AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,017 papers
Reinforcement learning typically optimizes average reward. For generative policies, the average can hide an important distinction: two policies can achieve the same mean reward while having very different chances of producing a rare but high-reward rollout. This matters as sampling increases during training and inferen...
Graph neural networks (GNNs) are a class of neural networks suitable for learning on graph-structured data. Their application to spatial data is a natural extension, however its relatively unclear which message-passing operations, architectural configurations, and graph representation is best suited for classifying cha...
The direction of ignorance is causally active: raising or lowering $\lambda$ at the final prediction state steers the prediction toward or away from the unigram prior in KL divergence, with larger models generally exhibiting lower prior reliance in the high-context limit.
This analysis quantifies the role of the reweighting factor in the score approximation error, providing theoretical guidance for weighting choices used in practice, and develops a Neural Tangent Kernel analysis tailored to diffusion training with stochastic gradients.
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A variation space for nonlinear operators between Hilbert spaces, defined through vector-valued Borel measures of bounded variation, is formulated and analyzed, which yields algebraic approximation and learning rates in contrast to the complexity barriers for Lipschitz and Fr\'echet differentiable operator classes.
It is shown that discretized F\"ollmer processes give natural hyper-parameter settings of the DDPM sampler while accommodating a broader class of variance schedules than discretized reverse SDEs.
This work proposes a Gram-based anisotropic objective perturbation, a ``pre-distortion"strategy that counteracts the distortion from the covariate structure to restore isotropy in the estimation process and provides theoretical insights into designing stable and efficient private estimators without relying on data-depe...
This approach provides a unified framework to handle MLP within both classical and quantum LLMs and shows that the Pythagorean inequality continues to hold in the infinite-dimensional setting whenever the convex information-projection problem attains a finite minimum.
This work proposes a modular and provably accurate cut-Bayes approach requiring no access to upstream data or model, and establishes, to the knowledge, first results on uniform Kullback-Leibler approximation rates of conditional distributions by common flow classes, yielding widely applicable fixed-data error rates for...
DLM-One eliminates iterative refinement by aligning the scores of a student model's outputs with the score function of a pretrained teacher DLM in the forward-diffused noisy space, which positions one-step score distillation as a viable path for the efficient deployment of continuous diffusion models operating in conti...
Matrix-variate distributions are a relatively recent addition to the model-based clustering literature, thereby making it possible to analyze data in matrix form with complex structure such as images and time series. Due to its recent appearance, there is limited literature on matrix-variate data, with even less on dea...
Thompson sampling (TS) is one of the most popular and earliest algorithms to solve stochastic multi-armed bandit problems. We consider a variant of TS, named $\alpha$-TS, where we use a fractional or $\alpha$-posterior ($\alpha\in(0,1)$) instead of the standard posterior distribution. To compute an $\alpha$-posterior,...
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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