AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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19,710 papers
Time series anomaly detection (TSAD) is a foundational problem in data mining, aiming to identify observations that deviate significantly from nominal temporal patterns. Driven by escalating data volumes and application complexity, recent research has yielded diverse architectures, including diffusion models, state spa...
Sparse autoencoders (SAEs) were originally motivated by discovering monosemantic features, a property that characterizes individual features but does not specify what the learned feature set should look like as a whole. As SAEs have increasingly been adopted for attribution, circuit discovery, and intervention, researc...
This work introduces Short-to-Long (S2L), which supplies a long-horizon behavior learner with features trained by smaller-discount source networks, which yields aggregate performance gains, improving the paired terminal median on 40 of 57 Atari games.
aggmetrics is a Python package that extends any binary performance measure expressed in the four confusion-matrix counts to multiclass, multilabel, ordinal, cost-sensitive, soft-label and cross-validation settings, by substituting one of four aggregation operators for what libraries usually expose as an average switch....
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Recent work in additive manufacturing and neural inverse design enables the creation of complex three-dimensional structures to manipulate physical fields, yet these designs must also adhere to discrete, non-differentiable fabrication constraints. Current methods limit the design space by imposing differentiable soft m...
Activation probes are computed directions in a language model's residual stream, used both to detect safety-relevant behaviours and, by adding their direction back into the stream, to steer them, which power an interactive interface through which a user can score and steer text directly.
Generator-Agent-Verifier (GAV), a scientific experimentation environment that makes new PDE problems verifiable by construction verifiable by construction, is introduced and a theoretical analysis of evaluation reliability and guided development is provided.
This work advances Markov chain Monte Carlo algorithms by introducing novel moves that simultaneously resample the parents for multiple nodes of the DAG by introducing duet sampling, a new generalization of the Metropolis-Hastings algorithm.
SWE-Verifier is introduced, a lightweight generative model that selectively triggers candidate sampling and verification that achieves comparable or higher resolution than the trajectory and all-step baselines at substantially lower cost.
This work proposes minimal-edit corrective on-policy distillation, a method that preserves on-policy supervision while avoiding full-response imitation, and improves perception-oriented performance, particularly low-level geometric perception, and transfers to external mathematics and perception benchmarks.
A lightweight screening tool that uses four small models to identify patients with high risk of exposure before production-model training and assess if flagged patients could be protected without removing their data is developed.
A workflow combining modality-specific variational autoencoders trained outside the estimation sample, cross-fitted doubly robust and one-step estimators, locked phenotypes, select-then-confirm pathway analysis, portable representations for external replication, and diagnostics for representation insufficiency, exposur...
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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