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caller    音标拼音: [k'ɔlɚ]
n. 访客,召集员,传唤员
a. 新鲜的

访客,召集员,传唤员新鲜的

caller
呼叫程式

caller
呼叫程式

caller
adj 1: providing coolness; "a cooling breeze"; "`caller' is a
Scottish term as in `a caller breeze'"
2: fresh; "caller fish"
n 1: a social or business visitor; "the room was a mess because
he hadn't expected company" [synonym: {caller}, {company}]
2: an investor who buys a call option
3: the bettor in a card game who matches the bet and calls for a
show of hands
4: a person who announces the changes of steps during a dance;
"you need a fiddler and a caller for country dancing" [synonym:
{caller}, {caller-out}]
5: someone who proclaims or summons in a loud voice; "the
callers were mothers summoning their children home for
dinner"
6: the person who convenes a meeting; "who is the caller of this
meeting?"
7: the person initiating a telephone call; "there were so many
callers that he finally disconnected the telephone" [synonym:
{caller}, {caller-up}, {phoner}, {telephoner}]

Caller \Call"er\, n.
One who calls.
[1913 Webster]


Caller \Cal"ler\, a. [Scot.]
1. Cool; refreshing; fresh; as, a caller day; the caller air.
--Jamieson.
[1913 Webster]

2. Fresh; in good condition; as, caller berrings.
[1913 Webster]

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英文字典中文字典相关资料:


  • Clever: A Curated Benchmark for Formally Verified Code Generation
    We introduce CLEVER, the first curated benchmark for evaluating the generation of specifications and formally verified code in Lean The benchmark comprises of 161 programming problems; it evaluates both formal speci-fication generation and implementation synthesis from natural language, requiring formal correctness proofs for both
  • CLEVER: A Curated Benchmark for Formally Verified Code Generation
    TL;DR: We introduce CLEVER, a hand-curated benchmark for verified code generation in Lean It requires full formal specs and proofs No few-shot method solves all stages, making it a strong testbed for synthesis and formal reasoning
  • The Clever Hans Mirage: A Comprehensive Survey on Spurious. . .
    Back in the early 20th century, a horse named Hans appeared to perform arithmetic and other intellectual tasks during exhibitions in Germany, while it actually relied solely on involuntary cues in
  • Evaluating the Robustness of Neural Networks: An Extreme Value. . .
    Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness The proposed CLEVER score is attack-agnostic and is computationally feasible for large neural networks
  • Counterfactual Debiasing for Fact Verification
    579 In this paper, we have proposed a novel counter- factual framework CLEVER for debiasing fact- checking models Unlike existing works, CLEVER is augmentation-free and mitigates biases on infer- ence stage In CLEVER, the claim-evidence fusion model and the claim-only model are independently trained to capture the corresponding information
  • Contrastive Learning Via Equivariant Representation - OpenReview
    In this paper, we revisit the roles of augmentation strategies and equivariance in improving CL's efficacy We propose CLeVER (Contrastive Learning Via Equivariant Representation), a novel equivariant contrastive learning framework compatible with augmentation strategies of arbitrary complexity for various mainstream CL backbone models
  • On the Planning Abilities of Large Language Models : A Critical . . .
    While, as we mentioned earlier, there can be thorny “clever hans” issues about humans prompting LLMs, an automated verifier mechanically backprompting the LLM doesn’t suffer from these We tested this setup on a subset of the failed instances in the one-shot natural language prompt configuration using GPT-4, given its larger context window
  • Dual-Model Defense: Safeguarding Diffusion Models from Membership . . .
    Membership inference and memorization is a key challenge with diffusion models Mitigating such vulnerabilities is hence an important topic The idea of using an ensemble of model is clever
  • Submissions | OpenReview
    Leaving the barn door open for Clever Hans: Simple features predict LLM benchmark answers Lorenzo Pacchiardi, Marko Tesic, Lucy G Cheke, Jose Hernandez-Orallo 27 Sept 2024 (modified: 05 Feb 2025) Submitted to ICLR 2025 Readers: Everyone





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