Firstly, many machine learning models use optimization algorithms which require access to derivatives of the model.

TraceGraph, it compares the type, attributes, and the executed location of each operation. For example, the MatMul operation of TensorFlow has ' MatMul ' as GraphGenerator fails to match because of the different attributes. The pushed call id is popped when the function is returned. As same as the call id stack, Terra manages the loop id stack for the entire program execution. Current implementation of Terra does not consider multi-threading yet.

We evaluated Terra's performance improvement and coverage with ten imperative DL programs for several DNN architectures.

Data is often impractical to share for a range of well considered reasons, such as concerns over privacy, intellectual property, and legal constraints. This not only fragments the statistical power of predictive models, but creates an accessibility bias, where accuracy becomes inequitably distributed to those who have the resources to overcome these concerns. We present DISCO: an open-source DIStributed COllaborative learning platform accessible to non-technical users, offering a means to collaboratively build machine learning models without sharing any original data or requiring any programming knowledge. DISCO's web application trains models locally directly in the browser, making our tool cross-platform out-of-the-box, including smartphones. The modular design of \disco offers choices between federated and decentralized paradigms, various levels of privacy guarantees and several approaches to weight aggregation strategies that allow for model personalization and bias resilience in the collaborative training. Code repository is available at https://github.com/epfml/disco and a showcase web interface at https://discolab.ai

Given recent deep learning results that demonstrate the ability to effectively optimize high-dimensional non-convex functions with gradient descent optimization on GPUs, we ask in this paper whether symbolic gradient optimization tools such as Tensorflow can be effective for planning in hybrid (mixed discrete and continuous) nonlinear domains with high dimensional state and action spaces? To this end, we demonstrate that hybrid planning with Tensorflow and RMSProp gradient descent is competitive with mixed integer linear program (MILP) based optimization on piecewise linear planning domains (where we can compute optimal solutions) and substantially outperforms state-of-the-art interior point methods for nonlinear planning domains. Furthermore, we remark that Tensorflow is highly scalable, converging to a strong plan on a large-scale concurrent domain with a total of 576,000 continuous action parameters distributed over a horizon of 96 time steps and 100 parallel instances in only 4 minutes. We provide a number of insights that clarify such strong performance including observations that despite long horizons, RMSProp avoids both the vanishing and exploding gradient problems. Together these results suggest a new frontier for highly scalable planning in nonlinear hybrid domains by leveraging GPUs and the power of recent advances in gradient descent with highly optimized toolkits like Tensorflow.

RMSProp avoids both the vanishing and exploding gradient problems.

Firstly, many machine learning models use optimization algorithms which require access to derivatives of the model.

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Proofs of results stated in the main text are provided in Appendix A. Additional experimental results, including coverage plots, are provided in Appendix B. Therefore, the proof is completed by continuous mapping. Applying Taylor's theorem using the Lagrange form of the remainder, we have that, for some random From the proof of Eq. (7), we know that, with probability tending to 1, Figure 1 shows the fitted mean and covariance on a single draw of the quadratic dataset. In this section we provide details for the experimental setup used in the paper. The visualized covariance matrices were projecting to ensure positive semi-definiteness. In this section we present the multivariate algorithm for finite-difference IDM (FDIDM).