Abstract: Techniques to generate driving scenarios for autonomous vehicles characterize a path in a driving scenario according to metrics such as narrowness and effort. Nodes of the path are assigned a time for action to avoid collision from the node. The generated scenarios may be simulated in a computer.

Abstract: The disclosure provides a learning framework that unifies both semantic segmentation and semantic edge detection. A learnable recurrent message passing layer is disclosed where semantic edges are considered as explicitly learned gating signals to refine segmentation and improve dense prediction quality by finding compact structures for message paths. The disclosure includes a method for coupled segmentation and edge learning. In one example, the method includes: (1) receiving an input image, (2) generating, from the input image, a semantic feature map, an affinity map, and a semantic edge map from a single backbone network of a convolutional neural network (CNN), and (3) producing a refined semantic feature map by smoothing pixels of the semantic feature map using spatial propagation, and controlling the smoothing using both affinity values from the affinity map and edge values from the semantic edge map.

Abstract: Apparatuses, systems, and techniques are presented to generate one or more images comprising one or more objects based, at least in part, on one or more dynamically configurable attributes of the one or objects. In at least one embodiment, one or more images comprising one or more objects can be generated based, at least in part, on one or more dynamically configurable attributes of the one or objects.

Abstract: Techniques to generate driving scenarios for autonomous vehicles characterize a path in a driving scenario according to metrics such as narrowness and effort. Nodes of the path are assigned a time for action to avoid collision from the node. The generated scenarios may be simulated in a computer.

Abstract: Apparatuses, systems, and techniques generate poses of an object based on data of the object observed from a first viewpoint and a second viewpoint. The poses can be evaluated to determine a portion of the data usable by an estimator to generate a pose of the object.

Abstract: Apparatuses, systems, and techniques generate poses of an object based on image data of the object obtained from a first viewpoint of the object and a second viewpoint of the object. The poses can be evaluated to determine a portion of the image data usable by an estimator to generate a pose of the object.

Abstract: An end-to-end low-precision training system based on a multi-base logarithmic number system and a multiplicative weight update algorithm. The multi-base logarithmic number system is applied to update weights of the neural network, with different bases of the multi-base logarithmic number system utilized between calculation of weight updates, calculation of feed-forward signals, and calculation of feedback signals. The LNS expresses a high dynamic range and computational energy efficiency, making it advantageous for on-board training in energy-constrained edge devices.

Abstract: Apparatuses, systems, and techniques to train one or more neural networks. In at least one embodiment, one or more neural networks are trained to perform segmentation tasks based at least in part on training data comprising bounding box annotations.

Abstract: Techniques to characterize driving scenarios for autonomous vehicles characterize a path in a driving scenario according to metrics such as narrowness and effort. The scenarios may be characterized using a tree-based or tensor-based approach.

Abstract: One embodiment of a method for performing a task includes generating a first posterior distribution of a global latent context variable for the task based on a pool of contexts sampled from one or more previous episodes of the task. The method also includes generating a second posterior distribution of a local latent context variable for a current time step in a current episode of the task based on one or more recent contexts sampled at one or more previous time steps of the current episode. The method further includes causing an agent to perform an action related to carrying out the task based on the first posterior distribution, the second posterior distribution, and a current state associated with the current time step.

Abstract: Apparatuses, systems, and techniques used to train one or more neural networks to generate images comprising one or more features. In at least one embodiment, one or more neural networks are trained to determine one or more styles for an input image and then generate features associated with said one or more styles in an output image.

Abstract: Techniques to generate driving scenarios for autonomous vehicles characterize a path in a driving scenario according to metrics such as narrowness and effort. Nodes of the path are assigned a time for action to avoid collision from the node. The generated scenarios may be simulated in a computer.

Abstract: Techniques to characterize driving scenarios for autonomous vehicles characterize a path in a driving scenario according to metrics such as narrowness and effort. The scenarios may be characterized using a tree-based or tensor-based approach.

Abstract: Apparatuses, systems, and techniques to train one or more neural networks. In at least one embodiment, one or more neural networks are trained based, at least in part, on inferencing output from one or more second neural networks.

Abstract: Systems and methods for determining molecular structures based on molecular-orbital-based (MOB) features are described. MOB features can be utilized in combination with machine-learning methods to predict accurate properties, such as quantum mechanical energy, of molecular systems.