Search Results for author:
Found 9 papers, 5 papers with code
Are EEG Sequences Time Series? EEG Classification with Time Series Models and Joint Subject Training
For EEG classification many models have been developed with layer types and architectures we typically do not see in time series classification.
Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research
Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years.
ProbSAINT: Probabilistic Tabular Regression for Used Car Pricing
Used car pricing is a critical aspect of the automotive industry, influenced by many economic factors and market dynamics.
Moco: A Learnable Meta Optimizer for Combinatorial Optimization
Our approach, Moco, learns a graph neural network that updates the solution construction procedure based on features extracted from the current search state.
Selecting Features by their Resilience to the Curse of Dimensionality
Real-world datasets are often of high dimension and effected by the curse of dimensionality.
Intrinsic Dimension for Large-Scale Geometric Learning
In the present work, we derive a computationally feasible method for determining said axiomatic ID functions.
LG4AV: Combining Language Models and Graph Neural Networks for Author Verification
The automatic verification of document authorships is important in various settings.
FCA2VEC: Embedding Techniques for Formal Concept Analysis
Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets.
Orometric Methods in Bounded Metric Data
Notably, metric sets of items inclosed in knowledge graphs.
