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Spinal lumbar dI2 interneurons bring about stableness associated with bipedal stepping.
The convergence of generative adversarial networks (GANs) has been studied substantially in various aspects to achieve successful generative tasks. Ever since it is first proposed, the idea has achieved many theoretical improvements by injecting an instance noise, choosing different divergences, penalizing the discriminator, and so on. In essence, these efforts are to approximate a real-world measure with an idle measure through a learning procedure. In this article, we provide an analysis of GANs in the most general setting to reveal what, in essence, should be satisfied to achieve successful convergence. This work is not trivial since handling a converging sequence of an abstract measure requires a lot more sophisticated concepts. In doing so, we find an interesting fact that the discriminator can be penalized in a more general setting than what has been implemented. Furthermore, our experiment results substantiate our theoretical argument on various generative tasks.In this article, we propose a novel model-parallel learning method, called local critic training, which trains neural networks using additional modules called local critic networks. The main network is divided into several layer groups, and each layer group is updated through error gradients estimated by the corresponding local critic network. We show that the proposed approach successfully decouples the update process of the layer groups for both convolutional neural networks (CNNs) and recurrent neural networks (RNNs). In addition, we demonstrate that the proposed method is guaranteed to converge to a critical point. We also show that trained networks by the proposed method can be used for structural optimization. Experimental results show that our method achieves satisfactory performance, reduces training time greatly, and decreases memory consumption per machine. Code is available at https//github.com/hjdw2/Local-critic-training.Neural networks are widely used as a model for classification in a large variety of tasks. Typically, a learnable transformation (i.e., the classifier) is placed at the end of such models returning a value for each class used for classification. This transformation plays an important role in determining how the generated features change during the learning process. In this work, we argue that this transformation not only can be fixed (i.e., set as nontrainable) with no loss of accuracy and with a reduction in memory usage, but it can also be used to learn stationary and maximally separated embeddings. We show that the stationarity of the embedding and its maximal separated representation can be theoretically justified by setting the weights of the fixed classifier to values taken from the coordinate vertices of the three regular polytopes available in Rd, namely, the d-Simplex, the d-Cube, and the d-Orthoplex. These regular polytopes have the maximal amount of symmetry that can be exploited to generate stationary features angularly centered around their corresponding fixed weights. Our approach improves and broadens the concept of a fixed classifier, recently proposed by Hoffer et al., to a larger class of fixed classifier models. Experimental results confirm the theoretical analysis, the generalization capability, the faster convergence, and the improved performance of the proposed method. Code will be publicly available.Perturbation has a positive effect, as it contributes to the stability of neural systems through adaptation and robustness. this website For example, deep reinforcement learning generally engages in exploratory behavior by injecting noise into the action space and network parameters. It can consistently increase the agent's exploration ability and lead to richer sets of behaviors. Evolutionary strategies also apply parameter perturbations, which makes network architecture robust and diverse. Our main concern is whether the notion of synaptic perturbation introduced in a spiking neural network (SNN) is biologically relevant or if novel frameworks and components are desired to account for the perturbation properties of artificial neural systems. In this work, we first review part of the locality-sensitive hashing (LSH) of similarity search, the FLY algorithm, as recently published in Science, and propose an improved architecture, time-shifted spiking LSH (TS-SLSH), with the consideration of temporal perturbations of the firing moments of spike pulses. Experiment results show promising performance of the proposed method and demonstrate its generality to various spiking neuron models. Therefore, we expect temporal perturbation to play an active role in SNN performance.This article studied the stability and convergence of a robust iterative learning control (ILC) design for a class of nonlinear systems with unknown control input delay. First, the iterative integral sliding mode (IISM) design was proposed, which comprised iterative actions. The iterative action made the convergence of the tracking error under the ideal sliding mode. Then, a suitable iterative update law was provided for the IISM-based robust ILC controller. The controller had the capability of both minimizing the steady tracking error and suppressing the unrepeatable disturbance. Using the controller, the closed-loop system stability was analyzed, and the stability conditions were given. Consequently, the sliding mode convergence in the iteration domain was proved by a composite energy function (CEF). In addition, by analyzing the influence of affection on the tracking error, several measures were taken to solve the chattering problem of the sliding mode control. Finally, a one-link robotic manipulator and a vertical three-tank system were used to verify the control design. The application simulations validated the performance of the proposed sliding mode iterative learning control (SMILC) design, which achieved the stability of the nonlinear system and overcame the control input time delay.An unmanned surface vehicle (USV) under complicated marine environments can hardly be modeled well such that model-based optimal control approaches become infeasible. In this article, a self-learning-based model-free solution only using input-output signals of the USV is innovatively provided. To this end, a data-driven performance-prescribed reinforcement learning control (DPRLC) scheme is created to pursue control optimality and prescribed tracking accuracy simultaneously. By devising state transformation with prescribed performance, constrained tracking errors are substantially converted into constraint-free stabilization of tracking errors with unknown dynamics. Reinforcement learning paradigm using neural network-based actor-critic learning framework is further deployed to directly optimize controller synthesis deduced from the Bellman error formulation such that transformed tracking errors evolve a data-driven optimal controller. Theoretical analysis eventually ensures that the entire DPRLC scheme can guarantee prescribed tracking accuracy, subject to optimal cost.
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