pwc-archive/papers-with-abstracts huggingface dataset:
Discrete Randomized Smoothing Meets Quantum Computing
Authors: Tom Wollschläger, Aman Saxena, Nicola Franco, Jeanette Miriam Lorenz, Stephan Günnemann
Date: 2024-08-01 00:00:00
Tasks: Quantum Machine Learning

Breakthroughs in machine learning (ML) and advances in quantum computing (QC) drive the interdisciplinary field of quantum machine learning to new levels. However, due to the susceptibility of ML models to adversarial attacks, practical use raises safety-critical concerns. Existing Randomized Smoothing (RS) certification methods for classical machine learning models are computationally intensive. In this paper, we propose the combination of QC and the concept of discrete randomized smoothing to speed up the stochastic certification of ML models for discrete data. We show how to encode all the perturbations of the input binary data in superposition and use Quantum Amplitude Estimation (QAE) to obtain a quadratic reduction in the number of calls to the model that are required compared to traditional randomized smoothing techniques. In addition, we propose a new binary threat model to allow for an extensive evaluation of our approach on images, graphs, and text.
Distance-Preserving Spatial Representations in Genomic Data
Authors: Wenbin Zhou, Jin-Hong Du
Date: 2024-08-01 00:00:00
Tasks: Representation Learning, Transfer Learning, Variational Inference

The spatial context of single-cell gene expression data is crucial for many downstream analyses, yet often remains inaccessible due to practical and technical limitations, restricting the utility of such datasets. In this paper, we propose a generic representation learning and transfer learning framework dp-VAE, capable of reconstructing the spatial coordinates associated with the provided gene expression data. Central to our approach is a distance-preserving regularizer integrated into the loss function during training, ensuring the model effectively captures and utilizes spatial context signals from reference datasets. During the inference stage, the produced latent representation of the model can be used to reconstruct or impute the spatial context of the provided gene expression by solving a constrained optimization problem. We also explore the theoretical connections between distance-preserving loss, distortion, and the bi-Lipschitz condition within generative models. Finally, we demonstrate the effectiveness of dp-VAE in different tasks involving training robustness, out-of-sample evaluation, and transfer learning inference applications by testing it over 27 publicly available datasets. This underscores its applicability to a wide range of genomics studies that were previously hindered by the absence of spatial data.
Towards Certified Unlearning for Deep Neural Networks
Authors: Binchi Zhang, Yushun Dong, Tianhao Wang, Jundong Li
Date: 2024-08-01 00:00:00
Tasks: Machine Unlearning

In the field of machine unlearning, certified unlearning has been extensively studied in convex machine learning models due to its high efficiency and strong theoretical guarantees. However, its application to deep neural networks (DNNs), known for their highly nonconvex nature, still poses challenges. To bridge the gap between certified unlearning and DNNs, we propose several simple techniques to extend certified unlearning methods to nonconvex objectives. To reduce the time complexity, we develop an efficient computation method by inverse Hessian approximation without compromising certification guarantees. In addition, we extend our discussion of certification to nonconvergence training and sequential unlearning, considering that real-world users can send unlearning requests at different time points. Extensive experiments on three real-world datasets demonstrate the efficacy of our method and the advantages of certified unlearning in DNNs.
Automatic Pull Request Description Generation Using LLMs: A T5 Model Approach
Authors: Md Nazmus Sakib, Md Athikul Islam, Md Mashrur Arifin
Date: 2024-08-01 00:00:00
Tasks: Text Summarization

Developers create pull request (PR) descriptions to provide an overview of their changes and explain the motivations behind them. These descriptions help reviewers and fellow developers quickly understand the updates. Despite their importance, some developers omit these descriptions. To tackle this problem, we propose an automated method for generating PR descriptions based on commit messages and source code comments. This method frames the task as a text summarization problem, for which we utilized the T5 text-to-text transfer model. We fine-tuned a pre-trained T5 model using a dataset containing 33,466 PRs. The model's effectiveness was assessed using ROUGE metrics, which are recognized for their strong alignment with human evaluations. Our findings reveal that the T5 model significantly outperforms LexRank, which served as our baseline for comparison.
Verification of Machine Unlearning is Fragile
Authors: Binchi Zhang, Zihan Chen, Cong Shen, Jundong Li
Date: 2024-08-01 00:00:00
Tasks: Machine Unlearning

As privacy concerns escalate in the realm of machine learning, data owners now have the option to utilize machine unlearning to remove their data from machine learning models, following recent legislation. To enhance transparency in machine unlearning and avoid potential dishonesty by model providers, various verification strategies have been proposed. These strategies enable data owners to ascertain whether their target data has been effectively unlearned from the model. However, our understanding of the safety issues of machine unlearning verification remains nascent. In this paper, we explore the novel research question of whether model providers can circumvent verification strategies while retaining the information of data supposedly unlearned. Our investigation leads to a pessimistic answer: \textit{the verification of machine unlearning is fragile}. Specifically, we categorize the current verification strategies regarding potential dishonesty among model providers into two types. Subsequently, we introduce two novel adversarial unlearning processes capable of circumventing both types. We validate the efficacy of our methods through theoretical analysis and empirical experiments using real-world datasets. This study highlights the vulnerabilities and limitations in machine unlearning verification, paving the way for further research into the safety of machine unlearning.
Equivariant neural networks and piecewise linear representation theory
Authors: Joel Gibson, Daniel Tubbenhauer, Geordie Williamson
Date: 2024-08-01 00:00:00

Equivariant neural networks are neural networks with symmetry. Motivated by the theory of group representations, we decompose the layers of an equivariant neural network into simple representations. The nonlinear activation functions lead to interesting nonlinear equivariant maps between simple representations. For example, the rectified linear unit (ReLU) gives rise to piecewise linear maps. We show that these considerations lead to a filtration of equivariant neural networks, generalizing Fourier series. This observation might provide a useful tool for interpreting equivariant neural networks.
Reconstructing Richtmyer-Meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks
Authors: Daniel A. Serino, Marc L. Klasky, Balasubramanya T. Nadiga, Xiaojian Xu, Trevor Wilcox
Date: 2024-08-02 00:00:00

A trained attention-based transformer network can robustly recover the complex topologies given by the Richtmyer-Meshkoff instability from a sequence of hydrodynamic features derived from radiographic images corrupted with blur, scatter, and noise. This approach is demonstrated on ICF-like double shell hydrodynamic simulations. The key component of this network is a transformer encoder that acts on a sequence of features extracted from noisy radiographs. This encoder includes numerous self-attention layers that act to learn temporal dependencies in the input sequences and increase the expressiveness of the model. This approach is demonstrated to exhibit an excellent ability to accurately recover the Richtmyer-Meshkov instability growth rates, even despite the gas-metal interface being greatly obscured by radiographic noise.
Adaptive Two-Stage Cloud Resource Scaling via Hierarchical Multi-Indicator Forecasting and Bayesian Decision-Making
Authors: Yang Luo, Shiyu Wang, Zhemeng Yu, Wei Lu, Xiaofeng Gao, Lintao Ma, Guihai Chen
Date: 2024-08-02 00:00:00
Tasks: Cloud Computing, Decision Making, Decision Making Under Uncertainty, GPU

The surging demand for cloud computing resources, driven by the rapid growth of sophisticated large-scale models and data centers, underscores the critical importance of efficient and adaptive resource allocation. As major tech enterprises deploy massive infrastructures with thousands of GPUs, existing cloud platforms still struggle with low resource utilization due to key challenges: capturing hierarchical indicator structures, modeling non-Gaussian distributions, and decision-making under uncertainty. To address these challenges, we propose HRAMONY, an adaptive Hierarchical Attention-based Resource Modeling and Decision-Making System. HARMONY combines hierarchical multi-indicator distribution forecasting and uncertainty-aware Bayesian decision-making. It introduces a novel hierarchical attention mechanism that comprehensively models complex inter-indicator dependencies, enabling accurate predictions that can adapt to evolving environment states. By transforming Gaussian projections into adaptive non-Gaussian distributions via Normalizing Flows. Crucially, HARMONY leverages the full predictive distributions in an adaptive Bayesian process, proactively incorporating uncertainties to optimize resource allocation while robustly meeting SLA constraints under varying conditions. Extensive evaluations across four large-scale cloud datasets demonstrate HARMONY's state-of-the-art performance, significantly outperforming nine established methods. A month-long real-world deployment validated HARMONY's substantial practical impact, realizing over 35,000 GPU hours in savings and translating to $100K+ in cost reduction, showcasing its remarkable economic value through adaptive, uncertainty-aware scaling. Our code is available at https://github.com/Floating-LY/HARMONY1.
Enhanced Prediction of Ventilator-Associated Pneumonia in Patients with Traumatic Brain Injury Using Advanced Machine Learning Techniques
Authors: Negin Ashrafi, Armin Abdollahi, Maryam Pishgar
Date: 2024-08-02 00:00:00
Tasks: Feature Importance, feature selection, Specificity

Background: Ventilator-associated pneumonia (VAP) in traumatic brain injury (TBI) patients poses a significant mortality risk and imposes a considerable financial burden on patients and healthcare systems. Timely detection and prognostication of VAP in TBI patients are crucial to improve patient outcomes and alleviate the strain on healthcare resources. Methods: We implemented six machine learning models using the MIMIC-III database. Our methodology included preprocessing steps, such as feature selection with CatBoost and expert opinion, addressing class imbalance with the Synthetic Minority Oversampling Technique (SMOTE), and rigorous model tuning through 5-fold cross-validation to optimize hyperparameters. Key models evaluated included SVM, Logistic Regression, Random Forest, XGBoost, ANN, and AdaBoost. Additionally, we conducted SHAP analysis to determine feature importance and performed an ablation study to assess feature impacts on model performance. Results: XGBoost outperformed the baseline models and the best existing literature. We used metrics, including AUC, Accuracy, Specificity, Sensitivity, F1 Score, PPV, and NPV. XGBoost demonstrated the highest performance with an AUC of 0.940 and an Accuracy of 0.875, which are 23.4% and 23.5% higher than the best results in the existing literature, with an AUC of 0.706 and an Accuracy of 0.640, respectively. This enhanced performance underscores the models' effectiveness in clinical settings. Conclusions: This study enhances the predictive modeling of VAP in TBI patients, improving early detection and intervention potential. Refined feature selection and advanced ensemble techniques significantly boosted model accuracy and reliability, offering promising directions for future clinical applications and medical diagnostics research.
Domain Adaptation-Enhanced Searchlight: Enabling classification of brain states from visual perception to mental imagery
Authors: Alexander Olza, David Soto, Roberto Santana
Date: 2024-08-02 00:00:00
Tasks: Binary Classification, Brain Computer Interface, Brain Decoding, Domain Adaptation, domain classification, Prediction

In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction using primarily visual data from fMRI scans of 18 subjects. Initially, we train a baseline model on visual stimuli to predict imagined stimuli, utilizing data from 14 brain regions. We then develop several models to improve imagery prediction, comparing different DA methods. Our results demonstrate that DA significantly enhances imagery prediction in binary classification on our dataset, as well as in multiclass classification on a publicly available dataset. We then conduct a DA-enhanced searchlight analysis, followed by permutation-based statistical tests to identify brain regions where imagery decoding is consistently above chance across subjects. Our DA-enhanced searchlight predicts imagery contents in a highly distributed set of brain regions, including the visual cortex and the frontoparietal cortex, thereby outperforming standard cross-domain classification methods. The complete code and data for this paper have been made openly available for the use of the scientific community.
ZNorm: Z-Score Gradient Normalization Accelerating Skip-Connected Network Training without Architectural Modification
Authors: Juyoung Yun
Date: 2024-08-02 00:00:00

The rapid advancements in deep learning necessitate better training methods for deep neural networks (DNNs). As models grow in complexity, vanishing and exploding gradients impede performance, particularly in skip-connected architectures like Deep Residual Networks. We propose Z-Score Normalization for Gradient Descent (ZNorm), an innovative technique that adjusts only the gradients without modifying the network architecture to accelerate training and improve model performance. ZNorm normalizes the overall gradients, providing consistent gradient scaling across layers, effectively reducing the risks of vanishing and exploding gradients and achieving superior performance. Extensive experiments on CIFAR-10 and medical datasets confirm that ZNorm consistently outperforms existing methods under the same experimental settings. In medical imaging applications, ZNorm significantly enhances tumor prediction and segmentation accuracy, underscoring its practical utility. These findings highlight ZNorm's potential as a robust and versatile tool for enhancing the training and effectiveness of deep neural networks, especially in skip-connected architectures, across various applications.
Automated Classification of Dry Bean Varieties Using XGBoost and SVM Models
Authors: Ramtin Ardeshirifar
Date: 2024-08-02 00:00:00
Tasks: Classification, Dimensionality Reduction

This paper presents a comparative study on the automated classification of seven different varieties of dry beans using machine learning models. Leveraging a dataset of 12,909 dry bean samples, reduced from an initial 13,611 through outlier removal and feature extraction, we applied Principal Component Analysis (PCA) for dimensionality reduction and trained two multiclass classifiers: XGBoost and Support Vector Machine (SVM). The models were evaluated using nested cross-validation to ensure robust performance assessment and hyperparameter tuning. The XGBoost and SVM models achieved overall correct classification rates of 94.00% and 94.39%, respectively. The results underscore the efficacy of these machine learning approaches in agricultural applications, particularly in enhancing the uniformity and efficiency of seed classification. This study contributes to the growing body of work on precision agriculture, demonstrating that automated systems can significantly support seed quality control and crop yield optimization. Future work will explore incorporating more diverse datasets and advanced algorithms to further improve classification accuracy.
Certified Robust Invariant Polytope Training in Neural Controlled ODEs
Authors: Akash Harapanahalli, Samuel Coogan
Date: 2024-08-02 00:00:00

We consider a nonlinear control system modeled as an ordinary differential equation subject to disturbance, with a state feedback controller parameterized as a feedforward neural network. We propose a framework for training controllers with certified robust forward invariant polytopes, where any trajectory initialized inside the polytope remains within the polytope, regardless of the disturbance. First, we parameterize a family of lifted control systems in a higher dimensional space, where the original neural controlled system evolves on an invariant subspace of each lifted system. We use interval analysis and neural network verifiers to further construct a family of lifted embedding systems, carefully capturing the knowledge of this invariant subspace. If the vector field of any lifted embedding system satisfies a sign constraint at a single point, then a certain convex polytope of the original system is robustly forward invariant. Treating the neural network controller and the lifted system parameters as variables, we propose an algorithm to train controllers with certified forward invariant polytopes in the closed-loop control system. Through two examples, we demonstrate how the simplicity of the sign constraint allows our approach to scale with system dimension to over $50$ states, and outperform state-of-the-art Lyapunov-based sampling approaches in runtime.
Feature Clock: High-Dimensional Effects in Two-Dimensional Plots
Authors: Olga Ovcharenko, Rita Sevastjanova, Valentina Boeva
Date: 2024-08-02 00:00:00
Tasks: Dimensionality Reduction

Humans struggle to perceive and interpret high-dimensional data. Therefore, high-dimensional data are often projected into two dimensions for visualization. Many applications benefit from complex nonlinear dimensionality reduction techniques, but the effects of individual high-dimensional features are hard to explain in the two-dimensional space. Most visualization solutions use multiple two-dimensional plots, each showing the effect of one high-dimensional feature in two dimensions; this approach creates a need for a visual inspection of k plots for a k-dimensional input space. Our solution, Feature Clock, provides a novel approach that eliminates the need to inspect these k plots to grasp the influence of original features on the data structure depicted in two dimensions. Feature Clock enhances the explainability and compactness of visualizations of embedded data and is available in an open-source Python library.
Optimal Mixed Integer Linear Optimization Trained Multivariate Classification Trees
Authors: Brandon Alston, Illya V. Hicks
Date: 2024-08-02 00:00:00
Tasks: Binary Classification

Multivariate decision trees are powerful machine learning tools for classification and regression that attract many researchers and industry professionals. An optimal binary tree has two types of vertices, (i) branching vertices which have exactly two children and where datapoints are assessed on a set of discrete features and (ii) leaf vertices at which datapoints are given a prediction, and can be obtained by solving a biobjective optimization problem that seeks to (i) maximize the number of correctly classified datapoints and (ii) minimize the number of branching vertices. Branching vertices are linear combinations of training features and therefore can be thought of as hyperplanes. In this paper, we propose two cut-based mixed integer linear optimization (MILO) formulations for designing optimal binary classification trees (leaf vertices assign discrete classes). Our models leverage on-the-fly identification of minimal infeasible subsystems (MISs) from which we derive cutting planes that hold the form of packing constraints. We show theoretical improvements on the strongest flow-based MILO formulation currently in the literature and conduct experiments on publicly available datasets to show our models' ability to scale, strength against traditional branch and bound approaches, and robustness in out-of-sample test performance. Our code and data are available on GitHub.
Decentralized Smoothing ADMM for Quantile Regression with Non-Convex Sparse Penalties
Authors: Reza Mirzaeifard, Diyako Ghaderyan, Stefan Werner
Date: 2024-08-02 00:00:00
Tasks: quantile regression, regression

In the rapidly evolving internet-of-things (IoT) ecosystem, effective data analysis techniques are crucial for handling distributed data generated by sensors. Addressing the limitations of existing methods, such as the sub-gradient approach, which fails to distinguish between active and non-active coefficients effectively, this paper introduces the decentralized smoothing alternating direction method of multipliers (DSAD) for penalized quantile regression. Our method leverages non-convex sparse penalties like the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD), improving the identification and retention of significant predictors. DSAD incorporates a total variation norm within a smoothing ADMM framework, achieving consensus among distributed nodes and ensuring uniform model performance across disparate data sources. This approach overcomes traditional convergence challenges associated with non-convex penalties in decentralized settings. We present theoretical proofs and extensive simulation results to validate the effectiveness of the DSAD, demonstrating its superiority in achieving reliable convergence and enhancing estimation accuracy compared with prior methods.
UnifiedNN: Efficient Neural Network Training on the Cloud
Authors: Sifat Ut Taki, Arthi Padmanabhan, Spyridon Mastorakis
Date: 2024-08-02 00:00:00
Tasks: Efficient Neural Network

Nowadays, cloud-based services are widely favored over the traditional approach of locally training a Neural Network (NN) model. Oftentimes, a cloud service processes multiple requests from users--thus training multiple NN models concurrently. However, training NN models concurrently is a challenging process, which typically requires significant amounts of available computing resources and takes a long time to complete. In this paper, we present UnifiedNN to effectively train multiple NN models concurrently on the cloud. UnifiedNN effectively "combines" multiple NN models and features several memory and time conservation mechanisms to train multiple NN models simultaneously without impacting the accuracy of the training process. Specifically, UnifiedNN merges multiple NN models and creates a large singular unified model in order to efficiently train all models at once. We have implemented a prototype of UnifiedNN in PyTorch and we have compared its performance with relevant state-of-the-art frameworks. Our experimental results demonstrate that UnifiedNN can reduce memory consumption by up to 53% and training time by up to 81% when compared with vanilla PyTorch without impacting the model training and testing accuracy. Finally, our results indicate that UnifiedNN can reduce memory consumption by up to 52% and training time by up to 41% when compared to state-of-the-art frameworks when training multiple models concurrently.