On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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6 results for “BIM”

Classification of Building Information Model (BIM) Structures with Deep Learningcs.CV

Deep learning outperforms classical methods in categorizing BIM images.

problem Classifying building designs from BIM models.

method Used classical machine learning (HOG + SVM) and deep learning models (pre-trained and custom-designed networks).

result Deep learning models achieve significantly higher accuracy (above 89%) compared to classical methods (57%).

Building Information Modeling BIM Deep Learning Machine Learning Classification

2018-08-01abs ↗pdf ↗

Adversarial Robustness via Label-Smoothingcs.LG

Label-Smoothing improves adversarial robustness of deep learning models.

problem Improving the robustness of deep learning models against adversarial attacks.

method Label-Smoothing, including adversarial, Boltzmann, and second-best variants, applied to various models and datasets.

result Label-Smoothing enhances adversarial robustness across multiple attacks and datasets.

Label-Smoothing Adversarial Robustness Deep Learning Supervised Learning Cross-Entropy Loss

2019-06-27abs ↗pdf ↗

ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustnesscs.LG

Paper introduces techniques to enhance time series data robustness against adversarial attacks.

problem Vulnerability of deep learning time series classifiers to adversarial attacks.

method Two local gradient based and one spectral density based data augmentation techniques.

result Trained models with augmented data achieve state-of-the-art classification accuracy and robustness.

time series adversarial attacks data augmentation robustness deep learning

2019-10-15abs ↗pdf ↗

Crafting Adversarial Examples for Deep Learning Based Prognostics (Extended Version)cs.LG

Paper tackles cyber threats to PHM systems using adversarial examples.

problem Vulnerability of IoT sensors and DL algorithms to cyber attacks.

method Adopted adversarial example crafting techniques from computer vision to PHM domain.

result PHM models are vulnerable to adversarial attacks, leading to inaccurate remaining useful life estimation.

cyber attacks PHM systems adversarial examples DL algorithms IoT sensors

2020-09-21abs ↗pdf ↗

Using Single-Step Adversarial Training to Defend Iterative Adversarial Examplescs.LG

New method defends against both single-step and iterative adversarial examples.

problem Defending against adversarial examples, especially iterative ones, is computationally expensive.

method Single-Step Adversarial Training (SST) with modifications.

result Our method outperforms state-of-the-art methods in both accuracy and training time.

Adversarial Training Iterative Adversarial Examples Single-Step CIFAR10 Training Time

2020-02-22abs ↗pdf ↗

Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domaincs.LG

Study tackles discretization problem in crafting adversarial examples for discrete integer domains.

problem Discretization problem in crafting adversarial examples for discrete integer domains.

method Proposes a black-box method to reduce adversarial example searching to a derivative-free optimization problem.

result Significantly higher success rate in crafting adversarial images in discrete integer domain compared to black-box methods.

discretization problem adversarial examples black-box attacks derivative-free optimization NIPS competition

2019-05-19abs ↗pdf ↗