Zobrazeno 1 - 10
of 21 192
pro vyhledávání: '"P, Diao"'
Autor:
Wang, Xinran, Le, Qi, Ahmed, Ammar, Diao, Enmao, Zhou, Yi, Baracaldo, Nathalie, Ding, Jie, Anwar, Ali
Ensuring that generative AI systems align with human values is essential but challenging, especially when considering multiple human values and their potential trade-offs. Since human values can be personalized and dynamically change over time, the d
Externí odkaz:
http://arxiv.org/abs/2410.19198
Publikováno v:
38th Conference on Neural Information Processing Systems (NeurIPS 2024)
Federated Learning (FL) is an evolving paradigm that enables multiple parties to collaboratively train models without sharing raw data. Among its variants, Vertical Federated Learning (VFL) is particularly relevant in real-world, cross-organizational
Externí odkaz:
http://arxiv.org/abs/2410.17986
Cross-modal metric learning is a prominent research topic that bridges the semantic heterogeneity between vision and language. Existing methods frequently utilize simple cosine or complex distance metrics to transform the pairwise features into a sim
Externí odkaz:
http://arxiv.org/abs/2410.15266
In this paper, we investigate a transmission eigenvalue problem that couples the principles of acoustics and elasticity. This problem naturally arises when studying fluid-solid interactions and constructing bubbly-elastic structures to create metamat
Externí odkaz:
http://arxiv.org/abs/2410.11167
Parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA), are known to enhance training efficiency in Large Language Models (LLMs). Due to the limited parameters of LoRA, recent studies seek to combine LoRA with Mixture-of-Experts
Externí odkaz:
http://arxiv.org/abs/2410.10054
Although the diffusion model has achieved remarkable performance in the field of image generation, its high inference delay hinders its wide application in edge devices with scarce computing resources. Therefore, many training-free sampling methods h
Externí odkaz:
http://arxiv.org/abs/2410.07679
Continual learning (CL) is designed to learn new tasks while preserving existing knowledge. Replaying samples from earlier tasks has proven to be an effective method to mitigate the forgetting of previously acquired knowledge. However, the current re
Externí odkaz:
http://arxiv.org/abs/2410.06645
This paper presents a recurrent neural network approach to simulating mechanical ventilator pressure. The traditional mechanical ventilator has a control pressure that is monitored by a medical practitioner and can behave incorrectly if the proper pr
Externí odkaz:
http://arxiv.org/abs/2410.06552
Autor:
Wang, Haibo, Xu, Zhiyang, Cheng, Yu, Diao, Shizhe, Zhou, Yufan, Cao, Yixin, Wang, Qifan, Ge, Weifeng, Huang, Lifu
Video Large Language Models (Video-LLMs) have demonstrated remarkable capabilities in coarse-grained video understanding, however, they struggle with fine-grained temporal grounding. In this paper, we introduce Grounded-VideoLLM, a novel Video-LLM ad
Externí odkaz:
http://arxiv.org/abs/2410.03290
We introduce Functional Group-Aware Representations for Small Molecules (FARM), a novel foundation model designed to bridge the gap between SMILES, natural language, and molecular graphs. The key innovation of FARM lies in its functional group-aware
Externí odkaz:
http://arxiv.org/abs/2410.02082