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Found 8 papers, 7 papers with code
A Compact Implicit Neural Representation for Efficient Storage of Massive 4D Functional Magnetic Resonance Imaging
Functional Magnetic Resonance Imaging (fMRI) data is a widely used kind of four-dimensional biomedical data, which requires effective compression.
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
This study introduces the CausalTime pipeline to generate time-series that highly resemble the real data and with ground truth causal graphs for quantitative performance evaluation.
HOPE: High-order Polynomial Expansion of Black-box Neural Networks
Despite their remarkable performance, deep neural networks remain mostly ``black boxes'', suggesting inexplicability and hindering their wide applications in fields requiring making rational decisions.
SHoP: A Deep Learning Framework for Solving High-order Partial Differential Equations
Solving partial differential equations (PDEs) has been a fundamental problem in computational science and of wide applications for both scientific and engineering research.
CUTS+: High-dimensional Causal Discovery from Irregular Time-series
Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios.
CUTS: Neural Causal Discovery from Irregular Time-Series Data
Causal discovery from time-series data has been a central task in machine learning.
TINC: Tree-structured Implicit Neural Compression
In this paper, we propose a Tree-structured Implicit Neural Compression (TINC) to conduct compact representation for local regions and extract the shared features of these local representations in a hierarchical manner.
SCI: A Spectrum Concentrated Implicit Neural Compression for Biomedical Data
To address this issue, we firstly derive a mathematical explanation for INR's spectrum concentration property and an analytical insight on the design of INR based compressor.
