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of 927
pro vyhledávání: '"Kang, U."'
Given an edge-incomplete graph, how can we accurately find the missing links? The link prediction in edge-incomplete graphs aims to discover the missing relations between entities when their relationships are represented as a graph. Edge-incomplete g
Externí odkaz:
http://arxiv.org/abs/2405.11911
How can we compress language models without sacrificing accuracy? The number of compression algorithms for language models is rapidly growing to benefit from remarkable advances of recent language models without side effects due to the gigantic size
Externí odkaz:
http://arxiv.org/abs/2401.15347
How can we recommend cold-start bundles to users? The cold-start problem in bundle recommendation is crucial because new bundles are continuously created on the Web for various marketing purposes. Despite its importance, existing methods for cold-sta
Externí odkaz:
http://arxiv.org/abs/2310.03813
Autor:
Xu, Huiwen, Kang, U
Given a set of pre-trained models, how can we quickly and accurately find the most useful pre-trained model for a downstream task? Transferability measurement is to quantify how transferable is a pre-trained model learned on a source task to a target
Externí odkaz:
http://arxiv.org/abs/2308.05986
Given a pretrained encoder-based language model, how can we accurately compress it without retraining? Retraining-free structured pruning algorithms are crucial in pretrained language model compression due to their significantly reduced pruning cost
Externí odkaz:
http://arxiv.org/abs/2308.03449
How can we efficiently and accurately analyze an irregular tensor in a dual-way streaming setting where the sizes of two dimensions of the tensor increase over time? What types of anomalies are there in the dual-way streaming setting? An irregular te
Externí odkaz:
http://arxiv.org/abs/2305.18376
Autor:
Jang, Jun-Gi, Shim, Sooyeon, Egay, Vladimir, Lee, Jeeyong, Park, Jongmin, Chae, Suhyun, Kang, U
How can we accurately identify new memory workloads while classifying known memory workloads? Verifying DRAM (Dynamic Random Access Memory) using various workloads is an important task to guarantee the quality of DRAM. A crucial component in the proc
Externí odkaz:
http://arxiv.org/abs/2212.08817
When recommending personalized top-$k$ items to users, how can we recommend the items diversely to them while satisfying their needs? Aggregately diversified recommender systems aim to recommend a variety of items across whole users without sacrifici
Externí odkaz:
http://arxiv.org/abs/2211.01328
How can we recommend existing bundles to users accurately? How can we generate new tailored bundles for users? Recommending a bundle, or a group of various items, has attracted widespread attention in e-commerce owing to the increased satisfaction of
Externí odkaz:
http://arxiv.org/abs/2210.15460