AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,822 papers
CON decomposition is introduced, which quantifies how much of a layer's variance each concept explains given all other concepts and the outcome, and how much none of them explains, yielding layer-comparable, calibrated scores that suppress false positives.
This work empirically validate predictions through exact calculations and controlled stochastic optimization of the worst-case joint-distribution error permitted by excess risk at most $\varepsilon$-identifiability modulus using an $\varepsilon$-identifiability modulus.
Seq2Synth is introduced, a unified benchmark for assessing temporal and schema properties to determine applicable evaluations, covering timestamp, cross-sectional, longitudinal, and structural fidelity, alongside trajectory-aware utility and privacy.
The Mamba-Assisted Closure (MAC) framework is proposed, which employs a Mamba-based sequence model to predict the closure from the resolved trajectory and couples the learned closure with the reduced-order governing equations through a numerical integrator to advance the resolved variables in time.
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This work pairs the brain emulator with large language models that generate news headlines from linguistic parameters such as valence, arousal, and dominance and shows that these parameters can be recovered from predicted brain maps, demonstrating that the emulator's synthetic neural encodings preserve information abou...
This study develops a theoretical framework framing transformers as kernel regressors, motivating a purely intrinsic strategy for ablating heads based on the stable rank of the per-head projection matrices, and uncovers the specific heads responsible for degenerate phenomena widely observed in TSFMs.
This work introduces SHAKE-GNN, a novel scalable graph-level GNN framework based on a hierarchy of Kirchhoff Forests, a class of random spanning forests used to construct stochastic multi-resolution decompositions of graphs, enabling flexible trade-offs between efficiency and performance.
The empirical evaluations on standard benchmarks and in a comparison study with widely used explanation methods show that attention weights can be meaningfully incorporated into the studied XAI frameworks, highlighting their value in enriching transformer explainability.
The teacher, an auxiliary behavior model, is trained to sample high-loss regions of the student and can generalize across unexplored modes, thereby enhancing mode coverage by providing an efficient training curriculum.
It is established that gradient-based training can induce an implicit regularization towards low rank for several neural network architectures, and it is demonstrated empirically that this phenomenon may facilitate an explanation of generalization over natural data.
A new algorithm for amortized inference in sparse probabilistic graphical models (PGMs) is presented that enables off-policy training but avoids the need to instantiate all the random variables for each parameter update, thus speeding up training considerably.
This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs. We demon...
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
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Health & Bioscience
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Data Mining & Modeling
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