Zobrazeno 1 - 10
of 458
pro vyhledávání: '"Zhang, Tianle"'
Autor:
Zhang, Tianle, Ma, Langtian, Yan, Yuchen, Zhang, Yuchen, Wang, Kai, Yang, Yue, Guo, Ziyao, Shao, Wenqi, You, Yang, Qiao, Yu, Luo, Ping, Zhang, Kaipeng
Recent text-to-video (T2V) technology advancements, as demonstrated by models such as Gen2, Pika, and Sora, have significantly broadened its applicability and popularity. Despite these strides, evaluating these models poses substantial challenges. Pr
Externí odkaz:
http://arxiv.org/abs/2406.08845
While Hyperbolic Graph Neural Network (HGNN) has recently emerged as a powerful tool dealing with hierarchical graph data, the limitations of scalability and efficiency hinder itself from generalizing to deep models. In this paper, by envisioning dep
Externí odkaz:
http://arxiv.org/abs/2406.01282
Autor:
Dong, Yi, Mu, Ronghui, Zhang, Yanghao, Sun, Siqi, Zhang, Tianle, Wu, Changshun, Jin, Gaojie, Qi, Yi, Hu, Jinwei, Meng, Jie, Bensalem, Saddek, Huang, Xiaowei
In the burgeoning field of Large Language Models (LLMs), developing a robust safety mechanism, colloquially known as "safeguards" or "guardrails", has become imperative to ensure the ethical use of LLMs within prescribed boundaries. This article prov
Externí odkaz:
http://arxiv.org/abs/2406.02622
Autor:
Zhang, Tianle, Li, Dongjiang, Li, Yihang, Zeng, Zecui, Zhao, Lin, Sun, Lei, Chen, Yue, Wei, Xuelong, Zhan, Yibing, Li, Lusong, He, Xiaodong
The advancements in embodied AI are increasingly enabling robots to tackle complex real-world tasks, such as household manipulation. However, the deployment of robots in these environments remains constrained by the lack of comprehensive bimanual-mob
Externí odkaz:
http://arxiv.org/abs/2405.18860
Autor:
Zhang, Tianle, Guan, Jiayi, Zhao, Lin, Li, Yihang, Li, Dongjiang, Zeng, Zecui, Sun, Lei, Chen, Yue, Wei, Xuelong, Li, Lusong, He, Xiaodong
Offline reinforcement learning (RL) aims to learn optimal policies from previously collected datasets. Recently, due to their powerful representational capabilities, diffusion models have shown significant potential as policy models for offline RL is
Externí odkaz:
http://arxiv.org/abs/2405.18729
Autor:
Liu, Shuo, Ying, Kaining, Zhang, Hao, Yang, Yue, Lin, Yuqi, Zhang, Tianle, Li, Chuanhao, Qiao, Yu, Luo, Ping, Shao, Wenqi, Zhang, Kaipeng
This paper presents ConvBench, a novel multi-turn conversation evaluation benchmark tailored for Large Vision-Language Models (LVLMs). Unlike existing benchmarks that assess individual capabilities in single-turn dialogues, ConvBench adopts a three-l
Externí odkaz:
http://arxiv.org/abs/2403.20194
Although adversarial training (AT) has proven effective in enhancing the model's robustness, the recently revealed issue of fairness in robustness has not been well addressed, i.e. the robust accuracy varies significantly among different categories.
Externí odkaz:
http://arxiv.org/abs/2402.17729
Autor:
Zhang, Yuchen, Zhang, Tianle, Wang, Kai, Guo, Ziyao, Liang, Yuxuan, Bresson, Xavier, Jin, Wei, You, Yang
Graph condensation aims to reduce the size of a large-scale graph dataset by synthesizing a compact counterpart without sacrificing the performance of Graph Neural Networks (GNNs) trained on it, which has shed light on reducing the computational cost
Externí odkaz:
http://arxiv.org/abs/2402.05011
Autor:
Zhang, Tianle, Zhang, Yuchen, Wang, Kun, Wang, Kai, Yang, Beining, Zhang, Kaipeng, Shao, Wenqi, Liu, Ping, Zhou, Joey Tianyi, You, Yang
Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have raised growing concerns. As one of the most promising directions, graph condensation methods address these issues by employ
Externí odkaz:
http://arxiv.org/abs/2402.04924
Autor:
Mu, Ronghui, Marcolino, Leandro Soriano, Zhang, Tianle, Zhang, Yanghao, Huang, Xiaowei, Ruan, Wenjie
Reinforcement Learning (RL) has achieved remarkable success in safety-critical areas, but it can be weakened by adversarial attacks. Recent studies have introduced "smoothed policies" in order to enhance its robustness. Yet, it is still challenging t
Externí odkaz:
http://arxiv.org/abs/2312.06436