AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Category
18,068 papers
This work presents a plant-aware conversational copilot that extends this paradigm in three directions: plant-specific context ingestion, a chat-first interaction model for explanation and debugging, and a graduated tool-access ladder from chat to deployment.
Causal discovery with latent confounding is particularly challenging, but a common setting in real-world data. In this work, we focus on linear causal models with latent confounding, represented by acyclic directed mixed graphs (ADMGs). Existing methods that fully identify ADMGs typically assume non-Gaussianity or grap...
It is argued that continuous-time graph learning should identify low-dimensional states sufficient for future prediction rather than reconstruct the full history, and proposed PreDyG, a lightweight model whose capacity is matched to the intrinsic predictive dimension.
PULSE, an event-driven framework for personalized opportunistic learning that treats each encounter as an online collaboration decision, selecting either no interaction or a promising nearby peer, shows that selective opportunistic collaboration can provide effective personalization while limiting communication and res...
Reach audiences
Advertise in front of researchers, engineers, and readers.
Groundwater in Ghana's Densu River Basin remains a key domestic water source, but multi-metal contamination threatens children's health. Previous deterministic hazard index assessments provide limited treatment of uncertainty, co-exposure, and spatial variability. This study applies a Bayesian probabilistic framework f...
Concept Bottleneck Models (CBMs) promise predictions that humans can inspect and correct through concepts such as"curved beak". Yet on CUB-200-2011, their standard benchmark, progress is measured like that of ordinary classifiers: by task and concept accuracy. Neither reveals whether the label is predicted from the exp...
A diagnostic study finds that stronger teacher--student agreement in intermediate transitions is associated with higher answer correctness within questions, with correct responses exhibiting lower mean transition KL across all measured intermediate layer pairs.
Direct preference optimisation (DPO) is widely used for language model post-training, but its performance depends strongly on the choice of its hyperparameters, such as the learning rate and the reference-policy regularisation parameter $\beta$. To better understand the DPO hyperparameter landscape, we conduct extensiv...
Class-wise analyses, together with attention and masking analyses, show improved discrimination of ambiguous states and indicate that physical variables meaningfully constrain local response interpretation, and demonstrate a deployment-oriented approach to robust diagnosis in heterogeneous industrial environments.
P perturbation concentration is established as a complementary, model- and attack-dependent axis of $\ell_1$ attack evaluation, and direct in-loop constrained optimization preserves up to $88$ percentage points more attack success than post-hoc clipping, highlighting the importance of constraint enforcement.
Learned barrier certificates provide a principled mechanism for establishing safety of stochastic systems, but their validity may deteriorate during deployment when the underlying dynamics differ from those used for training. We propose an anytime-valid runtime monitor that audits a learned barrier certificate using ob...
Deep neural networks often fail at small-sample regression, collapsing to low-variance predictions that miss individual differences. Here, we introduce a pairwise comparison framework that reframes regression as aggregated relative rankings. This method improves data efficiency across model architectures and dataset si...
The new approach, known as FLEX, could lead to more intuitive, interactive household and assistive robots.
By learning how matter moves, the WorldParticle system could assist with developing soft robots, medicines, and more.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
A Blog post by Ai2 on Hugging Face
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
