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pro vyhledávání: '"Zilberstein, A"'
The advent of large language models (LLMs) has sparked significant interest in using natural language for preference learning. However, existing methods often suffer from high computational burdens, taxing human supervision, and lack of interpretabil
Externí odkaz:
http://arxiv.org/abs/2412.07207
Autor:
Bercovich, Akhiad, Ronen, Tomer, Abramovich, Talor, Ailon, Nir, Assaf, Nave, Dabbah, Mohammad, Galil, Ido, Geifman, Amnon, Geifman, Yonatan, Golan, Izhak, Haber, Netanel, Karpas, Ehud, Koren, Roi, Levy, Itay, Molchanov, Pavlo, Mor, Shahar, Moshe, Zach, Nabwani, Najeeb, Puny, Omri, Rubin, Ran, Schen, Itamar, Shahaf, Ido, Tropp, Oren, Argov, Omer Ullman, Zilberstein, Ran, El-Yaniv, Ran
Large language models (LLMs) have demonstrated remarkable capabilities, but their adoption is limited by high computational costs during inference. While increasing parameter counts enhances accuracy, it also widens the gap between state-of-the-art c
Externí odkaz:
http://arxiv.org/abs/2411.19146
Although randomization has long been used in concurrent programs, formal methods for reasoning about this mixture of effects have lagged behind. In particular, no existing program logics can express specifications about the distributions of outcomes
Externí odkaz:
http://arxiv.org/abs/2411.11662
While there is a long tradition of reasoning about termination (and nontermination) in the context of program analysis, specialized logics are typically needed to give different termination guarantees. This includes partial correctness, where termina
Externí odkaz:
http://arxiv.org/abs/2411.00197
Publikováno v:
Proc. ACM Program. Lang. 9, POPL, Article 19 (January 2025)
Programs increasingly rely on randomization in applications such as cryptography and machine learning. Analyzing randomized programs has been a fruitful research direction, but there is a gap when programs also exploit nondeterminism (for concurrency
Externí odkaz:
http://arxiv.org/abs/2410.22540
Graphons are continuous models that represent the structure of graphs and allow the generation of graphs of varying sizes. We propose Scalable Implicit Graphon Learning (SIGL), a scalable method that combines implicit neural representations (INRs) an
Externí odkaz:
http://arxiv.org/abs/2410.17464
Publikováno v:
Discrete Analysis, September 2024
The sum of the absolute values of the Fourier coefficients of a function $f:\mathbb{F}_2^n \to \mathbb{R}$ is called the spectral norm of $f$. Green and Sanders' quantitative version of Cohen's idempotent theorem states that if the spectral norm of $
Externí odkaz:
http://arxiv.org/abs/2409.10634
Score Distillation Sampling (SDS) has been pivotal for leveraging pre-trained diffusion models in downstream tasks such as inverse problems, but it faces two major challenges: $(i)$ mode collapse and $(ii)$ latent space inversion, which become more p
Externí odkaz:
http://arxiv.org/abs/2406.16683
We present a novel \emph{weakest pre calculus} for \emph{reasoning about quantitative hyperproperties} over \emph{nondeterministic and probabilistic} programs. Whereas existing calculi allow reasoning about the expected value that a quantity assumes
Externí odkaz:
http://arxiv.org/abs/2404.05097
Autor:
Zilberstein, Noam
Starting with Hoare Logic over 50 years ago, numerous program logics have been devised to reason about the diverse programs encountered in the real world. This includes reasoning about computational effects, particularly those effects that cause the
Externí odkaz:
http://arxiv.org/abs/2401.04594