Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Chrysos, Grigorios G."'
Autor:
Xiong, Zheyang, Cai, Ziyang, Cooper, John, Ge, Albert, Papageorgiou, Vasilis, Sifakis, Zack, Giannou, Angeliki, Lin, Ziqian, Yang, Liu, Agarwal, Saurabh, Chrysos, Grigorios G, Oymak, Samet, Lee, Kangwook, Papailiopoulos, Dimitris
Large Language Models (LLMs) have demonstrated remarkable in-context learning (ICL) capabilities. In this study, we explore a surprising phenomenon related to ICL: LLMs can perform multiple, computationally distinct ICL tasks simultaneously, during a
Externí odkaz:
http://arxiv.org/abs/2410.05603
We introduce MIGS (Multi-Identity Gaussian Splatting), a novel method that learns a single neural representation for multiple identities, using only monocular videos. Recent 3D Gaussian Splatting (3DGS) approaches for human avatars require per-identi
Externí odkaz:
http://arxiv.org/abs/2407.07284
Extracting Implicit Neural Representations (INRs) on video data poses unique challenges due to the additional temporal dimension. In the context of videos, INRs have predominantly relied on a frame-only parameterization, which sacrifices the spatiote
Externí odkaz:
http://arxiv.org/abs/2406.19299
Adversarial attacks in Natural Language Processing apply perturbations in the character or token levels. Token-level attacks, gaining prominence for their use of gradient-based methods, are susceptible to altering sentence semantics, leading to inval
Externí odkaz:
http://arxiv.org/abs/2405.04346
In this work, we introduce a method that learns a single dynamic neural radiance field (NeRF) from monocular talking face videos of multiple identities. NeRFs have shown remarkable results in modeling the 4D dynamics and appearance of human faces. Ho
Externí odkaz:
http://arxiv.org/abs/2403.19920
Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these
Externí odkaz:
http://arxiv.org/abs/2403.13134
Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate \emph{scaled} ResNet in the limit of infinitely deep and wide neural ne
Externí odkaz:
http://arxiv.org/abs/2403.09889
Autor:
Oldfield, James, Georgopoulos, Markos, Chrysos, Grigorios G., Tzelepis, Christos, Panagakis, Yannis, Nicolaou, Mihalis A., Deng, Jiankang, Patras, Ioannis
The Mixture of Experts (MoE) paradigm provides a powerful way to decompose dense layers into smaller, modular computations often more amenable to human interpretation, debugging, and editability. However, a major challenge lies in the computational c
Externí odkaz:
http://arxiv.org/abs/2402.12550
Large Language Models (LLMs) are susceptible to Jailbreaking attacks, which aim to extract harmful information by subtly modifying the attack query. As defense mechanisms evolve, directly obtaining harmful information becomes increasingly challenging
Externí odkaz:
http://arxiv.org/abs/2402.09177
Despite the remarkable capabilities of deep neural networks in image recognition, the dependence on activation functions remains a largely unexplored area and has yet to be eliminated. On the other hand, Polynomial Networks is a class of models that
Externí odkaz:
http://arxiv.org/abs/2401.17992