Zobrazeno 1 - 10
of 170
pro vyhledávání: '"Shou, Lidan"'
Schema and entity matching tasks are crucial for data integration and management. While large language models (LLMs) have shown promising results in these tasks, they suffer from hallucinations and confusion about task instructions. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2410.12480
Machine learning (ML) on tabular data is ubiquitous, yet obtaining abundant high-quality tabular data for model training remains a significant obstacle. Numerous works have focused on tabular data augmentation (TDA) to enhance the original table with
Externí odkaz:
http://arxiv.org/abs/2407.21523
Publikováno v:
Data Science and Engineering (2024)
Federated learning (FL) is a promising approach for learning a model from data distributed on massive clients without exposing data privacy. It works effectively in the ideal federation where clients share homogeneous data distribution and learning b
Externí odkaz:
http://arxiv.org/abs/2403.04146
Multi-modal multi-label emotion recognition (MMER) aims to identify relevant emotions from multiple modalities. The challenge of MMER is how to effectively capture discriminative features for multiple labels from heterogeneous data. Recent studies ar
Externí odkaz:
http://arxiv.org/abs/2312.10201
We present a novel inference scheme, self-speculative decoding, for accelerating Large Language Models (LLMs) without the need for an auxiliary model. This approach is characterized by a two-stage process: drafting and verification. The drafting stag
Externí odkaz:
http://arxiv.org/abs/2309.08168
Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people's routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor pa
Externí odkaz:
http://arxiv.org/abs/2104.05480
Federated Learning (FL) is a promising distributed learning paradigm, which allows a number of data owners (also called clients) to collaboratively learn a shared model without disclosing each client's data. However, FL may fail to proceed properly,
Externí odkaz:
http://arxiv.org/abs/2011.11160
Indoor location-based services (LBS), such as POI search and routing, are often built on top of typical indoor spatial queries. To support such queries and indoor LBS, multiple techniques including model/indexes and search algorithms have been propos
Externí odkaz:
http://arxiv.org/abs/2010.03910
This paper addresses the problem of key phrase extraction from sentences. Existing state-of-the-art supervised methods require large amounts of annotated data to achieve good performance and generalization. Collecting labeled data is, however, often
Externí odkaz:
http://arxiv.org/abs/1904.03898
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