the agent. A high-level policy can be employed to provide a macro movement control over the low-level policy movements, such as providing direction in the environment. The high-level policy can utilize a reward or a finite-state machine function.

Abstract: A system includes a processing device to receive video content and output encoded video of the video content for a client video device. The system includes a controller coupled to the processing device, the controller programmed with machine instructions to receive, from a video encoder while encoding the video content, frame statistics based on one or more encoded frames of the video content corresponding to a current frame. The machine instructions further generate a first quantization parameter (QP) value for the current frame using a frame machine learning model, wherein the frame machine learning model includes states that depend on the frame statistics. The machine instructions further provide the first QP value to the video encoder for rate control of the frame encoding of the current frame.

Abstract: A system includes a processing device to receive video content, metadata related to the video content, and a target bit rate for encoding the video content. The processing device further detects a content type of the video content based on the metadata and encodes hardware to perform frame encoding on the video content. The system further includes a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the content type and the target bit rate. The controller further provides the first QP values to the encoding hardware for rate control of the frame encoding.

Abstract: A system includes a processing device to receive a video content, a quality metric, and a target bit rate for encoding the video content. The system includes encoding hardware to perform frame encoding on the video content and a controller coupled between the processing device and the encoding hardware. The controller is programmed with machine instructions to generate first QP values on a per-frame basis using a frame machine learning model with a first plurality of weights. The first plurality of weights depends at least in part on the quality metric and the target bit rate. The controller is further programmed to provide the first QP values to the encoding hardware for rate control of the frame encoding.

Abstract: A system to generate a latent space model of a scene or video and apply this latent space and candidate sentences formed from digital audio to a vision-language matching model to enhance the accuracy of speech-to-text conversion. A latent space embedding of the scene is generated in which similar features are represented in the space closer to one another. An embedding for the digital audio is also generated. The vision-language matching model utilizes the latent space embedding to enhance the accuracy of transcribing/interpreting the embedding of the digital audio.

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.