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Haiyan Jiang

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7 papers

2 author rows

EAAI Journal 2026 Journal Article

Research on the connectivity between injection and production wells in the Hongqian in situ combustion project in Xinjiang based on machine learning

Shibao Yuan
Jia Song
Fengxiang Yang
Haiyan Jiang
Zihan Ren

Accurate assessment of injection-production connectivity is critical for optimizing the in-situ combustion process. Conventional methods often focus on individual geological or dynamic parameters, lacking a comprehensive evaluation framework. This study, centered on the Hongqian-1 Well Block pilot area, processed all logging curves through noise reduction and segmentation‌, then quantified structural similarity between injection and production well curves as a static similarity coefficient to reflect geological connectivity‌. Machine learning algorithms were used to identify the most influential dynamic features. These features were integrated with the static coefficient to establish a hybrid geological-dynamic model that generated connectivity coefficients Validation via production trends and tracer tests confirmed the model's accuracy, demonstrating that the calculated inter-well connectivity aligns with actual production behavior and tracer results. The static coefficient effectively characterizes reservoir heterogeneity and significantly influences connectivity patterns‌. This method provides a time-efficient, reliable basis for understanding in-situ combustion connectivity and guiding injection-production adjustments‌.

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AAAI Conference 2024 Conference Paper

G–LIME: Statistical Learning for Local Interpretations of Deep Neural Networks Using Global Priors (Abstract Reprint)

Xuhong Li
Haoyi Xiong
Xingjian Li
Xiao Zhang
Ji Liu
Haiyan Jiang
Zeyu Chen
Dejing Dou

To explain the prediction result of a Deep Neural Network (DNN) model based on a given sample, LIME [1] and its derivatives have been proposed to approximate the local behavior of the DNN model around the data point via linear surrogates. Though these algorithms interpret the DNN by finding the key features used for classification, the random interpolations used by LIME would perturb the explanation result and cause the instability and inconsistency between repetitions of LIME computations. To tackle this issue, we propose G-LIME that extends the vanilla LIME through high-dimensional Bayesian linear regression using the sparsity and informative global priors. Specifically, with a dataset representing the population of samples (e.g., the training set), G-LIME first pursues the global explanation of the DNN model using the whole dataset. Then, with a new data point, -LIME incorporates an modified estimator of ElasticNet-alike to refine the local explanation result through balancing the distance to the global explanation and the sparsity/feature selection in the explanation. Finally, G-LIME uses Least Angle Regression (LARS) and retrieves the solution path of a modified ElasticNet under varying -regularization, to screen and rank the importance of features [2] as the explanation result. Through extensive experiments on real world tasks, we show that the proposed method yields more stable, consistent, and accurate results compared to LIME.

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ICML Conference 2024 Conference Paper

NDOT: Neuronal Dynamics-based Online Training for Spiking Neural Networks

Haiyan Jiang
Giulia De Masi
Huan Xiong
Bin Gu 0001

Spiking Neural Networks (SNNs) are attracting great attention for their energy-efficient and fast-inference properties in neuromorphic computing. However, the efficient training of deep SNNs poses challenges in gradient calculation due to the non-differentiability of their binary spike-generating activation functions. The widely used surrogate gradient (SG) method, combined with the back-propagation through time (BPTT), has shown considerable effectiveness. Yet, BPTT’s process of unfolding and back-propagating along the computation graph requires storing intermediate information at all time-steps, resulting in huge memory consumption and failing to meet online requirements. In this work, we propose Neuronal Dynamics-based Online Training (NDOT) for SNNs, which uses the neuronal dynamics-based temporal dependency/sensitivity in gradient computation. NDOT enables forward-in-time learning by decomposing the full gradient into temporal and spatial gradients. To illustrate the intuition behind NDOT, we employ the Follow-the-Regularized-Leader (FTRL) algorithm. FTRL explicitly utilizes historical information and addresses limitations in instantaneous loss. Our proposed NDOT method accurately captures temporal dependencies through neuronal dynamics, functioning similarly to FTRL’s explicit utilizing historical information. Experiments on CIFAR-10, CIFAR-100, and CIFAR10-DVS demonstrate the superior performance of our NDOT method on large-scale static and neuromorphic datasets within a small number of time steps. The codes are available at https: //github. com/HaiyanJiang/SNN-NDOT.

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ICLR Conference 2024 Conference Paper

TAB: Temporal Accumulated Batch Normalization in Spiking Neural Networks

Haiyan Jiang
Vincent Zoonekynd
Giulia De Masi
Bin Gu 0001
Huan Xiong

Spiking Neural Networks (SNNs) are attracting growing interest for their energy-efficient computing when implemented on neuromorphic hardware. However, directly training SNNs, even adopting batch normalization (BN), is highly challenging due to their non-differentiable activation function and the temporally delayed accumulation of outputs over time. For SNN training, this temporal accumulation gives rise to Temporal Covariate Shifts (TCS) along the temporal dimension, a phenomenon that would become increasingly pronounced with layer-wise computations across multiple layers and multiple time-steps. In this paper, we introduce TAB (Temporal Accumulated Batch Normalization), a novel SNN batch normalization method that addresses the temporal covariate shift issue by aligning with neuron dynamics (specifically the accumulated membrane potential) and utilizing temporal accumulated statistics for data normalization. Within its framework, TAB effectively encapsulates the historical temporal dependencies that underlie the membrane potential accumulation process, thereby establishing a natural connection between neuron dynamics and TAB batch normalization. Experimental results on CIFAR-10, CIFAR-100, and DVS-CIFAR10 show that our TAB method outperforms other state-of-the-art methods.

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ICML Conference 2023 Conference Paper

A Unified Optimization Framework of ANN-SNN Conversion: Towards Optimal Mapping from Activation Values to Firing Rates

Haiyan Jiang
Srinivas Anumasa
Giulia De Masi
Huan Xiong
Bin Gu 0001

Spiking Neural Networks (SNNs) have gained significant attention for their energy-efficient and fast-inference capabilities, but training SNNs from scratch can be challenging due to the discrete nature of spikes. One alternative method is to convert an Artificial Neural Network (ANN) into an SNN, known as ANN-SNN conversion. Currently, existing ANN-SNN conversion methods often involve redesigning the ANN with a new activation function, rather than utilizing the traditional ReLU, and converting it to an SNN. However, these methods do not take into account the potential performance loss between the regular ANN with ReLU and the tailored ANN. In this work, we propose a unified optimization framework for ANN-SNN conversion that considers both performance loss and conversion error. To achieve this, we introduce the SlipReLU activation function, which is a weighted sum of the threshold-ReLU and the step function. Theoretical analysis demonstrates that conversion error can be zero on a range of shift values $\delta \in [-0. 5, 0. 5]$ rather than a fixed shift term 0. 5. We evaluate our SlipReLU method on CIFAR datasets, which shows that SlipReLU outperforms current ANN-SNN conversion methods and supervised training methods in terms of accuracy and latency. To the best of our knowledge, this is the first ANN-SNN conversion method that enables SNN inference using only 1 time step. Code is available at https: //github. com/HaiyanJiang/SNN_Conversion_unified.

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AIJ Journal 2023 Journal Article

G -LIME: Statistical learning for local interpretations of deep neural networks using global priors

Xuhong Li
Haoyi Xiong
Xingjian Li
Xiao Zhang
Ji Liu
Haiyan Jiang
Zeyu Chen
Dejing Dou

To explain the prediction result of a Deep Neural Network (DNN) model based on a given sample, LIME [1] and its derivatives have been proposed to approximate the local behavior of the DNN model around the data point via linear surrogates. Though these algorithms interpret the DNN by finding the key features used for classification, the random interpolations used by LIME would perturb the explanation result and cause the instability and inconsistency between repetitions of LIME computations. To tackle this issue, we propose G -LIME that extends the vanilla LIME through high-dimensional Bayesian linear regression using the sparsity and informative global priors. Specifically, with a dataset representing the population of samples (e. g. , the training set), G -LIME first pursues the global explanation of the DNN model using the whole dataset. Then, with a new data point, G -LIME incorporates an modified estimator of ElasticNet-alike to refine the local explanation result through balancing the distance to the global explanation and the sparsity/feature selection in the explanation. Finally, G -LIME uses Least Angle Regression (LARS) and retrieves the solution path of a modified ElasticNet under varying ℓ 1 -regularization, to screen and rank the importance of features [2] as the explanation result. Through extensive experiments on real world tasks, we show that the proposed method yields more stable, consistent, and accurate results compared to LIME.

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NeurIPS Conference 2022 Conference Paper

AutoMS: Automatic Model Selection for Novelty Detection with Error Rate Control

Yifan Zhang
Haiyan Jiang
Haojie Ren
Changliang Zou
Dejing Dou

Given an unsupervised novelty detection task on a new dataset, how can we automatically select a ''best'' detection model while simultaneously controlling the error rate of the best model? For novelty detection analysis, numerous detectors have been proposed to detect outliers on a new unseen dataset based on a score function trained on available clean data. However, due to the absence of labeled data for model evaluation and comparison, there is a lack of systematic approaches that are able to select a ''best'' model/detector (i. e. , the algorithm as well as its hyperparameters) and achieve certain error rate control simultaneously. In this paper, we introduce a unified data-driven procedure to address this issue. The key idea is to maximize the number of detected outliers while controlling the false discovery rate (FDR) with the help of Jackknife prediction. We establish non-asymptotic bounds for the false discovery proportions and show that the proposed procedure yields valid FDR control under some mild conditions. Numerical experiments on both synthetic and real data validate the theoretical results and demonstrate the effectiveness of our proposed AutoMS method. The code is available at https: //github. com/ZhangYifan1996/AutoMS.

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