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pro vyhledávání: '"Lee., P."'
Advances in diffusion models for generative artificial intelligence have recently propagated to the time series (TS) domain, demonstrating state-of-the-art performance on various tasks. However, prior works on TS diffusion models often borrow the fra
Externí odkaz:
http://arxiv.org/abs/2410.14488
Retrieval-Augmented Generation (RAG) enhances language models by retrieving and incorporating relevant external knowledge. However, traditional retrieve-and-generate processes may not be optimized for real-world scenarios, where queries might require
Externí odkaz:
http://arxiv.org/abs/2410.13339
Large Vision-Language Models (LVLMs) demonstrate impressive capabilities in generating detailed and coherent responses from visual inputs. However, they are prone to generate hallucinations due to an over-reliance on language priors. To address this
Externí odkaz:
http://arxiv.org/abs/2410.13321
Developing effective text summarizers remains a challenge due to issues like hallucinations, key information omissions, and verbosity in LLM-generated summaries. This work explores using LLM-generated feedback to improve summary quality by aligning t
Externí odkaz:
http://arxiv.org/abs/2410.13116
As we enter the era of big data, collecting high-quality data is very important. However, collecting data by humans is not only very time-consuming but also expensive. Therefore, many scientists have devised various methods to collect data using comp
Externí odkaz:
http://arxiv.org/abs/2410.12561
Previous deep learning approaches for survival analysis have primarily relied on ranking losses to improve discrimination performance, which often comes at the expense of calibration performance. To address such an issue, we propose a novel contrasti
Externí odkaz:
http://arxiv.org/abs/2410.11340
Autor:
Yi, Minseok, Lee, Daehee, Gola, Alberto, Merzi, Stefano, Penna, Michele, Lee, Jae Sung, Cherry, Simon R., Kwon, Sun Il
Positron emission tomography (PET) is the most sensitive biomedical imaging modality for non-invasively detecting and visualizing positron-emitting radiopharmaceuticals within a subject. In PET, measuring the time-of-flight (TOF) information for each
Externí odkaz:
http://arxiv.org/abs/2410.12161
This paper explores the current state of generative AI policies of computer science conferences and offers guidelines for policy adoption.
Externí odkaz:
http://arxiv.org/abs/2410.11977
Improved Regret Bound for Safe Reinforcement Learning via Tighter Cost Pessimism and Reward Optimism
This paper studies the safe reinforcement learning problem formulated as an episodic finite-horizon tabular constrained Markov decision process with an unknown transition kernel and stochastic reward and cost functions. We propose a model-based algor
Externí odkaz:
http://arxiv.org/abs/2410.10158
We develop the viscosity method for the homogenization of an obstacle problem with highly oscillating obstacles. The associated operator, in non-divergence form, is linear and elliptic with variable coefficients. We first construct a highly oscillati
Externí odkaz:
http://arxiv.org/abs/2410.09378