Zobrazeno 1 - 10
of 5 946
pro vyhledávání: '"You Yang"'
Recent advances in Generative Artificial Intelligence have fueled numerous applications, particularly those involving Generative Adversarial Networks (GANs), which are essential for synthesizing realistic photos and videos. However, efficiently train
Externí odkaz:
http://arxiv.org/abs/2411.03999
Autor:
Wang, Kai, Li, Zekai, Cheng, Zhi-Qi, Khaki, Samir, Sajedi, Ahmad, Vedantam, Ramakrishna, Plataniotis, Konstantinos N, Hauptmann, Alexander, You, Yang
Dataset distillation has demonstrated strong performance on simple datasets like CIFAR, MNIST, and TinyImageNet but struggles to achieve similar results in more complex scenarios. In this paper, we propose EDF (emphasizes the discriminative features)
Externí odkaz:
http://arxiv.org/abs/2410.17193
Autor:
Kagaya, Tomoyuki, Lou, Yuxuan, Yuan, Thong Jing, Lakshmi, Subramanian, Karlekar, Jayashree, Pranata, Sugiri, Murakami, Natsuki, Kinose, Akira, Oguri, Koki, Wick, Felix, You, Yang
In recent years, Large Language Models (LLMs) have demonstrated high reasoning capabilities, drawing attention for their applications as agents in various decision-making processes. One notably promising application of LLM agents is robotic manipulat
Externí odkaz:
http://arxiv.org/abs/2410.16919
Autor:
Ni, Jinjie, Song, Yifan, Ghosal, Deepanway, Li, Bo, Zhang, David Junhao, Yue, Xiang, Xue, Fuzhao, Zheng, Zian, Zhang, Kaichen, Shah, Mahir, Jain, Kabir, You, Yang, Shieh, Michael
Perceiving and generating diverse modalities are crucial for AI models to effectively learn from and engage with real-world signals, necessitating reliable evaluations for their development. We identify two major issues in current evaluations: (1) in
Externí odkaz:
http://arxiv.org/abs/2410.13754
Large language models (LLMs) have gained human trust due to their capabilities and helpfulness. However, this in turn may allow LLMs to affect users' mindsets by manipulating language. It is termed as gaslighting, a psychological effect. In this work
Externí odkaz:
http://arxiv.org/abs/2410.09181
Autor:
Zhao, Wangbo, Han, Yizeng, Tang, Jiasheng, Wang, Kai, Song, Yibing, Huang, Gao, Wang, Fan, You, Yang
Diffusion Transformer (DiT), an emerging diffusion model for image generation, has demonstrated superior performance but suffers from substantial computational costs. Our investigations reveal that these costs stem from the static inference paradigm,
Externí odkaz:
http://arxiv.org/abs/2410.03456
Understanding visual semantics embedded in consecutive characters is a crucial capability for both large language models (LLMs) and multi-modal large language models (MLLMs). This type of artifact possesses the unique characteristic that identical in
Externí odkaz:
http://arxiv.org/abs/2410.01733
Autor:
Sun, Jiankai, Curtis, Aidan, You, Yang, Xu, Yan, Koehle, Michael, Guibas, Leonidas, Chitta, Sachin, Schwager, Mac, Li, Hui
Generalizable long-horizon robotic assembly requires reasoning at multiple levels of abstraction. End-to-end imitation learning (IL) has been proven a promising approach, but it requires a large amount of demonstration data for training and often fai
Externí odkaz:
http://arxiv.org/abs/2409.16451
We present Pyramid Attention Broadcast (PAB), a real-time, high quality and training-free approach for DiT-based video generation. Our method is founded on the observation that attention difference in the diffusion process exhibits a U-shaped pattern
Externí odkaz:
http://arxiv.org/abs/2408.12588
Autor:
Li, Zekai, Guo, Ziyao, Zhao, Wangbo, Zhang, Tianle, Cheng, Zhi-Qi, Khaki, Samir, Zhang, Kaipeng, Sajedi, Ahmad, Plataniotis, Konstantinos N, Wang, Kai, You, Yang
Dataset Distillation aims to compress a large dataset into a significantly more compact, synthetic one without compromising the performance of the trained models. To achieve this, existing methods use the agent model to extract information from the t
Externí odkaz:
http://arxiv.org/abs/2408.03360