AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
Category
3,080 papers
The Causal Context-Gated Forecaster (CCGF), which encodes a 60-frame pre-dropout history of gaze and head pose and combines it with DINOv3 scene features and evaluates two scene conditions: Live, in which the scene representation continues to update during tracker loss, and Frozen, in which the final pre-dropout repres...
Investigation of how communication should adapt to event priority and passenger activity highlights event consequence, passenger activity, continuing information value, and confirmation need as key considerations for selective in-cabin communication.
Evaluating state-of-the-art video generators, image generators, and LLM/VLM baselines reveals a sharp gap between visual quality and reasoning correctness: video models perform best on Physical Commonsense, but remain weak on symbolic tasks such as Sudoku, ARC, and Math, and brittle in cross-view embodied navigation.
We investigate the effect of pretrained T5 model scale and explicit motion features on pose-to-text Indian Sign Language Translation (SLT) for the WSLP 2026 Shared Task. Pose sequences are projected into the embedding space of T5 through a lightweight pose encoder, with the complete model fine-tuned to generate English...
Reach audiences
Advertise in front of researchers, engineers, and readers.
Synthesizing realistic and diverse anomalous samples from limited data is vital for robust model generalization. However, existing methods struggle to reconcile fidelity and diversity, often hampered by distribution misalignment and overfitting, respectively.To mitigate this, we introduce Anomaly Preference Optimizatio...
It is found that most baselines drastically degrade in performance, as they require significant overlap between viewpoints, while AnyView maintains the ability to produce realistic, plausible, and spatiotemporally consistent videos when prompted from $\textit{any}$ viewpoint.
Theoretically, the OPD framework is lifted from discrete tokens to continuous-state Markov processes, deriving a closed-form per-step KL objective that unifies both stochastic SDE and deterministic ODE refinement via mean-matching, which provides lower variance and better generality compared to conventional PPO-style p...
Handwritten mathematical expression recognition (HMER) is conventionally scored by exact-match rates and string-similarity metrics that are blind to where an error occurs: two predictions with identical token-error counts receive identical scores whether they misplace a subscript or swap the operands of a fraction. Ren...
A Reliability-Aware Boundary Refinement Network (RABR-Net), a two-stage framework for trustworthy image segmentation that provides an interpretable and reliability-focused strategy for boundary-sensitive biomedical image segmentation.
CP-BG-Bench is introduced, a paired-view evaluation framework that holds the central cell fixed across four matched views, ablating or augmenting surrounding pixels as a controlled intervention.
Experiments on seven heterogeneous handwriting benchmarks show that complementary supervision consistently improves training with page transcription alone, while joint multi-source training provides further gains on most datasets, and the proposed sparse expert model further improves the dense baseline on six of the se...
STAG is introduced, to the authors' knowledge the first post-hoc framework for token-level spectro-temporal grounding of captions generated by audio-based MLLMs, and provides behavioral support for the faithfulness and selectivity of the explanations.
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
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
