Abstract: A computer that includes a processor and a memory, the memory including instructions executable by the processor to train an agent neural network to input a first state and output a first action, input the first action to an environment and determine a second state and a reward. Koopman model neural network can be trained based on the first state, the first action and the second state to determine a fake state. The agent neural network can be re-trained and the Koopman model neural network can be re-trained based on reinforcement learning including the first state, the first action, the second state, the fake state, and the reward.

Abstract: A method may include providing a data set including rows of data. The rows of data may include at least one row of unpaired modality including a first modality, and at least one row of paired modality may include both the first modality and a second modality. The method may further include imputing, by a modality-specific encoder, the at least one row of unpaired modality by interpolating embeddings from the second modality of the paired modality; training, in a latent space, the modality-specific encoder based on the imputation for unimodal prediction and bimodal prediction; and generating a confidence value for the unimodal prediction and the bimodal prediction.

Abstract: A classification system includes: one or more processors; and memory including instructions that, when executed by the one or more processors, cause the one or more processors to: calculate reference Shapley values for features of a data sample based on a first classification model; and train a second classification model though multi-task distillation to: predict Shapley values for the features of the data sample based on the reference Shapley values and a distillation loss; and predict a class label for the data sample based on the predicted Shapley values and a ground truth class label for the data sample.

Abstract: Systems and methods for automotive shape design by combining computational fluid dynamics (CFD) and Generative Adversarial Network (GAN). CFD simulations may be performed to determine aerodynamic properties and identify a set of candidate vehicle outline shapes. Vehicle shape outlines may be provided as input to a generative adversarial network (GAN) that is trained to learn aesthetic preferences for vehicle attributes. The GAN may be used to determine, by based on the vehicle outline shape, a set of vehicle attributes. The GAN may be used to generate photo-realistic images with the vehicle shape outline and filling in additional aesthetic styles for the given outline, such as different colors, lighting, visual appearance, wheel design, aspect ratio, etc.

Abstract: A system includes a computer including a processor and a memory, the memory storing instructions executable by the processor to generate a synthetic image by adjusting respective color values of one or more pixels of a reference image based on a specified meteorological optical range from a vehicle sensor to simulated fog, and input the synthetic image to a machine learning program to train the machine learning program to identify a meteorological optical range from the vehicle sensor to actual fog.

Abstract: An image including a vehicle seat and a seatbelt webbing for the vehicle seat is obtained. The image is input to a neural network trained to, upon determining a presence of an occupant in the vehicle seat, output a physical state of the occupant and a seatbelt webbing state. Respective classifications for the physical state and the seatbelt webbing state are determined. The classifications are one of preferred or nonpreferred. A vehicle component is actuated based on the classification for at least one of the physical state of the occupant or the seatbelt webbing state being nonpreferred.

Abstract: A method for training an autonomous vehicle to reach a target location. The method includes detecting the state of an autonomous vehicle in a simulated environment, and using a neural network to navigate the vehicle from an initial location to a target destination. During the training phase, a second neural network may reward the first neural network for a desired action taken by the autonomous vehicle, and may penalize the first neural network for an undesired action taken by the autonomous vehicle. A corresponding system and computer program product are also disclosed and claimed herein.

Abstract: An end-to-end deep-learning-based system that can solve both ASR and TTS problems jointly using unpaired text and audio samples is disclosed herein. An adversarially-trained approach is used to generate a more robust independent TTS neural network and an ASR neural network that can be deployed individually or simultaneously. The process for training the neural networks includes generating an audio sample from a text sample using the TTS neural network, then feeding the generated audio sample into the ASR neural network to regenerate the text. The difference between the regenerated text and the original text is used as a first loss for training the neural networks. A similar process is used for an audio sample. The difference between the regenerated audio and the original audio is used as a second loss. Text and audio discriminators are similarly used on the output of the neural network to generate additional losses for training.

Abstract: Present embodiments use deep reinforcement learning (DRL) algorithms and use one or more path planning approaches to create a path using a deep learning approach using a reinforcement learning algorithm, trained using traditional learning algorithms such as A-Star. The reinforcement learning algorithm takes in a forward-facing camera operative as part of a computer vision system for a robot, and utilizes training the algorithm to train the robot to traverse from point A to point B in an operating environment using a sequence of waypoints as a breadcrumb trail. The system trains the robot to learn the path section by section by the waypoints, which prevents requiring the robot to solve the entire path.

Abstract: A system includes a computer including a processor and a memory, the memory storing instructions executable by the processor to, generate a synthetic image by adjusting respective color values of one or more pixels of a reference image based on a specified meteorological optical range from a vehicle sensor to simulated fog, and input the synthetic image to a machine learning program to train the machine learning program to identify a meteorological optical range from the vehicle sensor to actual fog.

Abstract: An end-to-end deep-learning-based system that can solve both ASR and TTS problems jointly using unpaired text and audio samples is disclosed herein. An adversarially-trained approach is used to generate a more robust independent TTS neural network and an ASR neural network that can be deployed individually or simultaneously. The process for training the neural networks includes generating an audio sample from a text sample using the TTS neural network, then feeding the generated audio sample into the ASR neural network to regenerate the text. The difference between the regenerated text and the original text is used as a first loss for training the neural networks. A similar process is used for an audio sample. The difference between the regenerated audio and the original audio is used as a second loss. Text and audio discriminators are similarly used on the output of the neural network to generate additional losses for training.

Abstract: A computing system can be programmed to receive a spoken language command in response to emitting a spoken language cue and process the spoken language command with a generalized adversarial neural network (GAN) to determine a vehicle command. The computing system can be further programmed to operate a vehicle based on the vehicle command.

Abstract: The disclosure relates to systems, methods, and devices for determining a depth map of an environment based on a monocular image. A method for determining a depth map includes receiving a plurality of images from a monocular camera forming an image sequence. The method includes determining pose vector data for two successive images of the image sequence and providing the image sequence and the pose vector data to a generative adversarial network (GAN), wherein the GAN is trained using temporal constraints to generate a depth map for each image of the image sequence. The method includes generating a reconstructed image based on a depth map received from the GAN.

Abstract: A computing system can be programmed to receive a spoken language command in response to emitting a spoken language cue and process the spoken language command with a generalized adversarial neural network (GAN) to determine a vehicle command. The computing system can be further programmed to operate a vehicle based on the vehicle command.

Abstract: The disclosure relates to systems, methods, and devices for determining a depth map of an environment based on a monocular image. A method for determining a depth map includes receiving a plurality of images from a monocular camera forming an image sequence. The method includes determining pose vector data for two successive images of the image sequence and providing the image sequence and the pose vector data to a generative adversarial network (GAN), wherein the GAN is trained using temporal constraints to generate a depth map for each image of the image sequence. The method includes generating a reconstructed image based on a depth map received from the GAN.

Abstract: A method for training an autonomous vehicle to reach a target location. The method includes detecting the state of an autonomous vehicle in a simulated environment, and using a neural network to navigate the vehicle from an initial location to a target destination. During the training phase, a second neural network may reward the first neural network for a desired action taken by the autonomous vehicle, and may penalize the first neural network for an undesired action taken by the autonomous vehicle. A corresponding system and computer program product are also disclosed and claimed herein.

Abstract: In some embodiments, the present disclosure pertains to energy storage compositions that comprise a clay and an ionic liquid. In some embodiments, the clay is a bentonite clay and the ionic liquid is a room temperature ionic liquid (RTIL). In some embodiments, the clay and the ionic liquid are present in the energy storage compositions of the present disclosure in a weight ratio of 1:1. In some embodiments, the ionic liquid further comprises a lithium-containing salt that is dissolved in the ionic liquid. In some embodiments, the energy storage compositions of the present disclosure further comprise a thermoplastic polymer, such as polyurethane. In some embodiments, the thermoplastic polymer constitutes about 10% by weight of the energy storage composition. In some embodiments, the energy storage compositions of the present disclosure are associated with components of energy storage devices, such as electrodes and separators.