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infestation    音标拼音: [ɪnf'ɛst'eʃən]
n. 群袭,出没,横行

群袭,出没,横行

infestation
n 1: the state of being invaded or overrun by parasites
2: a swarm of insects that attack plants; "a plague of
grasshoppers" [synonym: {infestation}, {plague}]


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英文字典中文字典相关资料:


  • Not All Inconsistency Is Equal: Decomposing LVLM Uncertainty into . . .
    Our method decomposes uncertainty into two complementary metrics: Belief Divergence, which quantifies ambiguity by measuring the separation between viewpoints, and Belief Conflict, which captures direct logical contradictions
  • dblp: Not All Inconsistency Is Equal: Decomposing LVLM Uncertainty into . . .
    Bibliographic details on Not All Inconsistency Is Equal: Decomposing LVLM Uncertainty into Belief Divergence and Belief Conflict
  • Decomposing Uncertainty for Large Language Models through Input . . .
    In this paper, we introduce an uncertainty decomposition framework for LLMs, called input clarification ensembling, which can be applied to any pre-trained LLM Our approach generates a set of clarifications for the input, feeds them into an LLM, and ensembles the corresponding predictions
  • Detecting Misbehaviors of Large Vision-Language Models by. . .
    We introduce a unified evidential perspective that quantifies distinct uncertainty types to effectively detect diverse misbehaviors, providing a principled solution for improving LVLM reliability
  • Decomposing Uncertainty for Large Language Models through Input . . .
    ①衡量了不同子模型之间的不一致性（近似表示model uncertainty）。 ②衡量了每个子模型的平均不确定性（近似表示 data uncertainty）。 然而直接将BNN框架应用在LLM上会有很多局限性，因此作者开发设计了一个与BNN几乎完全对称的框架。 Input Clarification Ensembling
  • NeurIPS 2025 Papers
    Uncertainty-Based Smooth Policy Regularisation for Reinforcement Learning with Few Demonstrations Lost in Transmission: When and Why LLMs Fail to Reason Globally PPMStereo: Pick-and-Play Memory Construction for Consistent Dynamic Stereo Matching Uncertain Knowledge Graph Completion via Semi-Supervised Confidence Distribution Learning
  • NeurIPS 2024 Papers
    Dataset Decomposition: Faster LLM Training with Variable Sequence Length Curriculum Towards Exact Gradient-based Training on Analog In-memory Computing Identifiability Analysis of Linear ODE Systems with Hidden Confounders AHA: Human-Assisted Out-of-Distribution Generalization and Detection
  • 北大联合字节提出多模态评估流程ConBench：揭示VLM的不一致性_问题_模型_答案
    自我诊断的 prompt 及其回答构造成新的 prompt，反馈给 LVLM 以生成更高质量的 Caption。 论文在 LLaVA-NeXT-34B 和 MiniGemini-34B 进行了实验，并在 ConBench 的 ConScore [C] 指标上进行了评估。 值得注意的是，LLaVA-NeXT-34B 的得分提高了 9 1 个点，而 MiniGemini 的总体提升为 9 6 个点。
  • Seeing It or Not? Interpretable Vision-aware Latent . . . - GitHub
    Recent promising Large Vision-Language Models (LVLMs) are notorious for generating outputs that are inconsistent with the visual content, a challenge known as hallucination





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