Zobrazeno 1 - 10
of 855
pro vyhledávání: '"Chen, Guihai"'
The surging demand for cloud computing resources, driven by the rapid growth of sophisticated large-scale models and data centers, underscores the critical importance of efficient and adaptive resource allocation. As major tech enterprises deploy mas
Externí odkaz:
http://arxiv.org/abs/2408.01000
Low-bit quantization has become widespread for compressing image super-resolution (SR) models for edge deployment, which allows advanced SR models to enjoy compact low-bit parameters and efficient integer/bitwise constructions for storage compression
Externí odkaz:
http://arxiv.org/abs/2406.06649
Image super-resolution (SR) methods typically model degradation to improve reconstruction accuracy in complex and unknown degradation scenarios. However, extracting degradation information from low-resolution images is challenging, which limits the m
Externí odkaz:
http://arxiv.org/abs/2311.14282
Pervasive mobile AI applications primarily employ one of the two learning paradigms: cloud-based learning (with powerful large models) or on-device learning (with lightweight small models). Despite their own advantages, neither paradigm can effective
Externí odkaz:
http://arxiv.org/abs/2311.11083
Autor:
Zhang, Hongxuan, Liu, Zhining, Zhao, Yao, Zheng, Jiaqi, Zhuang, Chenyi, Gu, Jinjie, Chen, Guihai
In this work, we propose FastCoT, a model-agnostic framework based on parallel decoding without any further training of an auxiliary model or modification to the LLM itself. FastCoT uses a size-varying context window whose size changes with position
Externí odkaz:
http://arxiv.org/abs/2311.08263
Publikováno v:
The Web Conference, 2024
User response prediction is essential in industrial recommendation systems, such as online display advertising. Among all the features in recommendation models, user behaviors are among the most critical. Many works have revealed that a user's behavi
Externí odkaz:
http://arxiv.org/abs/2308.08487
Many large vision models have been deployed on the cloud for real-time services. Meanwhile, fresh samples are continuously generated on the served mobile device. How to leverage the device-side samples to improve the cloud-side large model becomes a
Externí odkaz:
http://arxiv.org/abs/2303.10361
Automated bidding, an emerging intelligent decision making paradigm powered by machine learning, has become popular in online advertising. Advertisers in automated bidding evaluate the cumulative utilities and have private financial constraints over
Externí odkaz:
http://arxiv.org/abs/2301.13020
One-Time Model Adaptation to Heterogeneous Clients: An Intra-Client and Inter-Image Attention Design
The mainstream workflow of image recognition applications is first training one global model on the cloud for a wide range of classes and then serving numerous clients, each with heterogeneous images from a small subset of classes to be recognized. F
Externí odkaz:
http://arxiv.org/abs/2211.06276
In Click-through rate (CTR) prediction models, a user's interest is usually represented as a fixed-length vector based on her history behaviors. Recently, several methods are proposed to learn an attentive weight for each user behavior and conduct we
Externí odkaz:
http://arxiv.org/abs/2210.15154