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Found 11 papers, 3 papers with code
The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies
This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies.
AutoCT: Automated CT registration, segmentation, and quantification
The processing and analysis of computed tomography (CT) imaging is important for both basic scientific development and clinical applications.
FAIR for AI: An interdisciplinary and international community building perspective
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data.
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses
Despite the fact that the loss functions of deep neural networks are highly non-convex, gradient-based optimization algorithms converge to approximately the same performance from many random initial points.
Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Investigating Learned Representations
Thus, HFD enables the identification of shortcomings in existing methods, a critical first step toward developing new machine learning algorithms to extract hierarchical and compositional structure in the context of naturalistic variability.
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis
Linear dimensionality reduction methods are commonly used to extract low-dimensional structure from high-dimensional data.
Numerically Recovering the Critical Points of a Deep Linear Autoencoder
Numerically locating the critical points of non-convex surfaces is a long-standing problem central to many fields.
Spiking Linear Dynamical Systems on Neuromorphic Hardware for Low-Power Brain-Machine Interfaces
Neuromorphic architectures achieve low-power operation by using many simple spiking neurons in lieu of traditional hardware.
Deep learning as a tool for neural data analysis: speech classification and cross-frequency coupling in human sensorimotor cortex
A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements.
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction
The increasing size and complexity of scientific data could dramatically enhance discovery and prediction for basic scientific applications.
Bootstrapped Adaptive Threshold Selection for Statistical Model Selection and Estimation
A central goal of neuroscience is to understand how activity in the nervous system is related to features of the external world, or to features of the nervous system itself.
