AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,315 papers
Sensor-availability experiments show that MethaneFuse improves detection when S2 is available and transfers plume knowledge to L8/9, EMIT, and S5P when S2 is unavailable, demonstrating the value of learning from incomplete heterogeneous sensor observations for practical methane plume detection.
LightMedSeg-ISLES, a 1.26-million-parameter pipeline for T1-weighted stroke lesion segmentation in ISLES, is presented, establishing a strong single-checkpoint alternative to much larger models.
Critical infrastructure location data is often incomplete and unevenly distributed globally, especially in developing regions. Earth observation foundation models are proposed as a new step in enabling us to more efficiently understand the natural and built environment, raising questions as to their effectiveness in pe...
LeCor is proposed, which performs test-time training on a small set of case adapters that are reset for every case and meta-learned such that a single gradient step driven by a click improves the slices that were not clicked.
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This work leverages the geometric self-similarity and geometry-texture correlation existing in many natural and man-made shapes; and trains a shape-specific local texture generation and completion model that enables local geometry-conditioned texture inpainting, guided by artist-selected references, and generalizes to...
View-Structured Conformal Prediction (VSCP) is proposed, which splits the pre-calibration scale into a spatial shape from the renderer and a transferable view-difficulty factor, which predicts the smallest view-wise multiplier that shape needs.
A causal-abstraction-based audit instrument for efficiently evaluating fairness under interventions on the classifier-free guidance scale is proposed and experiments evaluate distributional fidelity, fairness-query accuracy, and computational efficiency.
PSCT-Net, a geometry-aware framework with differentiable back-projection, establishes a spatially faithful volumetric prior, alleviating depth ambiguity and addressing the gap in adult-centric, trunk-focused datasets.
FiberTune is proposed, a training-time objective that preserves teacher-structured visual residuals without adding inference-time overhead and improves over task-loss-only fine-tuning in every one of six controlled simulation settings spanning two benchmarks and two architectures.
Anatomical understanding, which is the ability to identify, localize, or segment anatomical structures, is critical in medical image analysis; however, its progress is constrained by the scarcity of expert-labeled data. A promising remedy is to leverage an annotated reference image to guide the interpretation of an unl...
A large-scale smartphone-oriented deblurring dataset constructed from 240~fps slow-motion video is introduced and multiple state-of-the-art deblurring models using PSNR and SSIM are benchmarked to observe consistent performance degradation relative to the baseline similarity between the input blurry images and ground t...
SyRe is presented, a promptable segmentation foundation model based on Synergistic vision-language Reinforcement that achieves accurate text-prompted segmentation across diverse clinical scenarios, with particularly strong performance on disease-related targets.
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.
Human-Computer Interaction and Visualization
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