Zobrazeno 1 - 10
of 322
pro vyhledávání: '"Ma, Pingchuan"'
Photonic tensor cores (PTCs) are essential building blocks for optical artificial intelligence (AI) accelerators based on programmable photonic integrated circuits. Most PTC designs today are manually constructed, with low design efficiency and unsat
Externí odkaz:
http://arxiv.org/abs/2410.01313
Autor:
Kotovenko, Dmytro, Grebenkova, Olga, Sarafianos, Nikolaos, Paliwal, Avinash, Ma, Pingchuan, Poursaeed, Omid, Mohan, Sreyas, Fan, Yuchen, Li, Yilei, Ranjan, Rakesh, Ommer, Björn
While style transfer techniques have been well-developed for 2D image stylization, the extension of these methods to 3D scenes remains relatively unexplored. Existing approaches demonstrate proficiency in transferring colors and textures but often st
Externí odkaz:
http://arxiv.org/abs/2409.17917
Autor:
Cappellazzo, Umberto, Kim, Minsu, Chen, Honglie, Ma, Pingchuan, Petridis, Stavros, Falavigna, Daniele, Brutti, Alessio, Pantic, Maja
Multimodal large language models (MLLMs) have recently become a focal point of research due to their formidable multimodal understanding capabilities. For example, in the audio and speech domains, an LLM can be equipped with (automatic) speech recogn
Externí odkaz:
http://arxiv.org/abs/2409.12319
A major challenge of AI + Science lies in their inherent incompatibility: today's AI is primarily based on connectionism, while science depends on symbolism. To bridge the two worlds, we propose a framework to seamlessly synergize Kolmogorov-Arnold N
Externí odkaz:
http://arxiv.org/abs/2408.10205
Autor:
Fuest, Michael, Ma, Pingchuan, Gui, Ming, Fischer, Johannes S., Hu, Vincent Tao, Ommer, Bjorn
Diffusion Models are popular generative modeling methods in various vision tasks, attracting significant attention. They can be considered a unique instance of self-supervised learning methods due to their independence from label annotation. This sur
Externí odkaz:
http://arxiv.org/abs/2407.00783
Autor:
Xiao, Qiao, Ma, Pingchuan, Fernandez-Lopez, Adriana, Wu, Boqian, Yin, Lu, Petridis, Stavros, Pechenizkiy, Mykola, Pantic, Maja, Mocanu, Decebal Constantin, Liu, Shiwei
The recent success of Automatic Speech Recognition (ASR) is largely attributed to the ever-growing amount of training data. However, this trend has made model training prohibitively costly and imposed computational demands. While data pruning has bee
Externí odkaz:
http://arxiv.org/abs/2406.18373
Autor:
Fernandez-Lopez, Adriana, Chen, Honglie, Ma, Pingchuan, Yin, Lu, Xiao, Qiao, Petridis, Stavros, Liu, Shiwei, Pantic, Maja
Pre-trained models have been a foundational approach in speech recognition, albeit with associated additional costs. In this study, we propose a regularization technique that facilitates the training of visual and audio-visual speech recognition mode
Externí odkaz:
http://arxiv.org/abs/2406.17614
The finite-difference time-domain (FDTD) method, which is important in photonic hardware design flow, is widely adopted to solve time-domain Maxwell equations. However, FDTD is known for its prohibitive runtime cost, taking minutes to hours to simula
Externí odkaz:
http://arxiv.org/abs/2406.17810
Differentiable causal discovery has made significant advancements in the learning of directed acyclic graphs. However, its application to real-world datasets remains restricted due to the ubiquity of latent confounders and the requirement to learn ma
Externí odkaz:
http://arxiv.org/abs/2406.10537
Autor:
Wang, Xunguang, Wu, Daoyuan, Ji, Zhenlan, Li, Zongjie, Ma, Pingchuan, Wang, Shuai, Li, Yingjiu, Liu, Yang, Liu, Ning, Rahmel, Juergen
Jailbreaking is an emerging adversarial attack that bypasses the safety alignment deployed in off-the-shelf large language models (LLMs) and has evolved into multiple categories: human-based, optimization-based, generation-based, and the recent indir
Externí odkaz:
http://arxiv.org/abs/2406.05498