Tag: Prediction

All the articles with the tag "Prediction".

• Enhancing Safety Standards in Automated Systems Using Dynamic Bayesian Networks

  Published: 8 May, 2025 |  at  11:06 AM

  

  90.63 🤔

  This paper proposes a Dynamic Bayesian Network framework for autonomous vehicles that enhances safety in cut-in maneuvers by integrating lateral evidence and probabilistic safety assessments, achieving superior crash avoidance in high-speed scenarios (9.22% crash rate) compared to baseline models in the JRC-FSM simulator.

• Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions

  Published: 2 Jun, 2025 |  at  11:32 AM

  

  90.51 🤔

  本文通过对92个开源语言模型的元分析，提出了一种超越缩放定律的性能预测框架，揭示了数据组成（如代码比例15-25%）和架构决策对下游任务性能的显著影响，预测精度相对提升3-28%。

• Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations

  Published: 8 May, 2025 |  at  10:22 AM

  

  89.20 🤔

  The Video Prediction Policy (VPP) introduces a novel generalist robot policy that leverages predictive visual representations from fine-tuned video diffusion models to learn implicit inverse dynamics, achieving significant improvements of 41.5% on the Calvin ABC→D benchmark and 31.6% in real-world dexterous manipulation tasks over state-of-the-art baselines.

• Deep Learning for On-Street Parking Violation Prediction

  Published: 14 May, 2025 |  at  11:08 AM

  

  89.01 🤔

  This paper develops a Deep Learning model with a novel data smoothing technique to predict fine-grained on-street parking violation rates in Thessaloniki, Greece, using indirect features like weather and time, achieving improved accuracy (MAE of 0.146) over baseline methods.

• LENSLLM: Unveiling Fine-Tuning Dynamics for LLM Selection

  Published: 8 May, 2025 |  at  06:16 PM

  

  86.80 🤔

  LENSLLM introduces a Hessian-based PAC-Bayes framework and NTK-based scaling model for LLM selection, achieving up to 91.1% accuracy and 88.5% computational cost reduction by modeling fine-tuning dynamics across diverse tasks.