AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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4,009 papers
GLAM (Glaucoma Longitudinal Analysis Model), a deep learning framework that ingests longitudinal Humphrey 24-2 total deviation sequences with five clinical features and predicts MD and visual field index progression rates using attention-based fusion and aleatoric uncertainty, is presented.
TOLA is proposed, a Text-aware One-step Latent Adaptation framework without iterative image-text diffusion that consistently surpasses existing diffusion-based TSR methods by at least 2.72 dB in PSNR on CTR-TSR-Test.
SARFusion is proposed, a robust 3D object detector that decouples object-query decoding into three parallel reasoning branches: a camera branch, a LiDAR branch, and a camera-LiDAR fusion branch that alleviates harmful cross-modal interference while preserving the benefits of multimodal fusion when complementary cues ar...
Findings establish Med-AR as a strong pretraining recipe for long-tailed chest X-ray classification on the evaluated public benchmarks and demonstrate the value of assessing discrimination and selective prediction together.
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Audio-Visual Semantic Segmentation (AVSS) aims to identify, segment, and classify sound-emitting objects in video frames. Previous Transformer-based AVSS approaches largely inherit design principles from image segmentation models. Recent studies show that these image segmentation models contain redundant components tha...
WildHSR combines both readouts to reconstruct metric cameras, scene and people from monocular video, and is the first feed-forward method in the published comparison to beat the best optimization-based WA-MPJPE and RTE while leading feed-forward methods on all three world-frame metrics.
DAWN is a noise-robust perception framework for legged locomotion which builds noise robustness directly into a world model via two modifications: feeding noisy depth to the encoder while keeping clean depth as the reconstruction target, forcing the model to implicitly denoise its input.
EIB-Net is proposed, an Entropy-guided Information Bottleneck Network that introduces a novel Image Entropy metric to automatically select the most informative patch, then processes it with a Variational Information Bottleneck (VIB) to learn compact, generalizable features.
A causal stage-intervention protocol that overwrites individual stages while holding the downstream task fixed is introduced, finding that grounding is the dominant source of downstream error, with roughly four times the causal impact of corrupting visual observations.
M$^2$PFN is proposed, an end-to-end framework that turns this tabular foundation model into a multimodal AD predictor, and transfers even when the cognitive instrument changes.
DrGait is introduced, a training-free agentic framework that shifts the VLM's role from a direct visual reasoner to a clinical planner, and anchoring VLM's reasoning in verifiable geometric and temporal measurements, reduces hallucinations.
Results characterize agent reasoning as dynamic working state rather than permanent interaction history, and suggest that historical reasoning becomes more replaceable once task relevant derived state has been reliably externalized into code, files, tool outputs, or environmental feedback.
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.
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