Search
Search Results
-
High-order proximity and relation analysis for cross-network heterogeneous node classification
Cross-network node classification aims to leverage the labeled nodes from a source network to assist the learning in a target network. Existing...
-
IDaTPA: importance degree based thread partitioning approach in thread level speculation
As an auto-parallelization technique with the level of thread on multi-core, Thread-Level Speculation (TLS) which is also called Speculative...
-
X-Detect: explainable adversarial patch detection for object detectors in retail
Object detection models, which are widely used in various domains (such as retail), have been shown to be vulnerable to adversarial attacks. Existing...
-
Supervised maximum variance unfolding
Maximum Variance Unfolding (MVU) is among the first methods in nonlinear dimensionality reduction for data visualization and classification. It aims...
-
Improving interpretability via regularization of neural activation sensitivity
State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their widespread adoption in...
-
REFUEL: rule extraction for imbalanced neural node classification
Imbalanced graph node classification is a highly relevant and challenging problem in many real-world applications. The inherent data scarcity, a...
-
Predictive analysis visualization component in simulated data streams
One of the most significant problems related to Big Data is their analysis with the use of various methods from the area of descriptive statistics or...
-
Using an explainable machine learning approach to prioritize factors contributing to healthcare professionals’ burnout
Burnout in healthcare professionals (HCPs) is a global concern. Few studies use theoretical and conceptual models to assess work system stressors...
-
Not all reconstruction effects are syntactic
This paper argues that not all reconstruction effects can be reduced to a syntactic mechanism that selectively interprets copies at LF. The argument...
-
Toward efficient resource utilization at edge nodes in federated learning
Federated learning (FL) enables edge nodes to collaboratively contribute to constructing a global model without sharing their data. This is...
-
Local neighborhood encodings for imbalanced data classification
This paper aims to propose Local Neighborhood Encodings (LNE)-a hybrid data preprocessing method dedicated to skewed class distribution balancing....
-
The impact of data distribution on Q-learning with function approximation
We study the interplay between the data distribution and Q -learning-based algorithms with function approximation. We provide a unified theoretical...
-
A framework for training larger networks for deep Reinforcement learning
The success of deep learning in computer vision and natural language processing communities can be attributed to the training of very deep neural...
-
Hyperraising, evidentiality, and phase deactivation
This paper investigates an interaction between locality requirements and syntactic dependencies through the lens of hyperraising constructions in...
-
Improving search and rescue planning and resource allocation through case-based and concept-based retrieval
The need for effective and efficient search and rescue operations is more important than ever as the frequency and severity of disasters increase due...
-
POMDP inference and robust solution via deep reinforcement learning: an application to railway optimal maintenance
Partially Observable Markov Decision Processes (POMDPs) can model complex sequential decision-making problems under stochastic and uncertain...
-
A Geth-based real-time detection system for sandwich attacks in Ethereum
With the rapid development of the Ethereum ecosystem and the increasing applications of decentralized finance (DeFi), the security research of smart...
-
-
Exploiting residual errors in nonlinear online prediction
We introduce a novel online (or sequential) nonlinear prediction approach that incorporates the residuals, i.e., prediction errors in the past...
-
Probabilistic grammars for modeling dynamical systems from coarse, noisy, and partial data
Ordinary differential equations (ODEs) are a widely used formalism for the mathematical modeling of dynamical systems, a task omnipresent in...
