AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Category
4,009 papers
Results show that lightweight channel-aware adaptation can recover a substantial fraction of the robustness of a much larger communication interface while broadening quality-valid operation under constrained wireless conditions.
Theoretical analysis shows that the fixed-point distributions of RWTD interpolate between off-policy reward tilting of the reference and on-policy tilting of the current model, providing a principled approach to balancing reward adaptation with retention of prior knowledge.
Quat-Sphere-Vision (QSV), a sparse spherical vision model that replaces this projection triple with a single learned unit quaternion per token, is studied, showing the two roles to be asymmetric.
The approach, called Unified Dynamic FLD, streamlines the landmark detection pipeline by enabling a single model to learn on any number of ``N$-point''datasets, and yield any number of specific landmark predictions by loading the designated landmark queries at runtime.
Reach audiences
Advertise in front of researchers, engineers, and readers.
DyMoS (Dynamic Motion Slider), a training-free and model-agnostic method that rebalances the attention pathway from generated frames to the reference frame during initial denoising steps, is proposed and demonstrated to improve motion dynamics while maintaining visual quality and fidelity to the reference image.
Concentrate and Concentrate (CaC) is a coarse-to-fine anomaly reward model based on Vision-Language Models that first conducts a global temporal scan to anchor anomalous time windows, then performs fine-grained spatial grounding within the localized interval, and finally derives robust judgments via structured spatiote...
This work proposes Generative Video Codebook Codec (GVCC), a zero-shot framework in which a pretrained video generative model serves directly as the decoder, and the transmitted bitstream specifies its generation trajectory.
A fully automated, concept-driven pipeline that bridges the compositional gap by generating photorealistic compositional benchmarks at scale and uncovers a task-dependent trade-off: visio-linguistic context aids relational reasoning but hinders attribute binding through visual clutter.
This work proposes GPERT, a framework to address the trade-off between accuracy and temporal resolution in event-based 3DGS, by decouple the rendering into two branches: event-by-event geometry (depth) rendering and snapshot-based radiance (intensity) rendering, by using ray-tracing and the image of warped events.
This work proposes the first Prompt-based Continual Compositional Zero-Shot Learning (PromptCCZSL) framework that retains prior knowledge through recency-weighted multi-teacher distillation, and introduces a comprehensive protocol that jointly measures catastrophic forgetting and compositional generalization.
A novel generative model that predicts 3D dose distributions based solely on user-defined preference flavors that enables planners to prioritize specific trade-offs between organs-at-risk (OARs) and planning target volumes (PTVs) offering greater flexibility and personalization is introduced.
Consist-Retinex is proposed, which first uses a Retinex Transformer Decomposition Network to obtain paired reflectance and illumination maps, then trains two conditional consistency models with a Retinex-aware dual objective and adaptive noise-emphasized fixed-point sampling.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
A Blog post by H company on Hugging Face
A Blog post by Liquid AI on Hugging Face
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
