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Found 18 papers, 2 papers with code
Few-Shot Tabular Data Enrichment Using Fine-Tuned Transformer Architectures
In this study we proposed Few-Shot Transformer based Enrichment (FeSTE), a generic and robust framework for the enrichment of tabular datasets using unstructured data.
Markov flow policy -- deep MC
Discounted algorithms often encounter evaluation errors due to their reliance on short-term estimations, which can impede their efficacy in addressing simple, short-term tasks and impose undesired temporal discounts (\(\gamma\)).
Detecting Anomalous Network Communication Patterns Using Graph Convolutional Networks
To protect an organizations' endpoints from sophisticated cyberattacks, advanced detection methods are required.
ReMark: Receptive Field based Spatial WaterMark Embedding Optimization using Deep Network
Watermarking is one of the most important copyright protection tools for digital media.
A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection
Many challenging real-world problems require the deployment of ensembles multiple complementary learning models to reach acceptable performance levels.
Secure Machine Learning in the Cloud Using One Way Scrambling by Deconvolution
Cloud-based machine learning services (CMLS) enable organizations to take advantage of advanced models that are pre-trained on large quantities of data.
Anomaly Detection for Aggregated Data Using Multi-Graph Autoencoder
In this research, we present a thorough analysis of the aggregated data and the relationships between aggregated events.
Hierarchical Deep Reinforcement Learning Approach for Multi-Objective Scheduling With Varying Queue Sizes
In this study we present MERLIN, a robust, modular and near-optimal DRL-based approach for multi-objective task scheduling.
PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction
Improving the robustness of neural nets in regression tasks is key to their application in multiple domains.
Automatic Machine Learning Derived from Scholarly Big Data
One of the challenging aspects of applying machine learning is the need to identify the algorithms that will perform best for a given dataset.
RankML: a Meta Learning-Based Approach for Pre-Ranking Machine Learning Pipelines
In this study, we propose RankML, a meta-learning based approach for predicting the performance of whole machine learning pipelines.
DeepLine: AutoML Tool for Pipelines Generation using Deep Reinforcement Learning and Hierarchical Actions Filtering
The second challenge is the computational cost of training and evaluating multiple pipelines.
Assessing the Quality of Scientific Papers
In this paper, we propose a measure and method for assessing the overall quality of the scientific papers in a particular field of study.
Transferable Cost-Aware Security Policy Implementation for Malware Detection Using Deep Reinforcement Learning
This security policy is then implemented, and for each inspected file, a different set of detectors is assigned and a different detection threshold is set.
New Item Consumption Prediction Using Deep Learning
Our experiments show that PISA outperforms a well-known deep learning baseline when new items are introduced.
MDGAN: Boosting Anomaly Detection Using \\Multi-Discriminator Generative Adversarial Networks
Anomaly detection is often considered a challenging field of machine learning due to the difficulty of obtaining anomalous samples for training and the need to obtain a sufficient amount of training data.
Wikiometrics: A Wikipedia Based Ranking System
We present a new concept - Wikiometrics - the derivation of metrics and indicators from Wikipedia.
ExploreKit: Automatic Feature Generation and Selection
To overcome the exponential growth of the feature space, ExploreKit uses a novel machine learning-based feature selection approach to predict the usefulness of new candidate features.
