publications

2023

  1. LoBaSS: Gauging Learnability in Supervised Fine-tuning Data
    Zhou, Haotian,  Liu, Tingkai, Ma, Qianli, Yuan, Jianbo, Liu, Pengfei, You, Yang, and Yang, Hongxia
    arXiv, Oct 2023
    arxiv

    We propose a loss-based LLM post-training data selection method method, which compresses Alpaca dataset by 16x while improving model performance on AlpacaEval.

  2. Let’s reward step by step: Step-Level reward model as the Navigators for Reasoning
    Ma, Qianli, Zhou, Haotian,  Liu, Tingkai, Yuan, Jianbo, Liu, Pengfei, You, Yang, and Yang, Hongxia
    arXiv, Oct 2023
    arxiv

    We propose a heuristic greedy search algorithm based on step-level reward for improved LLM reasoning capabilities during inference. We also present a new automated way to generate massive amount of step-level reward signal for code generation tasks based on mutation testing.

  3. Video-Teller: Enhancing Cross-Modal Generation with Fusion and Decoupling
    Liu, Haogeng, Fan, Qihang,  Liu, Tingkai, Yang, Linjie, Tao, Yunzhe, Huang, Huaibo, He, Ran, and Yang, Hongxia
    arXiv, Oct 2023
    arxiv

    We propose Video-Teller, a video-language foundation model with fine-grained modality alignment to enhance video-to-text generation tasks.

  5. Method and apparatus for encoding odorants
    Aurel A. Lazar, Chung-Heng Yeh, and Liu, Tingkai
    US Patent US11674937B2, , Jun 2023
    patent

    We developed a biomimetic method and apparatus for encoding odorants, which has been patented.

  6. The functional logic of odor information processing in the Drosophila antennal lobe
    Lazar, Aurel A.,  Liu, Tingkai, and Yeh, Chung-Heng
    PLOS Computational Biology, Apr 2023
    paper / code

    We provide theoretical and computational evidence for the functional logic of the Antennal Lobe as a robust odorant object identity recovery processor with ON-OFF event-based processing.

2022

  1. Divisive Normalization Circuits Faithfully Represent Auditory and Visual Sensory Stimuli
    Lazar, Aurel A.,  Liu, Tingkai, and Zhou, Yiyin
    bioRxiv, Sep 2022
    biorxiv

    We demonstrate both theoretically and computationally that the Divisive Normalization Processor (DNP) is an invertible operator that can faithfully represent input information given sufficient output samples, with application to different sensory modalities.

  2. Odorant Mixture Separation in Drosophila Early Olfactory System
    Lazar, Aurel A.,  Liu, Tingkai, Yeh, C.-H., and Zhou, Yiyin
    bioRxiv, Sep 2022
    biorxiv

    We propose a feedback divisive normalization architecture of the Mushroom Body Calyx circuit in the Drosophila olfactory system for odorant demixing. We show that the biological network is highly optimized for processing odorant mixtures.

2021

  1. Accelerating with FlyBrainLab the Discovery of the Functional Logic of the Drosophila Brain in the Connectomic and Synaptomic Era
    Lazar, Aurel A.,  Liu, Tingkai, Turkcan, Mehmet K., and Zhou, Yiyin
    eLife, Feb 2021
    paper / website

    We developed FlyBrainLab, an open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of modeled executable brain circuits in Drosophila.

2020

  1. An Odorant Encoding Machine for Sampling, Reconstruction and Robust Representation of Odorant Identity
    Lazar, Aurel A.,  Liu, Tingkai, and Yeh, Chung-Heng
    ICASSP 2020, May 2020
    paper

    We present a bio-mimetic odorant encoding machine for sampling, reconstruction, and robust representation of odorant identity in the Drosophila olfactory system.

2019

  1. The Fruit Fly Brain Observatory: From Structure to Function
    Ukani, N. H., Yeh, C.-H., Tomkins, A., Zhou, Y., Florescu, D., Ortiz, C. L., Huang, Y.-C., Wang, C.-T., Turkcan, M. K.,  Liu, Tingkai, Richmond, P., Lo, C.-C., Coca, D., Chiang, A.-S., and Lazar, A. A.
    bioRxiv, Mar 2019
    biorxiv / website

    The Fruit Fly Brain Observatory is a platform that aims to bridge the gap between structural and functional data in neuroscience research on Drosophila. It provides tools for exploring and analyzing various types of fruit fly brain data, including connectome and functional imaging data.

2017