Asynchronous parallel implementations of stochastic gradient (SG) have been broadly used in solving deep neural network and received many successes in practice recently. However, existing theories cannot explain their convergence and speedup properties, mainly due to the nonconvexity of most deep learning formulations and the asynchronous parallel mechanism. To fill the gaps in theory and provide theoretical supports, this paper studies two asynchronous parallel implementations of SG: one is over a computer network and the other is on a shared memory system. We establish an ergodic convergence rate O (1 / K) for both algorithms and prove that the linear speedup is achievable if the number of workers is bounded by K ( K is the total number of iterations). Our results generalize and improve existing analysis for convex minimization.

We propose RUDDER, a novel reinforcement learning approach for delayed rewards in finite Markov decision processes (MDPs).

reinforcement learning; delayed reward; reward redistribution; return decomposition; bias-variance; credit assignment; LSTM

Japan's Digital Agency to cooperate with OpenAI on administrative tools The Digital Agency will enable its employees to use OpenAI's cutting-edge large language model-based AI tools for their work. The Digital Agency said Thursday that it will cooperate with OpenAI to fully use artificial intelligence technology in administrative work and service. As part of the initiative, the agency will enable its employees to use OpenAI's cutting-edge large language model-based AI tools for their work. It is also considering joint development with the U.S. company of a generative AI app for administrative use. The agency plans to provide its employees with access to generative AI tools and encourage other government agencies to adopt these services starting as early as fiscal year 2026.

Perceptual loss functions based on deep neural networks have produced promising results.

The vast majority of deep models use multiple gradient signals, typically corresponding to a sum of multiple loss terms, to update a shared set of trainable weights.

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Third, a generative model is obtained by combining the estimated distribution with the decoder part of the AE.

In this work, we introduce a modification to traditional reinforcement learning which we call observational dropout, whereby we limit the agents ability to observe the real environment at each timestep.