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pro vyhledávání: '"LI, Jiacheng"'
Generative recommendation (GR) is an emerging paradigm that tokenizes items into discrete tokens and learns to autoregressively generate the next tokens as predictions. Although effective, GR models operate in a transductive setting, meaning they can
Externí odkaz:
http://arxiv.org/abs/2410.02939
In-loop filtering (ILF) is a key technology for removing the artifacts in image/video coding standards. Recently, neural network-based in-loop filtering methods achieve remarkable coding gains beyond the capability of advanced video coding standards,
Externí odkaz:
http://arxiv.org/abs/2407.10926
Image resampling is a basic technique that is widely employed in daily applications, such as camera photo editing. Recent deep neural networks (DNNs) have made impressive progress in performance by introducing learned data priors. Still, these method
Externí odkaz:
http://arxiv.org/abs/2407.09935
Interfacial hydration structures are crucial in wide-ranging applications, including battery, colloid, lubrication etc. Multivalent ions like Mg2+ and La3+ show irreplaceable roles in these applications, which are hypothesized due to their unique int
Externí odkaz:
http://arxiv.org/abs/2406.18827
Autor:
Zhao, Joshua C., Bagchi, Saurabh, Avestimehr, Salman, Chan, Kevin S., Chaterji, Somali, Dimitriadis, Dimitris, Li, Jiacheng, Li, Ninghui, Nourian, Arash, Roth, Holger R.
Deep learning has shown incredible potential across a vast array of tasks and accompanying this growth has been an insatiable appetite for data. However, a large amount of data needed for enabling deep learning is stored on personal devices and recen
Externí odkaz:
http://arxiv.org/abs/2405.03636
This paper introduces BLaIR, a series of pretrained sentence embedding models specialized for recommendation scenarios. BLaIR is trained to learn correlations between item metadata and potential natural language context, which is useful for retrievin
Externí odkaz:
http://arxiv.org/abs/2403.03952
As required by Industry 4.0, companies will move towards flexible and individual manufacturing. To succeed in this transition, convergence of 5G and time-sensitive networks (TSN) is the most promising technology and has thus attracted considerable in
Externí odkaz:
http://arxiv.org/abs/2312.10356
In Member Inference (MI) attacks, the adversary try to determine whether an instance is used to train a machine learning (ML) model. MI attacks are a major privacy concern when using private data to train ML models. Most MI attacks in the literature
Externí odkaz:
http://arxiv.org/abs/2311.00919
Publikováno v:
ACM MM 2023
Owing to the unrestricted nature of the content in the training data, large text-to-image diffusion models, such as Stable Diffusion (SD), are capable of generating images with potentially copyrighted or dangerous content based on corresponding textu
Externí odkaz:
http://arxiv.org/abs/2308.02552
Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequence modeling to understand user preferences. While promising, these app
Externí odkaz:
http://arxiv.org/abs/2305.13731