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Found 10 papers, 2 papers with code
Structured Estimation of Heterogeneous Time Series
How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences.
Investigation of factors regarding the effects of COVID-19 pandemic on college students' depression by quantum annealer
Based on the experimental results, we confirm that QA-based algorithms have comparable capabilities in factor analysis research to the MLR models that have been widely used in previous studies.
Testing the Channels of Convolutional Neural Networks
To understand and debug convolutional neural networks (CNNs) we propose techniques for testing the channels of CNNs.
Multiple Instance Neural Networks Based on Sparse Attention for Cancer Detection using T-cell Receptor Sequences
Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in tumor biology.
GMAC: A Distributional Perspective on Actor-Critic Framework
In this paper, we devise a distributional framework on actor-critic as a solution to distributional instability, action type restriction, and conflation between samples and statistics.
Deep reinforcement learning-designed radiofrequency waveform in MRI
This work may lay the foundation for an emerging field of AI-driven RF waveform design.
A Distributional Perspective on Actor-Critic Framework
Recent distributional reinforcement learning methods, despite their successes, still contain fundamental problems that can lead to inaccurate representations of value distributions, such as distributional instability, action type restriction, and biased approximation.
Learning Loss for Test-Time Augmentation
Data augmentation has been actively studied for robust neural networks.
Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data
Despite these new opportunities psychological researchers have not taken full advantage of promising opportunities inherent to this data, the potential to forecast psychological processes at the individual level.
ACE: Artificial Checkerboard Enhancer to Induce and Evade Adversarial Attacks
The checkerboard phenomenon is one of the well-known visual artifacts in the computer vision field.
