Author name cluster

Bo Xiao

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6 papers

2 author rows

EAAI Journal 2025 Journal Article

A novel approach of causality matrix embedded into the Graph Neural Network for forecasting the price of Bitcoin

Xinxin Luo
Wei Yin
Bo Xiao
Jia Cao

Accurately forecasting Bitcoin prices presents significant challenges due to its high volatility and the complex interactions among macroeconomic and crypto-specific variables. Traditional forecasting models often rely on correlations, which fail to capture the intrinsic causal relationships that drive price fluctuations. In this paper, we propose a novel method that integrates a Cause & Effect (C&E) Matrix within a Graph Neural Network (GNN) to explicitly model these causal dependencies. Unlike correlations, causal relationships remain relatively stable even under changing market conditions, making them more reliable for robust and interpretable forecasting. Our approach begins with causal analysis to identify the key variables influencing Bitcoin’s price, after which these causal links are translated into directed graph structures. These structures allow for the extraction of spatio-temporal features via GNN, capturing the underlying dynamics of Bitcoin’s price movements. Experimental results demonstrate that our C&E embedded GNN significantly improves short-term Bitcoin price forecasts compared to baseline models, highlighting the critical role of causality in enhancing prediction accuracy and model interpretability in volatile markets.

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NeurIPS Conference 2025 Conference Paper

DenoiseRotator: Enhance Pruning Robustness for LLMs via Importance Concentration

Tianteng Gu
Bei Liu
Bo Xiao
Ke Zeng
Jiacheng Liu
Yanmin Qian

Pruning is a widely used technique to compress large language models (LLMs) by removing unimportant weights, but it often suffers from significant performance degradation—especially under semi-structured sparsity constraints. Existing pruning methods primarily focus on estimating the importance of individual weights, which limits their ability to preserve critical capabilities of the model. In this work, we propose a new perspective: rather than merely selecting which weights to prune, we first redistribute parameter importance to make the model inherently more amenable to pruning. By minimizing the information entropy of normalized importance scores, our approach concentrates importance onto a smaller subset of weights, thereby enhancing pruning robustness. We instantiate this idea through DenoiseRotator, which applies learnable orthogonal transformations to the model’s weight matrices. Our method is model-agnostic and can be seamlessly integrated with existing pruning techniques such as Magnitude, SparseGPT, and Wanda. Evaluated on LLaMA3, Qwen2. 5, and Mistral models under 50% unstructured and 2: 4 semi-structured sparsity, DenoiseRotator consistently improves perplexity and zero-shot accuracy. For instance, on LLaMA3-70B pruned with SparseGPT at 2: 4 semi-structured sparsity, DenoiseRotator reduces the perplexity gap to the dense model by 58%, narrowing the degradation from 8. 1 to 3. 4 points.

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JBHI Journal 2024 Journal Article

Dietary Assessment With Multimodal ChatGPT: A Systematic Analysis

Frank P.-W. Lo
Jianing Qiu
Zeyu Wang
Junhong Chen
Bo Xiao
Wu Yuan
Stamatia Giannarou
Gary Frost

Conventional approaches to dietary assessment are primarily grounded in self-reporting methods or structured interviews conducted under the supervision of dietitians. These methods, however, are often subjective, inaccurate, and time-intensive. Although artificial intelligence (AI)-based solutions have been devised to automate the dietary assessment process, prior AI methodologies tackle dietary assessment in a fragmented landscape (e. g. , merely recognizing food types or estimating portion size) and encounter challenges in their ability to generalize across a diverse range of food categories, dietary behaviors, and cultural contexts. Recently, the emergence of multimodal foundation models, such as GPT-4V, has exhibited transformative potential across a wide range of tasks in various research domains. These models have demonstrated remarkable generalist intelligence and accuracy, owing to their large-scale pre-training on broad datasets and substantially scaled model size. In this study, we explore the application of GPT-4V powering multimodal ChatGPT for dietary assessment, along with prompt engineering and passive monitoring techniques. We evaluated the proposed pipeline using a self-collected, semi free-living dietary intake dataset, captured through wearable cameras. Our findings reveal that GPT-4V excels in food detection under challenging conditions without any fine-tuning or adaptation using food-specific datasets. By guiding the model with specific language prompts (e. g. , African cuisine), it shifts from recognizing common staples like rice and bread to accurately identifying regional dishes like banku and ugali. Another standout feature of GPT-4V is its contextual awareness. GPT-4V can leverage surrounding objects as scale references to deduce the portion sizes of food items, further facilitating the process of dietary assessment.

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JBHI Journal 2023 Journal Article

Large AI Models in Health Informatics: Applications, Challenges, and the Future

Jianing Qiu
Lin Li
Jiankai Sun
Jiachuan Peng
Peilun Shi
Ruiyang Zhang
Yinzhao Dong
Kyle Lam

Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A prime example is ChatGPT, whose capability has compelled people's imagination about the far-reaching influence that large AI models can have and their potential to transform different domains of our lives. In health informatics, the advent of large AI models has brought new paradigms for the design of methodologies. The scale of multi-modal data in the biomedical and health domain has been ever-expanding especially since the community embraced the era of deep learning, which provides the ground to develop, validate, and advance large AI models for breakthroughs in health-related areas. This article presents a comprehensive review of large AI models, from background to their applications. We identify seven key sectors in which large AI models are applicable and might have substantial influence, including: 1) bioinformatics; 2) medical diagnosis; 3) medical imaging; 4) medical informatics; 5) medical education; 6) public health; and 7) medical robotics. We examine their challenges, followed by a critical discussion about potential future directions and pitfalls of large AI models in transforming the field of health informatics.

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EAAI Journal 2018 Journal Article

Tracking control design of interval type-2 polynomial-fuzzy-model-based systems with time-varying delay

Bo Xiao
H.K. Lam
Xiaozhan Yang
Yan Yu
Hongliang Ren

In this paper, the tracking control design for the interval type-2 (IT2) polynomial-fuzzy-model-based (PFMB) control system subject to time-varying delay situation is investigated. The tracking control system is formed of the IT2 polynomial fuzzy model representing a nonlinear system with time-varying delay, the stable reference model and the IT2 polynomial fuzzy controller. The control objective is to design a proper IT2 polynomial fuzzy controller which is capable of driving the states of the polynomial fuzzy model to track those in the reference model and the tracking performance is evaluated and improved by the H ∞ performance index. Also, to handle the uncertainty in the membership functions, the property of IT2 fuzzy sets is utilized to enhance the fuzzy controller’s robustness against uncertainty. In addition, considering the effect of time-varying delay, the Lyapunov–Krasovskii functional based approach is adopted to facilitate the delay-dependent stability analysis. Stability conditions depending on the time-varying delay characteristic with the consideration of H ∞ performance are obtained in terms of sum-of-squares (SOS). Furthermore, the information of the IT2 membership functions is employed in the stability analysis to relax the stability conditions, both membership-function-independent (MFI) and membership-function-dependent (MFD) approaches are presented to develop the stability conditions. Simulation examples are presented to verify the effectiveness of the proposed tracking control approach.

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ICML Conference 2016 Conference Paper

Deep Speech 2: End-to-End Speech Recognition in English and Mandarin

Dario Amodei
Sundaram Ananthanarayanan
Rishita Anubhai
Jingliang Bai
Eric Battenberg
Carl Case
Jared Casper
Bryan Catanzaro

We show that an end-to-end deep learning approach can be used to recognize either English or Mandarin Chinese speech–two vastly different languages. Because it replaces entire pipelines of hand-engineered components with neural networks, end-to-end learning allows us to handle a diverse variety of speech including noisy environments, accents and different languages. Key to our approach is our application of HPC techniques, enabling experiments that previously took weeks to now run in days. This allows us to iterate more quickly to identify superior architectures and algorithms. As a result, in several cases, our system is competitive with the transcription of human workers when benchmarked on standard datasets. Finally, using a technique called Batch Dispatch with GPUs in the data center, we show that our system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.

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