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Jiaxiang Wu

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12 papers

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JBHI Journal 2025 Journal Article

Verification is All You Need: Prompting Large Language Models for Zero-Shot Clinical Coding

Shaoxin Li
Can Zheng
Jiaxiang Wu
Qinwei Xu
Xingkun Xu
Hanyang Wang
Yingkai Sun
Zhian Bai

Clinical coding translates medical information from Electronic Health Records (EHRs) into structured codes such as ICD-10, which are essential for healthcare applications. Advances in deep learning and natural language processing have enabled automatic ICD coding models to achieve notable accuracy metrics on in-domain datasets when adequately trained. However, the scarcity of clinical medical texts and the variability across different datasets pose significant challenges, making it difficult for current state-of-the-art models to ensure robust generalization performance across diverse data distributions. Recent advances in Large Language Models (LLMs), such as GPT-4o, have shown great generalization capabilities across general domains and potential in medical information processing tasks. However, their performance in generating clinical codes remains suboptimal. In this study, we propose a novel ICD coding paradigm based on code verification to leverage the capabilities of LLMs. Instead of directly generating accurate codes from a vast code space, we simplify the task by verifying the code assignment from a given candidate set. Through extensive experiments, we demonstrate that LLMs function more effectively as code verifiers rather than code generators, with GPT-4o achieving the best performance on the CodiEsp dataset under zero-shot settings. Furthermore, our results indicate that LLM-based systems can perform on par with state-of-the-art clinical coding systems while offering superior generalizability across institutions, languages, and ICD versions.

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AAAI Conference 2023 Conference Paper

DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery – a Focus on Affinity Prediction Problems with Noise Annotations

Yuanfeng Ji
Lu Zhang
Jiaxiang Wu
Bingzhe Wu
Lanqing Li
Long-Kai Huang
Tingyang Xu
Yu Rong

AI-aided drug discovery (AIDD) is gaining popularity due to its potential to make the search for new pharmaceuticals faster, less expensive, and more effective. Despite its extensive use in numerous fields (e.g., ADMET prediction, virtual screening), little research has been conducted on the out-of-distribution (OOD) learning problem with noise. We present DrugOOD, a systematic OOD dataset curator and benchmark for AIDD. Particularly, we focus on the drug-target binding affinity prediction problem, which involves both macromolecule (protein target) and small-molecule (drug compound). DrugOOD offers an automated dataset curator with user-friendly customization scripts, rich domain annotations aligned with biochemistry knowledge, realistic noise level annotations, and rigorous benchmarking of SOTA OOD algorithms, as opposed to only providing fixed datasets. Since the molecular data is often modeled as irregular graphs using graph neural network (GNN) backbones, DrugOOD also serves as a valuable testbed for graph OOD learning problems. Extensive empirical studies have revealed a significant performance gap between in-distribution and out-of-distribution experiments, emphasizing the need for the development of more effective schemes that permit OOD generalization under noise for AIDD.

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AAAI Conference 2022 Conference Paper

GNN-Retro: Retrosynthetic Planning with Graph Neural Networks

Peng Han
Peilin Zhao
Chan Lu
Junzhou Huang
Jiaxiang Wu
Shuo Shang
Bin Yao
Xiangliang Zhang

Retrosynthetic planning plays an important role in the field of organic chemistry, which could generate a synthetic route for the target product. The synthetic route is a series of reactions which are started from the available molecules. The most challenging problem in the generation of the synthetic route is the large search space of the candidate reactions. Estimating the cost of candidate reactions has been proved effectively to prune the search space, which could achieve a higher accuracy with the same search iteration. And the estimation of one reaction is comprised of the estimations of all its reactants. So, how to estimate the cost of these reactants will directly influence the quality of results. To get a better performance, we propose a new framework, named GNN-Retro, for retrosynthetic planning problem by combining graph neural networks (GNN) and the latest search algorithm. The structure of GNN in our framework could incorporate the information of neighboring molecules, which will improve the estimation accuracy of our framework. The experiments on the USPTO dataset show that our framework could outperform the state-of-the-art methods with a large margin under the same settings.

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AAAI Conference 2022 Conference Paper

Self-Supervised Pre-training for Protein Embeddings Using Tertiary Structures

Yuzhi Guo
Jiaxiang Wu
Hehuan Ma
Junzhou Huang

The protein tertiary structure largely determines its interaction with other molecules. Despite its importance in various structure-related tasks, fully-supervised data are often timeconsuming and costly to obtain. Existing pre-training models mostly focus on amino-acid sequences or multiple sequence alignments, while the structural information is not yet exploited. In this paper, we propose a self-supervised pre-training model for learning structure embeddings from protein tertiary structures. Native protein structures are perturbed with random noise, and the pre-training model aims at estimating gradients over perturbed 3D structures. Specifically, we adopt SE(3)-invariant features as the model inputs and reconstruct gradients over 3D coordinates with SE(3)equivariance preserved. Such a paradigm avoids the usage of sophisticated SE(3)-equivariant models, and dramatically improves the computational efficiency of pre-training models. We demonstrate the effectiveness of our pre-training model on two downstream tasks, protein structure quality assessment (QA) and protein-protein interaction (PPI) site prediction. Hierarchical structure embeddings are extracted to enhance corresponding prediction models. Extensive experiments indicate that such structure embeddings consistently improve the prediction accuracy for both downstream tasks.

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AAAI Conference 2021 Conference Paper

PSSM-Distil: Protein Secondary Structure Prediction (PSSP) on Low-Quality PSSM by Knowledge Distillation with Contrastive Learning

Qin Wang
Boyuan Wang
Zhenlei Xu
Jiaxiang Wu
Peilin Zhao
Zhen Li
Sheng Wang
Junzhou Huang

Protein secondary structure prediction (PSSP) is an essential task in computational biology. To achieve the accurate PSSP, the general and vital feature engineering is to use multiple sequence alignment (MSA) for Position-Specific Scoring Matrix (PSSM) extraction. However, when only low-quality PSSM can be obtained due to poor sequence homology, previous PSSP accuracy (merely around 65%) is far from practical usage for subsequent tasks. In this paper, we propose a novel PSSM-Distil framework for PSSP on low-quality PSSM, which not only enhances the PSSM feature at a lower level but also aligns the feature distribution at a higher level. In practice, the PSSM-Distil first exploits the proteins with high-quality PSSM to achieve a teacher network for PSSP in a full-supervised way. Under the guidance of the teacher network, the low-quality PSSM and corresponding student network with low discriminating capacity are effectively resolved by feature enhancement through EnhanceNet and distribution alignment through knowledge distillation with contrastive learning. Further, our PSSM-Distil supports the input from a pre-trained protein sequence language BERT model to provide auxiliary information, which is designed to address the extremely low-quality PSSM cases, i. e. , no homologous sequence. Extensive experiments demonstrate the proposed PSSM-Distil outperforms state-of-the-art models on PSSP by 6% on average and nearly 8% in extremely low-quality cases on public benchmarks, BC40 and CB513.

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AAAI Conference 2020 Conference Paper

Few Shot Network Compression via Cross Distillation

Haoli Bai
Jiaxiang Wu
Irwin King
Michael Lyu

Model compression has been widely adopted to obtain lightweighted deep neural networks. Most prevalent methods, however, require ﬁne-tuning with sufﬁcient training data to ensure accuracy, which could be challenged by privacy and security issues. As a compromise between privacy and performance, in this paper we investigate few shot network compression: given few samples per class, how can we effectively compress the network with negligible performance drop? The core challenge of few shot network compression lies in high estimation errors from the original network during inference, since the compressed network can easily over-ﬁts on the few training instances. The estimation errors could propagate and accumulate layer-wisely and ﬁnally deteriorate the network output. To address the problem, we propose cross distillation, a novel layer-wise knowledge distillation approach. By interweaving hidden layers of teacher and student network, layer-wisely accumulated estimation errors can be effectively reduced. The proposed method offers a general framework compatible with prevalent network compression techniques such as pruning. Extensive experiments n benchmark datasets demonstrate that cross distillation can signiﬁcantly improve the student network’s accuracy when only a few training instances are available.

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AAAI Conference 2020 Conference Paper

M-NAS: Meta Neural Architecture Search

Jiaxing Wang
Jiaxiang Wu
Haoli Bai
Jian Cheng

Neural Architecture Search (NAS) has recently outperformed hand-crafted networks in various areas. However, most prevalent NAS methods only focus on a pre-deﬁned task. For a previously unseen task, the architecture is either searched from scratch, which is inefﬁcient, or transferred from the one obtained on some other task, which might be sub-optimal. In this paper, we investigate a previously unexplored problem: whether a universal NAS method exists, such that task-aware architectures can be effectively generated? Towards this problem, we propose Meta Neural Architecture Search (M-NAS). To obtain task-speciﬁc architectures, M-NAS adopts a taskaware architecture controller for child model generation. Since optimal weights for different tasks and architectures span diversely, we resort to meta-learning, and learn metaweights that efﬁciently adapt to a new task on the corresponding architecture with only several gradient descent steps. Experimental results demonstrate the superiority of M-NAS against a number of competitive baselines on both toy regression and few shot classiﬁcation problems.

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NeurIPS Conference 2020 Conference Paper

Revisiting Parameter Sharing for Automatic Neural Channel Number Search

Jiaxing Wang
Haoli Bai
Jiaxiang Wu
Xupeng Shi
Junzhou Huang
Irwin King
Michael Lyu
Jian Cheng

Recent advances in neural architecture search inspire many channel number search algorithms~(CNS) for convolutional neural networks. To improve searching efficiency, parameter sharing is widely applied, which reuses parameters among different channel configurations. Nevertheless, it is unclear how parameter sharing affects the searching process. In this paper, we aim at providing a better understanding and exploitation of parameter sharing for CNS. Specifically, we propose affine parameter sharing~(APS) as a general formulation to unify and quantitatively analyze existing channel search algorithms. It is found that with parameter sharing, weight updates of one architecture can simultaneously benefit other candidates. However, it also results in less confidence in choosing good architectures. We thus propose a new strategy of parameter sharing towards a better balance between training efficiency and architecture discrimination. Extensive analysis and experiments demonstrate the superiority of the proposed strategy in channel configuration against many state-of-the-art counterparts on benchmark datasets.

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AAAI Conference 2019 Conference Paper

An Efficient Approach to Informative Feature Extraction from Multimodal Data

Lichen Wang
Jiaxiang Wu
Shao-Lun Huang
Lizhong Zheng
Xiangxiang Xu
Lin Zhang
Junzhou Huang

One primary focus in multimodal feature extraction is to find the representations of individual modalities that are maximally correlated. As a well-known measure of dependence, the Hirschfeld-Gebelein-Rényi (HGR) maximal correlation becomes an appealing objective because of its operational meaning and desirable properties. However, the strict whitening constraints formalized in the HGR maximal correlation limit its application. To address this problem, this paper proposes Soft-HGR, a novel framework to extract informative features from multiple data modalities. Specifically, our framework prevents the “hard” whitening constraints, while simultaneously preserving the same feature geometry as in the HGR maximal correlation. The objective of Soft-HGR is straightforward, only involving two inner products, which guarantees the efficiency and stability in optimization. We further generalize the framework to handle more than two modalities and missing modalities. When labels are partially available, we enhance the discriminative power of the feature representations by making a semi-supervised adaptation. Empirical evaluation implies that our approach learns more informative feature mappings and is more efficient to optimize.

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NeurIPS Conference 2019 Conference Paper

Double Quantization for Communication-Efficient Distributed Optimization

Yue Yu
Jiaxiang Wu
Longbo Huang

Modern distributed training of machine learning models often suffers from high communication overhead for synchronizing stochastic gradients and model parameters. In this paper, to reduce the communication complexity, we propose \emph{double quantization}, a general scheme for quantizing both model parameters and gradients. Three communication-efficient algorithms are proposed based on this general scheme. Specifically, (i) we propose a low-precision algorithm AsyLPG with asynchronous parallelism, (ii) we explore integrating gradient sparsification with double quantization and develop Sparse-AsyLPG, (iii) we show that double quantization can be accelerated by the momentum technique and design accelerated AsyLPG. We establish rigorous performance guarantees for the algorithms, and conduct experiments on a multi-server test-bed with real-world datasets to demonstrate that our algorithms can effectively save transmitted bits without performance degradation, and significantly outperform existing methods with either model parameter or gradient quantization.

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AAAI Conference 2016 Conference Paper

Shoot to Know What: An Application of Deep Networks on Mobile Devices

Jiaxiang Wu
Qinghao Hu
Cong Leng
Jian Cheng

Convolutional neural networks (CNNs) have achieved impressive performance in a wide range of computer vision areas. However, the application on mobile devices remains intractable due to the high computation complexity. In this demo, we propose the Quantized CNN (Q-CNN), an efﬁcient framework for CNN models, to fulﬁll efﬁcient and accurate image classiﬁcation on mobile devices. Our Q-CNN framework dramatically accelerates the computation and reduces the storage/memory consumption, so that mobile devices can independently run an ImageNet-scale CNN model. Experiments on the ILSVRC-12 dataset demonstrate 4 ∼ 6× speedup and 15 ∼ 20× compression, with merely one percentage drop in the classiﬁcation accuracy. Based on the Q-CNN framework, even mobile devices can accurately classify images within one second.

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JMLR Journal 2014 Journal Article

Bayesian Co-Boosting for Multi-modal Gesture Recognition

Jiaxiang Wu
Jian Cheng

With the development of data acquisition equipment, more and more modalities become available for gesture recognition. However, there still exist two critical issues for multi-modal gesture recognition: how to select discriminative features for recognition and how to fuse features from different modalities. In this paper, we propose a novel Bayesian Co-Boosting framework for multi-modal gesture recognition. Inspired by boosting learning and co-training method, our proposed framework combines multiple collaboratively trained weak classifiers to construct the final strong classifier for the recognition task. During each iteration round, we randomly sample a number of feature subsets and estimate weak classifier's parameters for each subset. The optimal weak classifier and its corresponding feature subset are retained for strong classifier construction. Furthermore, we define an upper bound of training error and derive the update rule of instance's weight, which guarantees the error upper bound to be minimized through iterations. For demonstration, we present an implementation of our framework using hidden Markov models as weak classifiers. We perform extensive experiments using the ChaLearn MMGR and ChAirGest data sets, in which our approach achieves 97.63% and 96.53% accuracy respectively on each publicly available data set. [abs] [ pdf ][ bib ] &copy JMLR 2014. ( edit, beta )

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