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Researcher profile

Steven C. H. Hoi

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Compositional Coding for Collaborative Filtering

    2019

    Efficiency is crucial to the online recommender systems, especially for the ones which needs to deal with tens of millions of users and items. Because representing users and items as binary vectors for Collaborative Filtering …

  2. DivideMix: Learning with Noisy Labels as Semi-supervised Learning

    2020 · arXiv (Cornell University)

    Deep neural networks are known to be annotation-hungry. Numerous efforts have been devoted to reducing the annotation cost when learning with deep networks. Two prominent directions include learning with noisy labels and semi-supervised learning by …

  3. Adaptive cost-sensitive online classification

    2019 · Singapore Management University Institutional Knowledge (InK) (Singapore Management University)

    National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiative

  4. CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

    2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

    Pre-trained models for Natural Languages (NL) like BERT and GPT have been recently shown to transfer well to Programming Languages (PL) and largely benefit a broad set of code-related tasks. Despite their success, most current …

  5. BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models

    2023 · arXiv (Cornell University)

    The cost of vision-and-language pre-training has become increasingly prohibitive due to end-to-end training of large-scale models. This paper proposes BLIP-2, a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image …

  6. CodeT5+: Open Code Large Language Models for Code Understanding and Generation

    2023

    Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. However, existing code LLMs have two main limitations. First, they often adopt a specific architecture (encoder-only or decoder-only) or …