Abstract: A computerized method has the steps of: generating an input computational graph (CG) for representing a neural architecture, and performing a plurality of optimization steps for at least one iteration to obtain a modified neural architecture represented by a modified CG, for obtaining a neural network for use in one or more computing devices. The optimization steps include: identifying one or more subgraphs from the input CG, obtaining the modified CG by replacing the identified one or more subgraphs with one or more replacement subgraphs, respectively, evaluating one or more metrics of a modified neural-network architecture represented by the modified CG, and based on the evaluation, selecting the modified CG or the input CG. When the optimization steps are performed for multiple iterations, the selected CG obtained in one iteration is used as the input CG for a next iteration.

Abstract: A battery cell includes a housing, an electrode assembly, and a current collector member, where the housing has an electrode lead-out member. The electrode assembly is accommodated in the housing and includes tabs. The current collector member is configured to electrically connect the tabs to the electrode lead-out member, where the current collector member is welded to the electrode lead-out member, the current collector member includes a conductive protective layer, and the conductive protective layer is disposed on a side of the current collector member facing away from the electrode lead-out member.

Abstract: A method and system for generating neural architectures to perform a particular task. An actor neural network, as part of a continuous action reinforcement learning (RL) agent, generates a randomized continuous actions parameters to encourage exploration of a search space to generate candidate architectures without bias. The continuous action parameters are discretized and applied to a search space to generate candidate architectures, the performance of which for performing the particular task is evaluated. Corresponding reward and state are determined based on the performance. A critic neural network, as part of the continuous action RL agent, learns a mapping of the continuous action to a reward using modified Deep Deterministic Policy Gradient (DDPG) with quantile loss function by sampling a list of top performing architectures. The actor neural network is updated with the learned mapping.

Abstract: A method and system for neural architectural search (NAS) for performing a task. A generative adversarial network comprising a generator and a discriminator receives, from a user device, a query for neural network architecture, the query including a search space. The generator of the generative adversarial network generates a plurality of generated neural network architectures responsive to the received search space. The discriminator of the generative adversarial network selects an optimal neural network architecture from among the plurality of generated neural network architectures. The optimal generated neural network architecture is transmitted to the user device.

Abstract: A method and system for generating neural architectures to perform a particular task. An actor neural network, as part of a continuous action reinforcement learning (RL) agent, generates a randomized continuous actions parameters to encourage exploration of a search space to generate candidate architectures without bias. The continuous action parameters are discretized and applied to a search space to generate candidate architectures, the performance of which for performing the particular task is evaluated. Corresponding reward and state are determined based on the performance. A critic neural network, as part of the continuous action RL agent, learns a mapping of the continuous action to a reward using modified Deep Deterministic Policy Gradient (DDPG) with quantile loss function by sampling a list of top performing architectures. The actor neural network is updated with the learned mapping.