AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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3,920 papers
With this ensemble, frequency matching with a public name list recovers zero identities by alignment across the four corpora; the names it got right were ones the detector missed, left in clear text.
Rep2Act, a representation-to-action alignment method that translates latent answerability awareness into explicit abstention decisions, is introduced, a representation-to-action alignment method that translates latent answerability awareness into explicit abstention decisions.
Latent medical image generators usually treat the tokenizer as fixed preprocessing. We test whether this separation holds in a controlled ChestMNIST study at $64\times64$ that crosses discrete tokenizers, generator families, and sampler settings under a shared latent grid, with continuous-latent reference cells. The ra...
This work proposes to restrict contrastive comparisons to categories within the same taxonomic level, and adopts a group-balanced design, ensuring each taxonomic level receives adequate optimization and improves both hierarchical consistency and classification accuracy from coarse to fine granularity.
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This v2 revision reports measured results on the completed implementation of DroneShield-AI, a unified open framework integrating six processing layers: RF signal classification, acoustic motor-signature detection, YOLOv8-based visual detection, evidence-weighted sensor fusion, a Behavioral Intent Classification Engine...
STRIVE-D, a data-calibrated retrieval framework for driving videos, uses weakly labeled in-domain videos to estimate when a query rule is reliable, adapt rules that mismatch observed data, and fuse calibrated rule scores with vision-language and keyword-based retrieval signals.
Across three VLMs and twelve benchmarks, this work tracks the answer-relevant information decodable at each decoder layer and measures when it becomes coupled to the final output, and distinguishes the availability of visual information from its influence on prediction.
This work studies how moving known computation outside the network changes learning under algebraically equivalent flow interfaces with JLT, a latent Transformer in a frozen variational autoencoder (VAE) representation.
Experiments show that COVER improves the coverage-conflict trade-off, making CM-EVS a sparse, compact, and auditable RGB-D-pose resource for geometry-consistent panoramic 3D learning.
While the overall inference latency of Video Diffusion Transformers (DiTs) can be substantially reduced through model distillation, per-step inference latency remains a critical bottleneck. Existing acceleration paradigms primarily exploit redundancy across the denoising trajectory; however, we identify a limitation wh...
Conformal Prediction (CP) is a distribution-free method for constructing prediction sets with marginal finite-sample coverage guarantees, making it a suitable framework for reliable uncertainty quantification in safety-critical object detection. However, object detection introduces structured multi-output predictions,...
Many urban prediction tasks depend not only on the static attributes of a location, such as the visual appearance of its built environment, but also on its dynamic function: how people navigate and use it over time. Predicting congestion, travel demand, or road safety risks, for instance, requires mobility information...
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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