A review of ML and DL for ecological data analysis.
problem Understanding the strengths and limitations of ML and DL in ecological research.
method Historical overview, algorithm families, differences, universal principles, and emerging trends.
result ML and DL excel in prediction tasks but are still debated for causal inference.
Machine Learning (ML) and Deep Learning (DL) innovations are being introduced at such a rapid pace that model owners and evaluators are hard-pressed analyzing and studying them. This is exacerbated by the complicated procedures for evaluation. The lack of standard systems and efficient techniques for specifying and pro…
Machine learning and deep learning infer surface/groundwater exchange from temperature data.
problem Inferring surface/groundwater exchange from temperature data with high temporal resolution.
method Application of machine learning and deep learning algorithms to infer surface/groundwater exchange flux from subsurface temperature observations.
result DL methods outperform ML methods in interpreting noisy temperature data, especially with a smoothing filter.
This paper reviews adversarial attacks on ML and DL models.
problem Adversarial attacks on ML and DL models.
method Review of existing adversarial attacks and perturbations.
result Comprehensive understanding of adversarial security attacks on ML and DL.
Survey on securing ML for healthcare, addressing privacy and robustness issues.
problem Security and robustness challenges in healthcare ML/DL applications.
method Overview of security and privacy methods for ML in healthcare.
result Discussion of current research challenges and future directions.
MLModelScope streamlines ML/DL model evaluation and benchmarking.
problem Challenges in evaluating and benchmarking ML/DL models.
method Open-source, framework/hardware agnostic platform with distributed design.
result Demonstrates the impact of model evaluation pipelines and HW/SW choices.
DL2 uses deep learning to optimize resource allocation in DL clusters.
problem Efficient resource scheduling for deep learning clusters is challenging.
method DL2 combines supervised learning and reinforcement learning to dynamically allocate resources.
result DL2 reduces average training completion time by 44.1% compared to fairness scheduler.
In this survey paper, we systematically summarize existing literature on bearing fault diagnostics with machine learning (ML) and data mining techniques. While conventional ML methods, including artificial neural network (ANN), principal component analysis (PCA), support vector machines (SVM), etc., have been successfu…
This review examines DL models for financial forecasting.
problem Lack of comprehensive reviews on DL for financial forecasting.
method Categorized studies by forecasting area and DL model type.
result DL models outperform traditional ML methods.
Paper proposes fairgroup construction to improve fairness in Medicaid eligibility decisions.
problem Improper decisions in Medicaid eligibility allocation due to ML/DL model limitations.
method Fairgroup construction based on legal doctrine of disparate impact.
result Demonstrates improved fairness in regressive classifiers for Medicaid eligibility decisions.
Predictive models can be used for causal inference with feature selection.
problem Limitations of predictive models in interpreting causal relationships.
method Constrained learning process by selecting features according to Pearl's backdoor adjustment criterion.
result Causal models provide near unbiased effect estimates and better generalization.
The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy is…
SplitEasy trains ML models on mobile devices without server data transfer.
problem Training complex DL models on resource-limited mobile devices.
method Split learning approach where sensitive layers are trained locally, computationally intensive layers on server.
result SplitEasy trains models on mobile devices with minimal data transfer, near-constant time per sample.
Carbontracker tracks and predicts training DL models' carbon footprint.
problem Exponential growth in energy consumption for training deep learning models.
method Carbontracker tool for tracking and predicting energy and carbon footprint.
result Promotes responsible computing and encourages energy-efficient deep learning.
Survey on uncertainty in ML and DL, covering sources, quantification, and decision-making.
problem Understanding and quantifying uncertainty in ML and DL for risk-sensitive applications.
method Structured review of literature, categorizing uncertainty, assessing uncertainty quantification techniques.
result Broadened scope of uncertainty discussion and updated DL uncertainty quantification methods.
The IB theory explains how ML systems reduce data dimensions while preserving predictive power.
problem Understanding how machine learning systems reduce data dimensions effectively.
method Information-theoretic approach using mutual information.
result The best representation T maximally informs Y while minimizing mutual information with X. Deep learning faces adoption challenges in business analytics.
problem Adoption of deep learning in business analytics is hindered by various factors.
method Empirical study based on three industry use cases.
result Gradient boosting is recommended for structured datasets in business analytics.
Deep learning aids in autism diagnosis and rehabilitation using neuroimaging data.
problem Challenges in automated detection and rehabilitation of ASD using neuroimaging data.
method Deep learning techniques applied to neuroimaging data for ASD diagnosis and rehabilitation.
result Deep learning improves accuracy in ASD diagnosis and rehabilitation.
Survey of deep learning models in finance.
problem Improving financial models with deep learning.
method Categorized and analyzed financial applications of deep learning models.
result Outperformance of deep learning over classical models in finance.
Systematic review of ML models for detecting social media deception.
problem Detecting fake news, spam, and fake accounts on social media.
method 36 studies evaluated using PROBAST tool, identifying biases and limitations.
result Over-reliance on accuracy in imbalanced data settings is a flaw.
Existing malware detectors on safety-critical devices have difficulties in runtime detection due to the performance overhead. In this paper, we introduce PROPEDEUTICA, a framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) te…
Deep learning helps identify promising startups.
problem Identifying successful startups amidst many.
method Literature review and synthesis of DL-based startup evaluation methods.
result Deep learning shows promise in startup success prediction.
Advanced ML/DL models predict stock prices using technical analysis.
problem Accurately predicting stock prices in a complex market.
method Use of deep learning models for stock price prediction.
result Deep learning models can predict stock prices with high accuracy.
This review explores ML and DL techniques for detecting distracted driving across various modalities.
problem Improving detection of complex distraction patterns, especially cognitive distractions.
method Categorizes and evaluates studies based on modality, data accessibility, and methodology.
result Multimodal systems outperform single-modal systems in detecting complex distraction patterns.
Deep learning predicts plant growth and yield in greenhouses.
problem Predicting plant growth and yield for better greenhouse management.
method Utilized a new deep recurrent neural network (RNN) with LSTM neurons to model growth parameters.
result Deep learning models outperformed traditional ML methods in predicting plant growth and yield.
The digitalization of the legal domain has been ongoing for a couple of years. In that process, the application of different machine learning (ML) techniques is crucial. Tasks such as the classification of legal documents or contract clauses as well as the translation of those are highly relevant. On the other side, di…
Machine learning speeds up RIS design for efficient RF components.
problem Designing reconfigurable intelligent surfaces (RIS) for efficient RF components is time-consuming and resource-intensive.
method Machine/deep learning techniques are used to reduce the computational cost and time of RIS inverse design.
result Machine learning techniques significantly reduce the time and computational cost of RIS design.
This paper reviews traditional and modern methods for detecting structural damage using vibrations.
problem Early warning of structural damage to maintain civil structures.
method Vibration-based methods and ML/DL algorithms.
result ML and DL algorithms show superior performance in detecting structural damage.
The practical success of widely used machine learning (ML) and deep learning (DL) algorithms in Artificial Intelligence (AI) community owes to availability of large datasets for training and huge computational resources. Despite the enormous practical success of AI, these algorithms are only loosely inspired from the b…
RE enhances DL by learning model behavior, enabling iterative self-improvement.
problem Static data representations limit DL's potential for evolving models.
method RE uses multiple mappings of data through identical deep architectures, analyzing internal representations and performance signals.
result Models can gain insight from predecessors, leading to iterative self-improvement.
Machine Learning (ML) and Deep Learning (DL) models have achieved state-of-the-art performance on multiple learning tasks, from vision to natural language modelling. With the growing adoption of ML and DL to many areas of computer science, recent research has also started focusing on the security properties of these mo…
Paper predicts M&A deal success using ML and DL techniques.
problem Predicting the success of M&A deals to avoid costly mistakes.
method Data preprocessing with ML techniques, feedforward neural networks, and sentiment scores integration.
result Methodology outperforms benchmark models in preliminary tests.
Study improves stock price prediction using advanced ML models.
problem Improving financial forecasting accuracy in stock markets.
method Evaluation of RNN architectures including LSTM, GRU, and attention-based models.
result Attention-based models outperform others in capturing complex dependencies.
Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a large number of hospitalized patients. Accurate early prediction of AKI can enable in-time interventions and treatments. However, AKI is hig…
GBM outperforms DL in credit scoring tasks, but performance depends on dataset.
problem Benchmarking deep learning vs. gradient boosting for credit scoring.
method Used three datasets with different features to compare DL and GBM.
result GBM is more powerful and faster than DL for credit scoring.
MAX simplifies access to DL models for non-experts.
problem Difficulty for non-experts in adopting latest DL models.
method Proposes MAX, a Python library that wraps DL models and provides RESTful APIs.
result Maximizes ease of using state-of-the-art DL models for inference.
DLBricks automates DL benchmarking on CPUs, reducing effort and time.
problem Lack of representative and up-to-date DL benchmarks on CPUs.
method Decomposes DL models into runnable networks, leveraging layer repetition and auto-generation.
result Accurately estimates DL model performance and speeds up benchmarking time.
This dissertation automates deep learning pipelines and uses meta-learning for better model selection and data augmentation.
problem Challenges in selecting and fine-tuning deep learning pipelines for new datasets.
method Meta-learning for DL pipeline selection and data augmentation, using synthetic data.
result Meta-learned approaches outperform traditional methods in automated DL pipeline selection and data augmentation.
ANN-based dynamic portfolio optimization outperforms traditional methods.
problem Optimizing dynamic portfolios in finance.
method Using artificial neural networks for dynamic portfolio allocation.
result ANN-based strategies outperform traditional methods in optimal portfolio allocation.
mlr3torch simplifies deep learning in R for tabular and image data.
problem Simplifying deep learning workflows in R for various data types.
method Built on torch, integrates mlr3 ecosystem, supports custom architectures.
result Extensible framework for tabular and image data, including preprocessing and model definition.
The paper proposes a tool to detect invalid inputs in DL models.
problem Vulnerability of DL models to invalid inputs during runtime.
method Design and implementation of a tool that extracts data flow footprints and conducts assertion-based validation.
result The assertion-based data sanity check mechanism effectively identifies invalid input cases.
Paper builds ML classifier to detect crypto-ransomware.
problem Detecting crypto-ransomware with high accuracy and low false positives.
method Behavior-based detection using input/output activities and file-content entropy. Deep-learning classifier with adversarial research and Integrated Gradient method for explanation.
result Deep-learning classifier achieves high accuracy and low false positive rate in detecting crypto-ransomware.
A scalable DL benchmarking platform for evaluating and comparing models, frameworks, and hardware.
problem Lack of a uniform DL benchmarking platform for evaluating and comparing innovations.
method Identified 10 design features for a DL benchmarking platform, proposed MLModelScope, and implemented as an open-source project.
result Demonstrated how model, hardware, and framework selection affect accuracy and performance under different scenarios.
Machine learning improves sub-seasonal climate forecasting, especially gradient boosting and deep learning.
problem Predicting climate variables like temperature and precipitation in 2-week to 2-month time scales.
method Carefully constructed feature representations and ML approaches including gradient boosting and deep learning.
result ML methods can outperform climatological baselines and improve prediction accuracy.
DL/FBF improves GPSR solutions by selecting compact, generalising expressions.
problem Overfitting and structural bloat in symbolic regression with genetic programming.
method Description length (DL) and fractional Bayes factor (FBF) criteria for selecting compact, generalising expressions.
result DL/FBF post-selection improves test performance compared to AIC/BIC baseline.
Recent breakthroughs in Deep Learning (DL) applications have made DL models a key component in almost every modern computing system. The increased popularity of DL applications deployed on a wide-spectrum of platforms have resulted in a plethora of design challenges related to the constraints introduced by the hardware…
Study compares ML and DL methods for autism classification.
problem Classifying autism from healthy individuals using rs-fMRI data.
method Developed classification frameworks for rs-fMRI connectivity patterns.
result Best model achieved 71% accuracy on multisite ABIDE I data.
Generative Adversarial Networks create synthetic data for structural damage detection.
problem Data scarcity in structural damage detection.
method 1-D Wasserstein Deep Convolutional Generative Adversarial Networks (1-D WDCGAN-GP) for synthetic data generation.
result Generated synthetic data improves damage detection accuracy in 1-D Deep Convolutional Neural Networks.