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3,048 papers
This study designed and empirically validated a three-layer Zero Trust architecture for Microsoft SQL Server that natively integrates instance-level authentication governance, enhanced Role-Based Access Control with Row-Level Security and Just-in-Time privilege elevation, and metadata-driven Attribute-Based Access Cont...
Over-parameterized linear regression has been widely studied over the last decade. However, most existing works assume that the covariates are independent and that their covariance matrices are non-degenerate. In this paper, we relax both assumptions and derive deterministic equivalents for the prediction risk in a van...
It is shown how epistemic and aleatoric uncertainty measures can be derived via decomposition of a subjective risk, based on a strictly proper loss, and considered a first step towards a full learning-theoretic framework for uncertainty quantification.
This note studies the geometry of full conformal prediction (FullCP) regions generated by an empirical energy-form pairwise score and finds modest but systematic tightening of the generic certificate and frequent robust nonconvexity witnesses.
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DiscoverPhysics, an interactive benchmark that asks a LLM agent to discover the laws of motion of a simulated world whose physics deliberately deviates from the authors' own, is introduced.
Simulations on EEG motor imagery benchmarks show that FedSPDnet outperforms federated EEGnet in F1 score and robustness to federation and partial participation, while using fewer parameters per communication round.
An MNAR-aware latent state-space model that combines linear traffic dynamics with a Bernoulli missingness channel whose probability depends on the latent state is studied, achieving lower P95 error, lower long-blackout RMSE, and orders of magnitude fewer stored scalar entries.
A new error metric is introduced that precisely captures model vulnerability to consistent adversarial attacks -- perturbations that preserve the ground-truth labels, offering theoretical insight into the mechanisms underlying model sensitivity to adversarial attacks.
This paper considers the estimation of quantiles via a smoothed version of the stochastic gradient descent (SGD) algorithm. By smoothing the score function with a bandwidth tied to the learning rate, we obtain estimates that are monotone in the quantile level at every iteration, while retaining the memory and computati...
This work builds on a principled continuous-time approximation of Markov algorithms and introduces a new, exact entropy flow formula for such processes, and establishes novel connections to a well-studied family of modified logarithmic Sobolev inequalities.
A deep learning based approximation algorithm for solutions of stochastic partial differential equations (SPDEs) using a deep neural network for every realization of the driving noise process of a SPDE to approximate the solution process of the SPDE under consideration.
This work investigates how the model parameters -- mainly attention heads and head dimensions -- should be allocated across layers to balance expressivity and efficiency, and proposes principled strategies for allocating attention heads and dimensions across Transformers' layers.
Accelerating the search for novel antimicrobial peptides using Google Antigravity for Science.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
General Science
General Science
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
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