AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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4,009 papers
This work proposes Time Vision Transformer (TiViT), the first framework that successfully unlocks the representational power of frozen Vision Transformers pretrained on large-scale image datasets for time series classification.
This study provides a comprehensive overview of mobile image-based healthcare solutions and their potential to support low-cost disease diagnosis and monitoring, particularly for underserved populations in remote and resource-constrained settings.
This work presents MiTHras, a task-specific pretraining framework that combines pseudo-label-guided image- and token-level contrastive learning with masked reconstruction that yields robust, transferable representations for automated mitotic activity assessment.
Spectral Energy-proportional Rank Allocation (SERA) is proposed, a simple task-adaptive strategy that allocates ranks according to the singular-value energy structure of each task vector, extending SVD-based model merging from uniform-capacity merging to task-dependent capacity allocation.
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This work introduces Mixture-of-Experts for Communication-Aware Incremental Learning (MECAIL), the first method that meets this strict requirement, in which each new domain or environment is served by a small expert network that adapts the base model.
Recognizing hand-drawn geometric shapes is a foundational sub-problem of sketch recognition, with applications in education, human-computer interaction, and diagram digitization. This paper presents the design, implementation, and evaluation of a desktop application that recognizes four basic hand-drawn geometric shape...
Mechanistic interpretability of vision transformers seeks to decompose model computation into human-readable units, but learned representations entangle many concepts in each neuron. Feature superposition is widely treated as the central obstacle to this decomposition, yet most mitigations (sparse autoencoders, diction...
A self-supervised Vision-Transformer backbone combined with embedding retrieval is an effective, deployable strategy for fine-grained identification in species-rich, data-poor floras in New Guinea.
This work proposes UniK, a common platform for both digital and physical AI classes, covering the full knowledge lifecycle across modalities from rich text and video to molecular data and sensor telemetry, built on Polymath Retrieval with no task-specific fine-tuning.
These results identify supervision path as a determinant of state reliance in the audited methods and show that diffusion-specific computation often provides no deterministic endpoint advantage, including in methods that rely on the audited state properties.
Cardiovascular magnetic resonance (CMR) provides comprehensive cardiac assessment but remains underutilized because of the complexity of acquisition, post-processing, and interpretation. Existing artificial intelligence (AI) methods address isolated tasks, limiting clinical integration. We present ORION-CMR (On-scanner...
An end-to-end framework for reconstructing overhead power utility network topology and extracting span-level physical metadata from large-scale aerial LiDAR, which mitigates common failure modes of heuristic connectivity rules in dense or cluttered scenes and under partial wire observation.
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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