Table of Links Abstract and 1. Introduction Related Works 2.1 Traditional Index Selection Approaches 2.2 RL-based Index Selection Approaches Index Selection Problem Methodology 4.1 Formulation of the DRL Problem 4.2 Instance-Aware Deep Reinforcement Learning for Efficient Index Selection System Framework of IA2 5.1 Preprocessing Phase 5.2 RL Training and Application Phase Experiments 6.1 Experimental Setting 6.2 Experimental Results 6.3 End-to-End Performance Comparison 6.4 Key Insights Conclusion and Future Work, and References 6.2 Experimental Results Training Efficiency of TD3-TD-SWAR:. […]

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arXiv:2604.02429v1 Announce Type: new Abstract: Photonic computing is a computing paradigm which have great potential to overcome the energy bottlenecks of electronic von Neumann architecture. Throughput and power consumption are fundamental limitations of Complementary-metal-oxide-semiconductor (CMOS) chips, therefore convolutional neural network (CNN) is revolutionising machine learning, computer vision and other image based applications. In this work, we propose and validate a fully photonic convolutional neural network (PCNN) that performs MNIST image classification entirely in the optical domain, achieving 94 […]

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arXiv:2605.01335v1 Announce Type: new Abstract: We characterize the fundamental limits of high-dimensional mean testing under arbitrary truncation, where samples are drawn from the conditional distribution $P(cdot mid S)$ for an unknown truncation set $S$ that may hide up to an $varepsilon$-fraction of the probability mass. For distributions with $p$-th directional moments of magnitude at most $nu_{P,p}$, truncation induces a bias of order $O(nu_{P,p}varepsilon^{1-1/p})$. This bias creates a sharp information-theoretic detectability floor: when the signal $alpha$ falls below this […]

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arXiv:2603.05644v1 Announce Type: new Abstract: In programming, better tools often yield better results. For that, modern programming environments offer mechanisms to allow for their extensibility. The closer those tools are to the code, the easier it is for programmers to map the information provided by a tool to the code this information is about. However, existing extension mechanisms do not facilitate the close integration of tools with textual source code. Tools must be able to track program structures […]

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Hi guys, I’m new to the libary CleanRL. I have run some training scripts by using the `uv run python cleanrl/….py` command. I’m not sure if this can save the best policy (e.g. the policy returns best episode rewards) during training. I just went through the documentation of CleanRL and found no information about this. Do you know how can I save the best policy during training and load it after training ? submitted by /u/ZitaLovesCats [link] [comments]

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Spectral graph contrastive learning has emerged as a unified paradigm for handling both homophilic and heterophilic graphs by leveraging high-frequency components. However, we identify a fundamental spectral dilemma: while high-frequency signals are indispensable for encoding heterophily, our theoretical analysis proves they exhibit significantly higher variance under spectrally concentrated perturbations. We derive a regret lower bound showing that existing global (node-agnostic) spectral fusion is provably sub-optimal: on mixed graphs with separated node-wise frequency preferences, any global fusion strategy incurs […]

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arXiv:2602.07225v1 Announce Type: new Abstract: The pseudo-4D Doyle-Fuller-Newman (DFN) model enables predictive simulation of lithium-ion batteries with three-dimensional electrode architectures and particle-scale diffusion, extending the standard pseudo-2D (P2D) formulation to fully resolve cell geometry. This leads to large, nonlinear systems with strong coupling across multiple physical scales, posing significant challenges for scalable numerical solution. We introduce block-structured preconditioning strategies that exploit the mathematical properties of the coupled system, employing multigrid techniques for electrode-level operators and localized solvers for […]

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We demonstrate that gradient-based data valuation produces curriculum orderings that significantly outperform metadata-based heuristics for training game-theoretic motion planners. Specifically, we apply TracIn gradient-similarity scoring to GameFormer on the nuPlan benchmark and construct a curriculum that weights training scenarios by their estimated contribution to validation loss reduction. Across three random seeds, the TracIn-weighted curriculum achieves a mean planning ADE of $1.704pm0.029$,m, significantly outperforming the metadata-based interaction-difficulty curriculum ($1.822pm0.014$,m; paired $t$-test $p=0.021$, Cohen’s $d_z=3.88$) while exhibiting lower variance than […]

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