Demystifying black-box models with symbolic metamodels
Understanding the predictions of a machine learning model can be as crucial as the model's
accuracy in many application domains. However, the black-box nature of most highly …
accuracy in many application domains. However, the black-box nature of most highly …
Pan-cortical 2-photon mesoscopic imaging and neurobehavioral alignment in awake, behaving mice
The flow of neural activity across the neocortex during active sensory discrimination is
constrained by task-specific cognitive demands, movements, and internal states. During …
constrained by task-specific cognitive demands, movements, and internal states. During …
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility of AI for scientific
discovery. The distinct goals of AI for industry versus the goals of AI for science create …
discovery. The distinct goals of AI for industry versus the goals of AI for science create …
Columnar localization and laminar origin of cortical surface electrical potentials
Electrocorticography (ECoG) methodologically bridges basic neuroscience and
understanding of human brains in health and disease. However, the localization of ECoG …
understanding of human brains in health and disease. However, the localization of ECoG …
UoI-NMF cluster: a robust nonnegative matrix factorization algorithm for improved parts-based decomposition and reconstruction of noisy data
With the ever growing collection of large volumes of scientific data, development of
interpretable machine learning tools to analyze such data is becoming more important …
interpretable machine learning tools to analyze such data is becoming more important …
[PDF][PDF] Pyuoi: The union of intersections framework in python
The increasing size and complexity of scientific data requires statistical analysis methods
that scale and produce models that are both interpretable and predictive. Interpretability …
that scale and produce models that are both interpretable and predictive. Interpretability …
Practical Considerations for Machine Learning-Enabled Discoveries in Spatial Transcriptomics
Development and homeostasis in multicellular systems require exquisite control over spatial
molecular pattern formation and maintenance. Advances in spatially resolved and high …
molecular pattern formation and maintenance. Advances in spatially resolved and high …
NIPS-not even wrong? A systematic review of empirically complete demonstrations of algorithmic effectiveness in the machine learning and artificial intelligence …
Objective: To determine the completeness of argumentative steps necessary to conclude
effectiveness of an algorithm in a sample of current ML/AI supervised learning literature …
effectiveness of an algorithm in a sample of current ML/AI supervised learning literature …
Numerical characterization of support recovery in sparse regression with correlated design
Sparse regression is employed in diverse scientific settings as a feature selection method. A
pervasive aspect of scientific data is the presence of correlations between predictive …
pervasive aspect of scientific data is the presence of correlations between predictive …
[HTML][HTML] Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data
Abstract Development and homeostasis in multicellular systems both require exquisite
control over spatial molecular pattern formation and maintenance. Advances in spatially …
control over spatial molecular pattern formation and maintenance. Advances in spatially …