As electric vehicles have gone mainstream, buyers are facing a smorgasbord of options, and Tesla--once untouchable--is no longer the dominant force. Last year was a tough one for Elon Musk's auto brand: Sales efforts faltered, and the company lost its title of world's largest EV maker to China's BYD . Today, it feels like all automakers-- including luxury brands --are racing to release their own EVs. But at the same time, some companies are scaling back production plans . So where is the market headed?

Huber's contaminated heavy-tailed rewards and meanwhile needs to ensure differential privacy.

Huber's contaminated heavy-tailed rewards and meanwhile needs to ensure differential privacy.

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We mitigate the usual worst-case nature of minimax analysis by showing that our bounds are tight for any given31 hypothesis class, and, tight in any noise regime (Theorems 1 and 2).

This approach has made variational inference applicable to alarge class ofcomplexgenerativemodels. However,manychallenges remain.

Inferring the synaptic plasticity rules that govern learning in the brain is a key challenge in neuroscience. We present a novel computational method to infer these rules from experimental data, applicable to both neural and behavioral data. Our approach approximates plasticity rules using a parameterized function, employing either truncated Taylor series for theoretical interpretability or multilayer percep-trons. These plasticity parameters are optimized via gradient descent over entire trajectories to align closely with observed neural activity or behavioral learning dynamics. This method can uncover complex rules that induce long nonlinear time dependencies, particularly involving factors like postsynaptic activity and current synaptic weights. We validate our approach through simulations, successfully recovering established rules such as Oja's, as well as more intricate plasticity rules with reward-modulated terms. We assess the robustness of our technique to noise and apply it to behavioral data from Drosophila in a probabilistic reward-learning experiment. Notably, our findings reveal an active forgetting component in reward learning in flies, improving predictive accuracy over previous models. This modeling framework offers a promising new avenue for elucidating the computational principles of synaptic plasticity and learning in the brain.

Knowledge distillation is effective to train the small and generalisable network models for meeting the low-memory and fast running requirements. Existing offline distillation methods rely on a strong pre-trained teacher, which enables favourable knowledge discovery and transfer but requires a complex two-phase training procedure. Online counterparts address this limitation at the price of lacking a high-capacity teacher. In this work, we present an On-the-fly Native Ensemble (ONE) learning strategyforone-stage online distillation.

In this paper, we aim to learn alow-dimensional Euclidean representation from aset of constraints of the form "itemj is closer to itemithan itemk".

This section consists of three parts, with each subsequent part building upon the previous one. Appendix A.1 covers the fundamentals of RL, where the actor-critic method is introduced. Appendix A.2 describes the RL algorithm for a single fulfillment agent, which is the proximal policy Appendix A.3 presents the MARL algorithm for the Currently, policy-based methods [Deisenroth et al., 2013] are prevalent because they are compatible with stochastic To sum up, the complete procedure is given in Algorithm 1.Algorithm 1 Heterogeneous Multi-Agent Reinforcement Learning for Order Fulfillment. With regard to the advantage estimator, we set the GAE parameters [Schulman et al., 2016] To highlight how our proposed benchmark differs from existing approaches focused on sub-tasks of order fulfillment, we compare the objectives, observations, and actions in Table 1. It should be noted that multiple formulations exist for each sub-task.