This work proposes a unified autoregressive diffusion-based graph generative model that jointly learns structure and features, constructing complete 3DSGs bottom-up from observed vertical planes across arbitrary hierarchy depths, and proposes an adaptation of the Fused Gromov--Wasserstein distance for principled graph-level evaluation of generated 3DSGs against ground truth.
J. A. Millan-Romera, Samuel Cognolato, Holger Voos et al.· 0 citations
ORDDAR (Observation-Driven Reasoning for Distortion-Resilient Decision, Action, and Cognitive Recovery) is presented, a reasoning framework that models reasoning as cognitive state transitions, detects localized distortions, retrieves related reasoning from prior experiences, and repairs only the affected states.
Deblina Kar, Anant Nawalgaria, S. D. Das Mandal· 0 citations
Match controls on training recipe as well as capacity, and buy compression robustness with codec diversity before architecture, and buy compression robustness with codec diversity with codec diversity before architecture.
The effectiveness of the proposed source codecs are demonstrated by achieving state-of-the-art performance in distributed semantic segmentation at below 0.2 bits per pixel, measured using the mean intersection-over-union metric on ADE20K (Cityscapes).
Danish Nazir, Timo Bartels, Thorsten Bagdonat et al.· 0 citations
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This work uses the abundant compute resources of the cloud to run a highly-accurate oracle model that will guide the retraining process of the on-vehicle model, leading to improved inference accuracy over time.
Yu-Heng Zhu, Dhruva Ungrupulithaya, Boluo Ge et al.· 2024 IEEE 100th Vehicular Te...· 1 citation
Autonomous vehicles operate in dynamic, ever-changing environments where new scenarios and edge cases constantly emerge. As a result, static learning models are inadequate for ensuring safe and reliable operation. Continuous learning is essential for adapting to these evolving conditions and maintaining robust performance across diverse real-world settings. However, autonomous vehicles generate massive streams of visual data during operation, and existing continuous learning approaches typically rely on heuristic sampling methods that fail to capture temporal dynamics, often overlooking critical learning opportunities or selecting redundant frames. In this paper, we introduce FrameScope, a temporal data valuation framework for continuous learning in autonomous vehicles. FrameScope extends neural tangent kernel theory to temporal domains, enabling principled valuation of streaming visual data. Unlike cloud-centric methods that transmit all video data for processing, our approach performs principled, local frame selection on the vehicle and queries a cloud-based oracle model only for labels of those high-value frames. Extensive experiments across multiple domain shifts show that FrameScope consistently outperforms existing methods, achieving higher sample efficiency and significantly reducing catastrophic forgetting in autonomous vehicle perception. By valuing data on the vehicle and querying only labels for selected frames, FrameScope reduces bandwidth requirements, enabling scalable operation with a lightweight cloud labeling service.
Yu-Heng Zhu, M. Yoon· IFIP International Informati...· 0 citations
This work forms LLM-driven equation discovery as an iterative search process that unifies Best-of-N, sequential refinement, tree search, and evolution-style methods under a common compute-allocation view and finds that search width is the dominant allocation parameter.
Hao-Wei Lin, Hubert Lim, Xiang-Yu Wang et al.· 0 citations
It is shown that user-preferring conflict resolution can coexist with a readable internal arbitration signal, and successful intervention depends on the geometry of the readout rather than probe accuracy alone, while directions selected mainly for pooled separability steer poorly.
Enrique Balp-Straffon, Chih-Hao Hsu, Rushiraj Gadhvi et al.· 0 citations
MAGG is proposed, a principled multi-agent framework for constructing Governed Knowledge Graphs that introduces explicit governance decisions for reliable and trustworthy knowledge sharing and demonstrates its effectiveness.
Pranav Bykampadi, Neel Mokaria, Vishesh Narayan et al.· 0 citations
A three-role Monte Carlo Tree Search (MCTS) framework that treats the Lean 4 compiler purely as a reward oracle using compiler output as a scalar signal for UCB-guided tree updates without feeding error content into the generation context is proposed.
AutoScientist-Quant, a self evolving search process that regards quantitative research as one budgeted search problem, is presented, a self evolving search process that regards quantitative research as one budgeted search problem.
Zong-Qian Li, Yaoyiran Li, Yao-Hui Guo et al.· 0 citations
The proposed learning-assisted Tabu Search notably reduces computation time while consistently producing higher-quality solutions than the standard algorithm, highlighting the potential of combining machine learning with metaheuristics by leveraging the implicit knowledge embedded in search trajectories.
Wissem Ahmed Zaid, Alain Hertz, Denny Liu· 0 citations
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.
