Zobrazeno 1 - 10
of 1 692
pro vyhledávání: '"Wang, Zichen"'
We introduce a general framework for solving partial differential equations (PDEs) using generative diffusion models. In particular, we focus on the scenarios where we do not have the full knowledge of the scene necessary to apply classical solvers.
Externí odkaz:
http://arxiv.org/abs/2406.17763
Autor:
Zheng, Da, Song, Xiang, Zhu, Qi, Zhang, Jian, Vasiloudis, Theodore, Ma, Runjie, Zhang, Houyu, Wang, Zichen, Adeshina, Soji, Nisa, Israt, Mottini, Alejandro, Cui, Qingjun, Rangwala, Huzefa, Zeng, Belinda, Faloutsos, Christos, Karypis, George
Publikováno v:
KDD 2024
Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challenging. We developed GraphStorm, which provides an end-to-end solution
Externí odkaz:
http://arxiv.org/abs/2406.06022
We present a simple algorithm for differentiable rendering of surfaces represented by Signed Distance Fields (SDF), which makes it easy to integrate rendering into gradient-based optimization pipelines. To tackle visibility-related derivatives that m
Externí odkaz:
http://arxiv.org/abs/2405.08733
The molecular-based ferrimagnetic insulator V(TCNE)x has gained recent interest for efficient spin-wave excitation due to its low Gilbert damping ratio a=4E-5, and narrow ferromagnetic resonance linewidth f=1Oe. Here we report a clean spin pumping si
Externí odkaz:
http://arxiv.org/abs/2403.16429
Magnetic resonance methods offer a unique chance for in-depth study of conductive organic material systems, not only accounts for number of charge carriers, but also allows manipulations of spin dynamics of particles. Here we present a study of conti
Externí odkaz:
http://arxiv.org/abs/2403.15965
Few-shot object detection (FSOD) aims at extending a generic detector for novel object detection with only a few training examples. It attracts great concerns recently due to the practical meanings. Meta-learning has been demonstrated to be an effect
Externí odkaz:
http://arxiv.org/abs/2401.07629
Autor:
Shi, Bowen, Zhao, Peisen, Wang, Zichen, Zhang, Yuhang, Wang, Yaoming, Li, Jin, Dai, Wenrui, Zou, Junni, Xiong, Hongkai, Tian, Qi, Zhang, Xiaopeng
Vision-language foundation models, represented by Contrastive language-image pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual tasks. However, existing approaches primarily focus on training model
Externí odkaz:
http://arxiv.org/abs/2401.06397
Neural fields have become widely used in various fields, from shape representation to neural rendering, and for solving partial differential equations (PDEs). With the advent of hybrid neural field representations like Instant NGP that leverage small
Externí odkaz:
http://arxiv.org/abs/2312.05984
We study the federated pure exploration problem of multi-armed bandits and linear bandits, where $M$ agents cooperatively identify the best arm via communicating with the central server. To enhance the robustness against latency and unavailability of
Externí odkaz:
http://arxiv.org/abs/2310.11015
Autor:
Wang, Zifeng, Wang, Zichen, Srinivasan, Balasubramaniam, Ioannidis, Vassilis N., Rangwala, Huzefa, Anubhai, Rishita
Foundation models (FMs) are able to leverage large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks. However, FMs developed for biomedical domains have largely remained unimodal, i.e., independently trained a
Externí odkaz:
http://arxiv.org/abs/2310.03320