pwc-archive/papers-with-abstracts huggingface dataset:
MixGCN: Scalable GCN Training by Mixture of Parallelism and Mixture of Accelerators
Authors: Cheng Wan, Runkai Tao, Zheng Du, Yang Katie Zhao, Yingyan Celine Lin
Date: 2025-01-03 00:00:00

Graph convolutional networks (GCNs) have demonstrated superiority in graph-based learning tasks. However, training GCNs on full graphs is particularly challenging, due to the following two challenges: (1) the associated feature tensors can easily explode the memory and block the communication bandwidth of modern accelerators, and (2) the computation workflow in training GCNs alternates between sparse and dense matrix operations, complicating the efficient utilization of computational resources. Existing solutions for scalable distributed full-graph GCN training mostly adopt partition parallelism, which is unsatisfactory as they only partially address the first challenge while incurring scaled-out communication volume. To this end, we propose MixGCN aiming to simultaneously address both the aforementioned challenges towards GCN training. To tackle the first challenge, MixGCN integrates mixture of parallelism. Both theoretical and empirical analysis verify its constant communication volumes and enhanced balanced workload; For handling the second challenge, we consider mixture of accelerators (i.e., sparse and dense accelerators) with a dedicated accelerator for GCN training and a fine-grain pipeline. Extensive experiments show that MixGCN achieves boosted training efficiency and scalability.
Semialgebraic Neural Networks: From roots to representations
Authors: S. David Mis, Matti Lassas, Maarten V. de Hoop
Date: 2025-01-02 00:00:00

Many numerical algorithms in scientific computing -- particularly in areas like numerical linear algebra, PDE simulation, and inverse problems -- produce outputs that can be represented by semialgebraic functions; that is, the graph of the computed function can be described by finitely many polynomial equalities and inequalities. In this work, we introduce Semialgebraic Neural Networks (SANNs), a neural network architecture capable of representing any bounded semialgebraic function, and computing such functions up to the accuracy of a numerical ODE solver chosen by the programmer. Conceptually, we encode the graph of the learned function as the kernel of a piecewise polynomial selected from a class of functions whose roots can be evaluated using a particular homotopy continuation method. We show by construction that the SANN architecture is able to execute this continuation method, thus evaluating the learned semialgebraic function. Furthermore, the architecture can exactly represent even discontinuous semialgebraic functions by executing a continuation method on each connected component of the target function. Lastly, we provide example applications of these networks and show they can be trained with traditional deep-learning techniques.
Subject Specific Deep Learning Model for Motor Imagery Direction Decoding
Authors: Praveen K. Parashiva, Sagila Gangadaran, A. P. Vinod
Date: 2025-01-03 00:00:00
Tasks: Deep Learning, Motor Imagery

Hemispheric strokes impair motor control in contralateral body parts, necessitating effective rehabilitation strategies. Motor Imagery-based Brain-Computer Interfaces (MI-BCIs) promote neuroplasticity, aiding the recovery of motor functions. While deep learning has shown promise in decoding MI actions for stroke rehabilitation, existing studies largely focus on bilateral MI actions and are limited to offline evaluations. Decoding directional information from unilateral MI, however, offers a more natural control interface with greater degrees of freedom but remains challenging due to spatially overlapping neural activity. This work proposes a novel deep learning framework for online decoding of binary directional MI signals from the dominant hand of 20 healthy subjects. The proposed method employs EEGNet-based convolutional filters to extract temporal and spatial features. The EEGNet model is enhanced by Squeeze-and-Excitation (SE) layers that rank the electrode importance and feature maps. A subject-independent model is initially trained using calibration data from multiple subjects and fine-tuned for subject-specific adaptation. The performance of the proposed method is evaluated using subject-specific online session data. The proposed method achieved an average right vs left binary direction decoding accuracy of 58.7 +\- 8% for unilateral MI tasks, outperforming the existing deep learning models. Additionally, the SE-layer ranking offers insights into electrode contribution, enabling potential subject-specific BCI optimization. The findings highlight the efficacy of the proposed method in advancing MI-BCI applications for a more natural and effective control of BCI systems.
Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries
Authors: Ali Rabeh, Ethan Herron, Aditya Balu, Soumik Sarkar, Chinmay Hegde, Adarsh Krishnamurthy, Baskar Ganapathysubramanian
Date: 2024-12-31 00:00:00
Tasks: Benchmarking, Out-of-Distribution Generalization

Rapid and accurate simulations of fluid dynamics around complicated geometric bodies are critical in a variety of engineering and scientific applications, including aerodynamics and biomedical flows. However, while scientific machine learning (SciML) has shown considerable promise, most studies in this field are limited to simple geometries, and complex, real-world scenarios are underexplored. This paper addresses this gap by benchmarking diverse SciML models, including neural operators and vision transformer-based foundation models, for fluid flow prediction over intricate geometries. Using a high-fidelity dataset of steady-state flows across various geometries, we evaluate the impact of geometric representations -- Signed Distance Fields (SDF) and binary masks -- on model accuracy, scalability, and generalization. Central to this effort is the introduction of a novel, unified scoring framework that integrates metrics for global accuracy, boundary layer fidelity, and physical consistency to enable a robust, comparative evaluation of model performance. Our findings demonstrate that newer foundation models significantly outperform neural operators, particularly in data-limited scenarios, and that SDF representations yield superior results with sufficient training data. Despite these promises, all models struggle with out-of-distribution generalization, highlighting a critical challenge for future SciML applications. By advancing both evaluation models and modeling capabilities, our work paves the way for robust and scalable ML solutions for fluid dynamics across complex geometries.
Explainable Brain Age Gap Prediction in Neurodegenerative Conditions using coVariance Neural Networks
Authors: Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro
Date: 2025-01-02 00:00:00

Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms. Increasing \textit{brain age gap} characterized by an elevated brain age relative to the chronological age can reflect increased vulnerability to neurodegeneration and cognitive decline. Hence, brain age gap is a promising biomarker for monitoring brain health. However, black-box machine learning approaches to brain age gap prediction have limited practical utility. Recent studies on coVariance neural networks (VNN) have proposed a relatively transparent deep learning pipeline for neuroimaging data analyses, which possesses two key features: (i) inherent \textit{anatomically interpretablity} of derived biomarkers; and (ii) a methodologically interpretable perspective based on \textit{linkage with eigenvectors of anatomic covariance matrix}. In this paper, we apply the VNN-based approach to study brain age gap using cortical thickness features for various prevalent neurodegenerative conditions. Our results reveal distinct anatomic patterns for brain age gap in Alzheimer's disease, frontotemporal dementia, and atypical Parkinsonian disorders. Furthermore, we demonstrate that the distinct anatomic patterns of brain age gap are linked with the differences in how VNN leverages the eigenspectrum of the anatomic covariance matrix, thus lending explainability to the reported results.
TreeLUT: An Efficient Alternative to Deep Neural Networks for Inference Acceleration Using Gradient Boosted Decision Trees
Authors: Alireza Khataei, Kia Bazargan
Date: 2025-01-02 00:00:00
Tasks: Quantization

Accelerating machine learning inference has been an active research area in recent years. In this context, field-programmable gate arrays (FPGAs) have demonstrated compelling performance by providing massive parallelism in deep neural networks (DNNs). Neural networks (NNs) are computationally intensive during inference, as they require massive amounts of multiplication and addition, which makes their implementations costly. Numerous studies have recently addressed this challenge to some extent using a combination of sparsity induction, quantization, and transformation of neurons or sub-networks into lookup tables (LUTs) on FPGAs. Gradient boosted decision trees (GBDTs) are a high-accuracy alternative to DNNs in a wide range of regression and classification tasks, particularly for tabular datasets. The basic building block of GBDTs is a decision tree, which resembles the structure of binary decision diagrams. FPGA design flows are heavily optimized to implement such a structure efficiently. In addition to decision trees, GBDTs perform simple operations during inference, including comparison and addition. We present TreeLUT as an open-source tool for implementing GBDTs using an efficient quantization scheme, hardware architecture, and pipelining strategy. It primarily utilizes LUTs with no BRAMs or DSPs on FPGAs, resulting in high efficiency. We show the effectiveness of TreeLUT using multiple classification datasets, commonly used to evaluate ultra-low area and latency architectures. Using these benchmarks, we compare our implementation results with existing DNN and GBDT methods, such as DWN, PolyLUT-Add, NeuraLUT, LogicNets, FINN, hls4ml, and others. Our results show that TreeLUT significantly improves hardware utilization, latency, and throughput at competitive accuracy compared to previous works.
Transfer Neyman-Pearson Algorithm for Outlier Detection
Authors: Mohammadreza M. Kalan, Eitan J. Neugut, Samory Kpotufe
Date: 2025-01-02 00:00:00
Tasks: Classification, imbalanced classification, Outlier Detection, Transfer Learning

We consider the problem of transfer learning in outlier detection where target abnormal data is rare. While transfer learning has been considered extensively in traditional balanced classification, the problem of transfer in outlier detection and more generally in imbalanced classification settings has received less attention. We propose a general meta-algorithm which is shown theoretically to yield strong guarantees w.r.t. to a range of changes in abnormal distribution, and at the same time amenable to practical implementation. We then investigate different instantiations of this general meta-algorithm, e.g., based on multi-layer neural networks, and show empirically that they outperform natural extensions of transfer methods for traditional balanced classification settings (which are the only solutions available at the moment).
Many of Your DPOs are Secretly One: Attempting Unification Through Mutual Information
Authors: Rasul Tutnov, Antoine Grosnit, Haitham Bou-Ammar
Date: 2025-01-02 00:00:00
Tasks: Navigate

Post-alignment of large language models (LLMs) is critical in improving their utility, safety, and alignment with human intentions. Direct preference optimisation (DPO) has become one of the most widely used algorithms for achieving this alignment, given its ability to optimise models based on human feedback directly. However, the vast number of DPO variants in the literature has made it increasingly difficult for researchers to navigate and fully grasp the connections between these approaches. This paper introduces a unifying framework inspired by mutual information, which proposes a new loss function with flexible priors. By carefully specifying these priors, we demonstrate that many existing algorithms, such as SimPO, TDPO, SparsePO, and others, can be derived from our framework. This unification offers a clearer and more structured approach, allowing researchers to understand the relationships between different DPO variants better. We aim to simplify the landscape of DPO algorithms, making it easier for the research community to gain insights and foster further advancements in LLM alignment. Ultimately, we hope our framework can be a foundation for developing more robust and interpretable alignment techniques.
Predicting the Performance of Black-box LLMs through Self-Queries
Authors: Dylan Sam, Marc Finzi, J. Zico Kolter
Date: 2025-01-02 00:00:00
Tasks: Question Answering

As large language models (LLMs) are increasingly relied on in AI systems, predicting when they make mistakes is crucial. While a great deal of work in the field uses internal representations to interpret model behavior, these representations are inaccessible when given solely black-box access through an API. In this paper, we extract features of LLMs in a black-box manner by using follow-up prompts and taking the probabilities of different responses as representations to train reliable predictors of model behavior. We demonstrate that training a linear model on these low-dimensional representations produces reliable and generalizable predictors of model performance at the instance level (e.g., if a particular generation correctly answers a question). Remarkably, these can often outperform white-box linear predictors that operate over a model's hidden state or the full distribution over its vocabulary. In addition, we demonstrate that these extracted features can be used to evaluate more nuanced aspects of a language model's state. For instance, they can be used to distinguish between a clean version of GPT-4o-mini and a version that has been influenced via an adversarial system prompt that answers question-answering tasks incorrectly or introduces bugs into generated code. Furthermore, they can reliably distinguish between different model architectures and sizes, enabling the detection of misrepresented models provided through an API (e.g., identifying if GPT-3.5 is supplied instead of GPT-4o-mini).
Stackelberg Game Based Performance Optimization in Digital Twin Assisted Federated Learning over NOMA Networks
Authors: Bibo Wu, Fang Fang, Xianbin Wang
Date: 2025-01-03 00:00:00
Tasks: Federated Learning, Problem Decomposition

Despite the advantage of preserving data privacy, federated learning (FL) still suffers from the straggler issue due to the limited computing resources of distributed clients and the unreliable wireless communication environment. By effectively imitating the distributed resources, digital twin (DT) shows great potential in alleviating this issue. In this paper, we leverage DT in the FL framework over non-orthogonal multiple access (NOMA) network to assist FL training process, considering malicious attacks on model updates from clients. A reputationbased client selection scheme is proposed, which accounts for client heterogeneity in multiple aspects and effectively mitigates the risks of poisoning attacks in FL systems. To minimize the total latency and energy consumption in the proposed system, we then formulate a Stackelberg game by considering clients and the server as the leader and the follower, respectively. Specifically, the leader aims to minimize the energy consumption while the objective of the follower is to minimize the total latency during FL training. The Stackelberg equilibrium is achieved to obtain the optimal solutions. We first derive the strategies for the followerlevel problem and include them in the leader-level problem which is then solved via problem decomposition. Simulation results verify the superior performance of the proposed scheme.
Multivariate Time Series Anomaly Detection using DiffGAN Model
Authors: Guangqiang Wu, Fu Zhang
Date: 2025-01-03 00:00:00
Tasks: Anomaly Detection, Generative Adversarial Network, model, Time Series, Time Series Anomaly Detection

In recent years, some researchers have applied diffusion models to multivariate time series anomaly detection. The partial diffusion strategy, which depends on the diffusion steps, is commonly used for anomaly detection in these models. However, different diffusion steps have an impact on the reconstruction of the original data, thereby impacting the effectiveness of anomaly detection. To address this issue, we propose a novel method named DiffGAN, which adds a generative adversarial network component to the denoiser of diffusion model. This addition allows for the simultaneous generation of noisy data and prediction of diffusion steps. Compared to multiple state-of-the-art reconstruction models, experimental results demonstrate that DiffGAN achieves superior performance in anomaly detection.
Online Meta-Learning Channel Autoencoder for Dynamic End-to-end Physical Layer Optimization
Authors: Ali Owfi, Jonathan Ashdown, Kurt Turck
Date: 2025-01-03 00:00:00
Tasks: Few-Shot Learning, Meta-Learning

Channel Autoencoders (CAEs) have shown significant potential in optimizing the physical layer of a wireless communication system for a specific channel through joint end-to-end training. However, the practical implementation of CAEs faces several challenges, particularly in realistic and dynamic scenarios. Channels in communication systems are dynamic and change with time. Still, most proposed CAE designs assume stationary scenarios, meaning they are trained and tested for only one channel realization without regard for the dynamic nature of wireless communication systems. Moreover, conventional CAEs are designed based on the assumption of having access to a large number of pilot signals, which act as training samples in the context of CAEs. However, in real-world applications, it is not feasible for a CAE operating in real-time to acquire large amounts of training samples for each new channel realization. Hence, the CAE has to be deployable in few-shot learning scenarios where only limited training samples are available. Furthermore, most proposed conventional CAEs lack fast adaptability to new channel realizations, which becomes more pronounced when dealing with a limited number of pilots. To address these challenges, this paper proposes the Online Meta Learning channel AE (OML-CAE) framework for few-shot CAE scenarios with dynamic channels. The OML-CAE framework enhances adaptability to varying channel conditions in an online manner, allowing for dynamic adjustments in response to evolving communication scenarios. Moreover, it can adapt to new channel conditions using only a few pilots, drastically increasing pilot efficiency and making the CAE design feasible in realistic scenarios.
Adaptive Meta-learning-based Adversarial Training for Robust Automatic Modulation Classification
Authors: Amirmohammad Bamdad, Ali Owfi, Fatemeh Afghah
Date: 2025-01-03 00:00:00
Tasks: Adversarial Attack, Meta-Learning

DL-based automatic modulation classification (AMC) models are highly susceptible to adversarial attacks, where even minimal input perturbations can cause severe misclassifications. While adversarially training an AMC model based on an adversarial attack significantly increases its robustness against that attack, the AMC model will still be defenseless against other adversarial attacks. The theoretically infinite possibilities for adversarial perturbations mean that an AMC model will inevitably encounter new unseen adversarial attacks if it is ever to be deployed to a real-world communication system. Moreover, the computational limitations and challenges of obtaining new data in real-time will not allow a full training process for the AMC model to adapt to the new attack when it is online. To this end, we propose a meta-learning-based adversarial training framework for AMC models that substantially enhances robustness against unseen adversarial attacks and enables fast adaptation to these attacks using just a few new training samples, if any are available. Our results demonstrate that this training framework provides superior robustness and accuracy with much less online training time than conventional adversarial training of AMC models, making it highly efficient for real-world deployment.
Crossing Language Borders: A Pipeline for Indonesian Manhwa Translation
Authors: Nithyasri Narasimhan, Sagarika Singh
Date: 2025-01-03 00:00:00
Tasks: Machine Translation, Object Detection, Optical Character Recognition (OCR), Translation

In this project, we develop a practical and efficient solution for automating the Manhwa translation from Indonesian to English. Our approach combines computer vision, text recognition, and natural language processing techniques to streamline the traditionally manual process of Manhwa(Korean comics) translation. The pipeline includes fine-tuned YOLOv5xu for speech bubble detection, Tesseract for OCR and fine-tuned MarianMT for machine translation. By automating these steps, we aim to make Manhwa more accessible to a global audience while saving time and effort compared to manual translation methods. While most Manhwa translation efforts focus on Japanese-to-English, we focus on Indonesian-to-English translation to address the challenges of working with low-resource languages. Our model shows good results at each step and was able to translate from Indonesian to English efficiently.
A Probabilistic Model for Node Classification in Directed Graphs
Authors: Diego Huerta, Gerardo Arizmendi
Date: 2025-01-03 00:00:00
Tasks: Math, Node Classification

In this work, we present a probabilistic model for directed graphs where nodes have attributes and labels. This model serves as a generative classifier capable of predicting the labels of unseen nodes using either maximum likelihood or maximum a posteriori estimations. The predictions made by this model are highly interpretable, contrasting with some common methods for node classification, such as graph neural networks. We applied the model to two datasets, demonstrating predictive performance that is competitive with, and even superior to, state-of-the-art methods. One of the datasets considered is adapted from the Math Genealogy Project, which has not previously been utilized for this purpose. Consequently, we evaluated several classification algorithms on this dataset to compare the performance of our model and provide benchmarks for this new resource.
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning
Authors: Danni Peng, YuAn Wang, Huazhu Fu, Jinpeng Jiang, Yong liu, Rick Siow Mong Goh, Qingsong Wei
Date: 2025-01-03 00:00:00
Tasks: Federated Learning, Personalized Federated Learning

Personalized federated learning (PFL) studies effective model personalization to address the data heterogeneity issue among clients in traditional federated learning (FL). Existing PFL approaches mainly generate personalized models by relying solely on the clients' latest updated models while ignoring their previous updates, which may result in suboptimal personalized model learning. To bridge this gap, we propose a novel framework termed pFedSeq, designed for personalizing adapters to fine-tune a foundation model in FL. In pFedSeq, the server maintains and trains a sequential learner, which processes a sequence of past adapter updates from clients and generates calibrations for personalized adapters. To effectively capture the cross-client and cross-step relations hidden in previous updates and generate high-performing personalized adapters, pFedSeq adopts the powerful selective state space model (SSM) as the architecture of sequential learner. Through extensive experiments on four public benchmark datasets, we demonstrate the superiority of pFedSeq over state-of-the-art PFL methods.