Author name cluster

Richard Jiang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

1 author row

TMLR Journal 2025 Journal Article

DNR-Pruning: Sparsity-Aware Pruning via Dying Neuron Reactivation in Convolutional Neural Networks

Boyuan Wang
Richard Jiang

In this paper, we challenge the conventional view of dead neurons—neurons that cease to activate—during deep neural network training. Traditionally regarded as problematic due to their association with optimization challenges and reduced model adaptability over training epochs, dead neurons are often seen as a hindrance. However, we present a novel perspective, demonstrating that they can be effectively leveraged to enhance network sparsity. Specifically, we propose DNR-Pruning, dying neuron reactivation based sparsity-aware pruning approach for convolutional neural networks (CNNs) that exploits the behavior of individual neurons during training. Through a systematic exploration of hyperparameter configurations, we show that dying neurons can be harnessed to improve pruning algorithms. Our method dynamically monitors the occurrence of dying neurons, enabling adaptive sparsification throughout CNN training. Extensive experiments on diverse datasets demonstrate that DNR-Pruning outperforms existing sparsity-aware pruning techniques while achieving competitive results compared to state-of-the-art methods. These findings suggest that dying neurons can serve as an efficient mechanism for network compression and resource optimization in CNNs, opening new avenues for more efficient and high-performance deep learning models.

PDF Details

NeurIPS Conference 2025 Conference Paper

Energy Landscape-Aware Vision Transformers: Layerwise Dynamics and Adaptive Task-Specific Training via Hopfield States

Runze Xia
Richard Jiang

Recent advances in Vision Transformers (ViTs) have shown remarkable performance across vision tasks, yet their deep, uniform layer structure introduces significant computational overhead. In this work, we explore the emergent dynamics of ViT layers through the lens of energy-based memory systems, drawing a connection between self-attention and modern Hopfield networks. We introduce a novel metric—Layer Instability Index (LII)—derived from the operational softmax mode and its variability, to quantify the metastability of each Transformer layer over time. Our analysis reveals that certain layers exhibit consistent convergence to attractor-like states, suggesting functional specialisation and early stabilisation. Leveraging this insight, we propose an adaptive training framework that dynamically freezes or skips stable layers based on their energy landscape behavior. Our method reduces training costs while maintaining or improving accuracy. Extensive experiments on ViT-S/B/L on CUB-200-2011, CIFAR-10/100, Food-101, Stanford Dogs, and Beans demonstrate the generality and efficiency of our approach. This work provides new theoretical and practical perspectives for energy-aware optimisation of deep Transformer models.

PDF Details

JBHI Journal 2025 Journal Article

Modeling Brain Aging With Explainable Triamese ViT: Towards Deeper Insights Into Autism Disorder

Zhaonian Zhang
Vaneet Aggarwal
Plamen Angelov
Richard Jiang

Machine learning, particularly through advanced imaging techniques such as three-dimensional Magnetic Resonance Imaging (MRI), has significantly improved medical diagnostics. This is especially critical for diagnosing complex conditions like Alzheimer’s disease. Our study introduces Triamese-ViT, an innovative Tri-structure of Vision Transformers (ViTs) that incorporates a built-in interpretability function, it has structure-aware explainability that allows for the identification and visualization of key features or regions contributing to the prediction, integrates information from three perspectives to enhance brain age estimation. This method not only increases accuracy but also improves interoperability with existing techniques. When evaluated, Triamese-ViT demonstrated superior performance and produced insightful attention maps. We applied these attention maps to the analysis of natural aging and the diagnosis of Autism Spectrum Disorder (ASD). The results aligned with those from occlusion analysis, identifying the Cingulum, Rolandic Operculum, Thalamus, and Vermis as important regions in normal aging, and highlighting the Thalamus and Caudate Nucleus as key regions for ASD diagnosis.

Details DOI

IJCAI Conference 2023 Conference Paper

User-Centric Democratization towards Social Value Aligned Medical AI Services

Zhaonian Zhang
Richard Jiang

Democratic AI, aiming at developing AI systems aligned with human values, holds promise for making AI services accessible to people. However, concerns have been raised regarding the participation of non-technical individuals, potentially undermining the carefully designed values of AI systems by experts. In this paper, we investigate Democratic AI, define it mathematically, and propose a user-centric evolutionary democratic AI (u-DemAI) framework. This framework maximizes the social values of cloud-based AI services by incorporating user feedback and emulating human behavior in a community via a user-in-the-loop iteration. We apply our framework to a medical AI service for brain age estimation and demonstrate that non-expert users can consistently contribute to improving AI systems through a natural democratic process. The u-DemAI framework presents a mathematical interpretation of Democracy for AI, conceptualizing it as a natural computing process. Our experiments successfully show that involving non-tech individuals can help improve performance and simultaneously mitigate bias in AI models developed by AI experts, showcasing the potential for Democratic AI to benefit end users and regain control over AI services that shape various aspects of our lives, including our health.

PDF Details DOI

JBHI Journal 2022 Journal Article

Private Facial Prediagnosis as an Edge Service for Parkinson's DBS Treatment Valuation

Richard Jiang
Paul Chazot
Nicola Pavese
Danny Crookes
Ahmed Bouridane
M. Emre Celebi

Facial phenotyping for medical prediagnosis has recently been successfully exploited as a novel way for the preclinical assessment of a range of rare genetic diseases, where facial biometrics is revealed to have rich links to underlying genetic or medical causes. In this paper, we aim to extend this facial prediagnosis technology for a more general disease, Parkinson's Diseases (PD), and proposed an Artificial-Intelligence-of-Things (AIoT) edge-oriented privacy-preserving facial prediagnosis framework to analyze the treatment of Deep Brain Stimulation (DBS) on PD patients. In the proposed framework, a novel edge-based privacy-preserving framework is proposed to implement private deep facial diagnosis as a service over an AIoT-oriented information theoretically secure multi-party communication scheme, while data privacy has been a primary concern toward a wider exploitation of Electronic Health and Medical Records (EHR/EMR) over cloud-based medical services. In our experiments with a collected facial dataset from PD patients, for the first time, we proved that facial patterns could be used to evaluate the facial difference of PD patients undergoing DBS treatment. We further implemented a privacy-preserving information theoretical secure deep facial prediagnosis framework that can achieve the same accuracy as the non-encrypted one, showing the potential of our facial prediagnosis as a trustworthy edge service for grading the severity of PD in patients.

Details DOI

AAAI Conference 2014 Conference Paper

Deep Salience: Visual Salience Modeling via Deep Belief Propagation

Richard Jiang
Danny Crookes

Visual salience is an intriguing phenomenon observed in biological neural systems. Numerous attempts have been made to model visual salience mathematically using various feature contrasts, either locally or globally. However, these algorithmic models tend to ignore the problem’s biological solutions, in which visual salience appears to arise during the propagation of visual stimuli along the visual cortex. In this paper, inspired by the conjecture that salience arises from deep propagation along the visual cortex, we present a Deep Salience model where a multi-layer model based on successive Markov random fields (sMRF) is proposed to analyze the input image successively through its deep belief propagation. As a result, the foreground object can be automatically separated from the background in a fully unsupervised way. Experimental evaluation on the benchmark dataset validated that our Deep Salience model can consistently outperform eleven state-of-the-art salience models, yielding the higher rates in the precision-recall tests and attaining the best F-measure and mean-square error in the experiments.

PDF Details

AAAI Conference 2012 Conference Paper

Visual Saliency Estimation through Manifold Learning

Richard Jiang
Danny Crookes

Saliency detection has been a desirable way for robotic vision to find the most noticeable objects in a scene. In this paper, a robust manifold based saliency estimation method has been developed to help capture the most salient objects in front of robotic eyes, namely cameras. In the proposed approach, an image is considered as a manifold of visual signals (stimuli) spreading over a connected grid, and local visual stimuli are compared against the global image variation to model the visual saliency. With this model, manifold learning is then applied to minimize the local variation while keeping the global contrast, and turns the RGB image into a multi channel image. After the projection through manifold learning, histogram based contrast is then computed for saliency modeling of all channels of the projected images, and mutual information is introduced to evaluate each single channel saliency map against prior knowledge to provide cues for the fusion of multiple channels. In the last step, the fusion procedure combines all single channel saliency maps according to their mutual information score, and generates the final saliency map. In our experiment, the proposed method is evaluated using one of the largest publicly available image datasets. The experimental results validated that our algorithm consistently outperforms the state of the art unsupervised saliency detection methods, yielding higher precision and better recall rates. Furthermore, the proposed method is tested on a video where a moving camera is trying to catch up with the walking person a salient object in the video sequence. Our experimental results demonstrated that the proposed approach can successful accomplish this task, revealing its potential use for similar robotic applications.

PDF Details