technocracy

Momentum SGD (MSGD) serves as a foundational optimizer in training deep models due to momentum’s key role in accelerating convergence and enhancing generalization. Meanwhile, asynchronous distributed learning is crucial for training large-scale deep models, especially when the computing capabilities of the workers in the cluster are heterogeneous. To reduce communication frequency, local updates are widely adopted in distributed learning. However, how to implement asynchronous distributed MSGD with local updates remains unexplored. To solve this problem, we propose a […]

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The company is expanding its efforts to protect ChatGPT users in cases where conversations may turn to self-harm.

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arXiv:2602.06014v1 Announce Type: cross Abstract: Thompson sampling (TS) is widely used for stochastic multi-armed bandits, yet its inferential properties under adaptive data collection are subtle. Classical asymptotic theory for sample means can fail because arm-specific sample sizes are random and coupled with the rewards through the action-selection rule. We study this phenomenon in the $K$-armed Gaussian bandit and identify emph{optimism} as a key mechanism for restoring emph{stability}, a sufficient condition for valid asymptotic inference requiring each arm’s pull […]

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arXiv:2603.05538v1 Announce Type: new Abstract: Data-driven surrogate models improve the efficiency of simulating continuous dynamical systems, yet their autoregressive rollouts are often limited by instability and spectral blow-up. While global regularization techniques can enforce contractive dynamics, they uniformly damp high-frequency features, introducing a contraction-dissipation dilemma. Furthermore, long-horizon trajectory optimization methods that explicitly correct drift are bottlenecked by memory constraints. In this work, we propose Jacobian-Adaptive Weighting for Stability (JAWS), a probabilistic regularization strategy designed to mitigate these limitations. […]

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In the first part, I talked about what Data Quality, Anomaly Detection and Outliers Detection are and what’s the difference between outliers detection and novelty detection. In this part, I will talk about a very known and easy method to detect outliers called Interquartile Range. Introduction The Interquartile Range method, also known as IQR, was developed by John Widler Turky, an American mathematician best known for development of the FFT algorithm and box plot.

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arXiv:2601.03386v1 Announce Type: new Abstract: Unmanned aerial vehicle (UAV) with slung load system is a classic air transportation system. In practical applications, the suspension point of the slung load does not always align with the center of mass (CoM) of the UAV due to mission requirements or mechanical interference. This offset creates coupling in the system’s nonlinear dynamics which leads to a complicated motion control problem. In existing research, modeling of the system are performed about the UAV’s […]

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arXiv:2603.24750v1 Announce Type: new Abstract: Online Health Communities connect patients for peer support, but users face a discovery challenge when they have minimal prior interactions to guide personalization. We study recommendation under extreme interaction sparsity in a survey driven setting where each user provides a 16 dimensional intake vector and each support group has a structured feature profile. We extend Neural Collaborative Filtering architectures, including Matrix Factorization, Multi Layer Perceptron, and NeuMF, with an auxiliary pseudo label objective […]

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