pwc-archive/papers-with-abstracts huggingface dataset:
TabVFL: Improving Latent Representation in Vertical Federated Learning
Authors: Mohamed Rashad, Zilong Zhao, Jeremie Decouchant, Lydia Y. Chen
Date: 2024-04-27 00:00:00
Tasks: Federated Learning, Representation Learning, Vertical Federated Learning

Autoencoders are popular neural networks that are able to compress high dimensional data to extract relevant latent information. TabNet is a state-of-the-art neural network model designed for tabular data that utilizes an autoencoder architecture for training. Vertical Federated Learning (VFL) is an emerging distributed machine learning paradigm that allows multiple parties to train a model collaboratively on vertically partitioned data while maintaining data privacy. The existing design of training autoencoders in VFL is to train a separate autoencoder in each participant and aggregate the latent representation later. This design could potentially break important correlations between feature data of participating parties, as each autoencoder is trained on locally available features while disregarding the features of others. In addition, traditional autoencoders are not specifically designed for tabular data, which is ubiquitous in VFL settings. Moreover, the impact of client failures during training on the model robustness is under-researched in the VFL scene. In this paper, we propose TabVFL, a distributed framework designed to improve latent representation learning using the joint features of participants. The framework (i) preserves privacy by mitigating potential data leakage with the addition of a fully-connected layer, (ii) conserves feature correlations by learning one latent representation vector, and (iii) provides enhanced robustness against client failures during training phase. Extensive experiments on five classification datasets show that TabVFL can outperform the prior work design, with 26.12% of improvement on f1-score.
Usefulness of Emotional Prosody in Neural Machine Translation
Authors: Charles Brazier, Jean-Luc Rouas
Date: 2024-04-27 00:00:00
Tasks: Emotion Recognition, Machine Translation, NMT, Speech Emotion Recognition, Translation

Neural Machine Translation (NMT) is the task of translating a text from one language to another with the use of a trained neural network. Several existing works aim at incorporating external information into NMT models to improve or control predicted translations (e.g. sentiment, politeness, gender). In this work, we propose to improve translation quality by adding another external source of information: the automatically recognized emotion in the voice. This work is motivated by the assumption that each emotion is associated with a specific lexicon that can overlap between emotions. Our proposed method follows a two-stage procedure. At first, we select a state-of-the-art Speech Emotion Recognition (SER) model to predict dimensional emotion values from all input audio in the dataset. Then, we use these predicted emotions as source tokens added at the beginning of input texts to train our NMT model. We show that integrating emotion information, especially arousal, into NMT systems leads to better translations.
PhishGuard: A Convolutional Neural Network Based Model for Detecting Phishing URLs with Explainability Analysis
Authors: Md Robiul Islam, Md Mahamodul Islam, Mst. Suraiya Afrin, Anika Antara, Nujhat Tabassum, Al Amin
Date: 2024-04-27 00:00:00

Cybersecurity is one of the global issues because of the extensive dependence on cyber systems of individuals, industries, and organizations. Among the cyber attacks, phishing is increasing tremendously and affecting the global economy. Therefore, this phenomenon highlights the vital need for enhancing user awareness and robust support at both individual and organizational levels. Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. However, these approaches often need more convincing accuracy and rely on datasets consisting of limited samples. Furthermore, these black box intelligent models decision to detect suspicious URLs needs proper explanation to understand the features affecting the output. To address the issues, we propose a 1D Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data. The proposed model outperforms existing works by attaining an accuracy of 99.85%. Additionally, our explainability analysis highlights certain features that significantly contribute to identifying the phishing URL.
Cauchy-Schwarz Divergence Information Bottleneck for Regression
Authors: Shujian Yu, Xi Yu, Sigurd Løkse, Robert Jenssen, Jose C. Principe
Date: 2024-04-27 00:00:00
Tasks: Adversarial Robustness, Information Plane, regression, Variational Inference

The information bottleneck (IB) approach is popular to improve the generalization, robustness and explainability of deep neural networks. Essentially, it aims to find a minimum sufficient representation $\mathbf{t}$ by striking a trade-off between a compression term $I(\mathbf{x};\mathbf{t})$ and a prediction term $I(y;\mathbf{t})$, where $I(\cdot;\cdot)$ refers to the mutual information (MI). MI is for the IB for the most part expressed in terms of the Kullback-Leibler (KL) divergence, which in the regression case corresponds to prediction based on mean squared error (MSE) loss with Gaussian assumption and compression approximated by variational inference. In this paper, we study the IB principle for the regression problem and develop a new way to parameterize the IB with deep neural networks by exploiting favorable properties of the Cauchy-Schwarz (CS) divergence. By doing so, we move away from MSE-based regression and ease estimation by avoiding variational approximations or distributional assumptions. We investigate the improved generalization ability of our proposed CS-IB and demonstrate strong adversarial robustness guarantees. We demonstrate its superior performance on six real-world regression tasks over other popular deep IB approaches. We additionally observe that the solutions discovered by CS-IB always achieve the best trade-off between prediction accuracy and compression ratio in the information plane. The code is available at \url{https://github.com/SJYuCNEL/Cauchy-Schwarz-Information-Bottleneck}.
CBMAP: Clustering-based manifold approximation and projection for dimensionality reduction
Authors: Berat Dogan
Date: 2024-04-27 00:00:00
Tasks: Clustering, Data Visualization, Dimensionality Reduction, feature selection

Dimensionality reduction methods are employed to decrease data dimensionality, either to enhance machine learning performance or to facilitate data visualization in two or three-dimensional spaces. These methods typically fall into two categories: feature selection and feature transformation. Feature selection retains significant features, while feature transformation projects data into a lower-dimensional space, with linear and nonlinear methods. While nonlinear methods excel in preserving local structures and capturing nonlinear relationships, they may struggle with interpreting global structures and can be computationally intensive. Recent algorithms, such as the t-SNE, UMAP, TriMap, and PaCMAP prioritize preserving local structures, often at the expense of accurately representing global structures, leading to clusters being spread out more in lower-dimensional spaces. Moreover, these methods heavily rely on hyperparameters, making their results sensitive to parameter settings. To address these limitations, this study introduces a clustering-based approach, namely CBMAP (Clustering-Based Manifold Approximation and Projection), for dimensionality reduction. CBMAP aims to preserve both global and local structures, ensuring that clusters in lower-dimensional spaces closely resemble those in high-dimensional spaces. Experimental evaluations on benchmark datasets demonstrate CBMAP's efficacy, offering speed, scalability, and minimal reliance on hyperparameters. Importantly, CBMAP enables low-dimensional projection of test data, addressing a critical need in machine learning applications. CBMAP is made freely available at https://github.com/doganlab/cbmap and can be installed from the Python Package Directory (PyPI) software repository with the command pip install cbmap.
Accurate and fast anomaly detection in industrial processes and IoT environments
Authors: Simone Tonini, Andrea Vandin, Francesca Chiaromonte, Daniele Licari, Fernando Barsacchi
Date: 2024-04-27 00:00:00
Tasks: Anomaly Detection, Feature Importance, Sand

We present a novel, simple and widely applicable semi-supervised procedure for anomaly detection in industrial and IoT environments, SAnD (Simple Anomaly Detection). SAnD comprises 5 steps, each leveraging well-known statistical tools, namely; smoothing filters, variance inflation factors, the Mahalanobis distance, threshold selection algorithms and feature importance techniques. To our knowledge, SAnD is the first procedure that integrates these tools to identify anomalies and help decipher their putative causes. We show how each step contributes to tackling technical challenges that practitioners face when detecting anomalies in industrial contexts, where signals can be highly multicollinear, have unknown distributions, and intertwine short-lived noise with the long(er)-lived actual anomalies. The development of SAnD was motivated by a concrete case study from our industrial partner, which we use here to show its effectiveness. We also evaluate the performance of SAnD by comparing it with a selection of semi-supervised methods on public datasets from the literature on anomaly detection. We conclude that SAnD is effective, broadly applicable, and outperforms existing approaches in both anomaly detection and runtime.
Generalization capabilities and robustness of hybrid models grounded in physics compared to purely deep learning models
Authors: Rodrigo Abadía-Heredia, Adrián Corrochano, Manuel Lopez-Martin, Soledad Le Clainche
Date: 2024-04-27 00:00:00
Tasks: Deep Learning, Hybrid Machine Learning, Time Series Forecasting

This study investigates the generalization capabilities and robustness of purely deep learning (DL) models and hybrid models based on physical principles in fluid dynamics applications, specifically focusing on iteratively forecasting the temporal evolution of flow dynamics. Three autoregressive models were compared: a hybrid model (POD-DL) that combines proper orthogonal decomposition (POD) with a long-short term memory (LSTM) layer, a convolutional autoencoder combined with a convolutional LSTM (ConvLSTM) layer and a variational autoencoder (VAE) combined with a ConvLSTM layer. These models were tested on two high-dimensional, nonlinear datasets representing the velocity field of flow past a circular cylinder in both laminar and turbulent regimes. The study used latent dimension methods, enabling a bijective reduction of high-dimensional dynamics into a lower-order space to facilitate future predictions. While the VAE and ConvLSTM models accurately predicted laminar flow, the hybrid POD-DL model outperformed the others across both laminar and turbulent flow regimes. This success is attributed to the model's ability to incorporate modal decomposition, reducing the dimensionality of the data, by a non-parametric method, and simplifying the forecasting component. By leveraging POD, the model not only gained insight into the underlying physics, improving prediction accuracy with less training data, but also reduce the number of trainable parameters as POD is non-parametric. The findings emphasize the potential of hybrid models, particularly those integrating modal decomposition and deep learning, in predicting complex flow dynamics.
Noisy Node Classification by Bi-level Optimization based Multi-teacher Distillation
Authors: Yujing Liu, Zongqian Wu, Zhengyu Lu, Ci Nie, Guoqiu Wen, Ping Hu, Xiaofeng Zhu
Date: 2024-04-27 00:00:00
Tasks: Node Classification, Representation Learning, Self-Supervised Learning

Previous graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, we propose a new multi-teacher distillation method based on bi-level optimization (namely BO-NNC), to conduct noisy node classification on the graph data. Specifically, we first employ multiple self-supervised learning methods to train diverse teacher models, and then aggregate their predictions through a teacher weight matrix. Furthermore, we design a new bi-level optimization strategy to dynamically adjust the teacher weight matrix based on the training progress of the student model. Finally, we design a label improvement module to improve the label quality. Extensive experimental results on real datasets show that our method achieves the best results compared to state-of-the-art methods.
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs
Authors: Youngjoon Hong, Seungchan Ko, Jaeyong Lee
Date: 2024-04-27 00:00:00
Tasks: Operator learning

In this paper, we provide a theoretical analysis of a type of operator learning method without data reliance based on the classical finite element approximation, which is called the finite element operator network (FEONet). We first establish the convergence of this method for general second-order linear elliptic PDEs with respect to the parameters for neural network approximation. In this regard, we address the role of the condition number of the finite element matrix in the convergence of the method. Secondly, we derive an explicit error estimate for the self-adjoint case. For this, we investigate some regularity properties of the solution in certain function classes for a neural network approximation, verifying the sufficient condition for the solution to have the desired regularity. Finally, we will also conduct some numerical experiments that support the theoretical findings, confirming the role of the condition number of the finite element matrix in the overall convergence.
Uncertainty quantification for iterative algorithms in linear models with application to early stopping
Authors: Pierre C. Bellec, Kai Tan
Date: 2024-04-27 00:00:00
Tasks: Uncertainty Quantification, valid

This paper investigates the iterates $\hbb^1,\dots,\hbb^T$ obtained from iterative algorithms in high-dimensional linear regression problems, in the regime where the feature dimension $p$ is comparable with the sample size $n$, i.e., $p \asymp n$. The analysis and proposed estimators are applicable to Gradient Descent (GD), proximal GD and their accelerated variants such as Fast Iterative Soft-Thresholding (FISTA). The paper proposes novel estimators for the generalization error of the iterate $\hbb^t$ for any fixed iteration $t$ along the trajectory. These estimators are proved to be $\sqrt n$-consistent under Gaussian designs. Applications to early-stopping are provided: when the generalization error of the iterates is a U-shape function of the iteration $t$, the estimates allow to select from the data an iteration $\hat t$ that achieves the smallest generalization error along the trajectory. Additionally, we provide a technique for developing debiasing corrections and valid confidence intervals for the components of the true coefficient vector from the iterate $\hbb^t$ at any finite iteration $t$. Extensive simulations on synthetic data illustrate the theoretical results.
pFedAFM: Adaptive Feature Mixture for Batch-Level Personalization in Heterogeneous Federated Learning
Authors: Liping Yi, Han Yu, Chao Ren, Heng Zhang, Gang Wang, Xiaoguang Liu, Xiaoxiao Li
Date: 2024-04-27 00:00:00
Tasks: Federated Learning, Personalized Federated Learning, Prediction

Model-heterogeneous personalized federated learning (MHPFL) enables FL clients to train structurally different personalized models on non-independent and identically distributed (non-IID) local data. Existing MHPFL methods focus on achieving client-level personalization, but cannot address batch-level data heterogeneity. To bridge this important gap, we propose a model-heterogeneous personalized Federated learning approach with Adaptive Feature Mixture (pFedAFM) for supervised learning tasks. It consists of three novel designs: 1) A sharing global homogeneous small feature extractor is assigned alongside each client's local heterogeneous model (consisting of a heterogeneous feature extractor and a prediction header) to facilitate cross-client knowledge fusion. The two feature extractors share the local heterogeneous model's prediction header containing rich personalized prediction knowledge to retain personalized prediction capabilities. 2) An iterative training strategy is designed to alternately train the global homogeneous small feature extractor and the local heterogeneous large model for effective global-local knowledge exchange. 3) A trainable weight vector is designed to dynamically mix the features extracted by both feature extractors to adapt to batch-level data heterogeneity. Theoretical analysis proves that pFedAFM can converge over time. Extensive experiments on 2 benchmark datasets demonstrate that it significantly outperforms 7 state-of-the-art MHPFL methods, achieving up to 7.93% accuracy improvement while incurring low communication and computation costs.
T-CLAP: Temporal-Enhanced Contrastive Language-Audio Pretraining
Authors: Yi Yuan, Zhuo Chen, Xubo Liu, Haohe Liu, Xuenan Xu, Dongya Jia, Yuanzhe Chen, Mark D. Plumbley, Wenwu Wang
Date: 2024-04-27 00:00:00
Tasks: Retrieval

Contrastive language-audio pretraining~(CLAP) has been developed to align the representations of audio and language, achieving remarkable performance in retrieval and classification tasks. However, current CLAP struggles to capture temporal information within audio and text features, presenting substantial limitations for tasks such as audio retrieval and generation. To address this gap, we introduce T-CLAP, a temporal-enhanced CLAP model. We use Large Language Models~(LLMs) and mixed-up strategies to generate temporal-contrastive captions for audio clips from extensive audio-text datasets. Subsequently, a new temporal-focused contrastive loss is designed to fine-tune the CLAP model by incorporating these synthetic data. We conduct comprehensive experiments and analysis in multiple downstream tasks. T-CLAP shows improved capability in capturing the temporal relationship of sound events and outperforms state-of-the-art models by a significant margin.
Dynamical Mode Recognition of Coupled Flame Oscillators by Supervised and Unsupervised Learning Approaches
Authors: Weiming Xu, Tao Yang, Peng Zhang
Date: 2024-04-27 00:00:00
Tasks: Dimensionality Reduction, Dynamic Time Warping

Combustion instability in gas turbines and rocket engines, as one of the most challenging problems in combustion research, arises from the complex interactions among flames, which are also influenced by chemical reactions, heat and mass transfer, and acoustics. Identifying and understanding combustion instability is essential to ensure the safe and reliable operation of many combustion systems, where exploring and classifying the dynamical behaviors of complex flame systems is a core take. To facilitate fundamental studies, the present work concerns dynamical mode recognition of coupled flame oscillators made of flickering buoyant diffusion flames, which have gained increasing attention in recent years but are not sufficiently understood. The time series data of flame oscillators are generated by fully validated reacting flow simulations. Due to limitations of expertise-based models, a data-driven approach is adopted. In this study, a nonlinear dimensional reduction model of variational autoencoder (VAE) is used to project the simulation data onto a 2-dimensional latent space. Based on the phase trajectories in latent space, both supervised and unsupervised classifiers are proposed for datasets with well known labeling and without, respectively. For labeled datasets, we establish the Wasserstein-distance-based classifier (WDC) for mode recognition; for unlabeled datasets, we develop a novel unsupervised classifier (GMM-DTWC) combining dynamic time warping (DTW) and Gaussian mixture model (GMM). Through comparing with conventional approaches for dimensionality reduction and classification, the proposed supervised and unsupervised VAE-based approaches exhibit a prominent performance for distinguishing dynamical modes, implying their potential extension to dynamical mode recognition of complex combustion problems.
BiLO: Bilevel Local Operator Learning for PDE inverse problems
Authors: Ray Zirui Zhang, Xiaohui Xie, John S. Lowengrub
Date: 2024-04-27 00:00:00
Tasks: Bilevel Optimization, Operator learning

We propose a new neural network based method for solving inverse problems for partial differential equations (PDEs) by formulating the PDE inverse problem as a bilevel optimization problem. At the upper level, we minimize the data loss with respect to the PDE parameters. At the lower level, we train a neural network to locally approximate the PDE solution operator in the neighborhood of a given set of PDE parameters, which enables an accurate approximation of the descent direction for the upper level optimization problem. The lower level loss function includes the L2 norms of both the residual and its derivative with respect to the PDE parameters. We apply gradient descent simultaneously on both the upper and lower level optimization problems, leading to an effective and fast algorithm. The method, which we refer to as BiLO (Bilevel Local Operator learning), is also able to efficiently infer unknown functions in the PDEs through the introduction of an auxiliary variable. Through extensive experiments over multiple PDE systems, we demonstrate that our method enforces strong PDE constraints, is robust to sparse and noisy data, and eliminates the need to balance the residual and the data loss, which is inherent to the soft PDE constraints in many existing methods.
Generative Diffusion-based Downscaling for Climate
Authors: Robbie A. Watt, Laura A. Mansfield
Date: 2024-04-27 00:00:00
Tasks: Super-Resolution

Downscaling, or super-resolution, provides decision-makers with detailed, high-resolution information about the potential risks and impacts of climate change, based on climate model output. Machine learning algorithms are proving themselves to be efficient and accurate approaches to downscaling. Here, we show how a generative, diffusion-based approach to downscaling gives accurate downscaled results. We focus on an idealised setting where we recover ERA5 at $0.25\degree$~resolution from coarse grained version at $2\degree$~resolution. The diffusion-based method provides superior accuracy compared to a standard U-Net, particularly at the fine scales, as highlighted by a spectral decomposition. Additionally, the generative approach provides users with a probability distribution which can be used for risk assessment. This research highlights the potential of diffusion-based downscaling techniques in providing reliable and detailed climate predictions.