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Found 9 papers, 6 papers with code
Metrics to guide development of machine learning algorithms for malaria diagnosis
Two factors in particular are crucial to developing algorithms translatable to clinical field settings: (i) Clear understanding of the clinical needs that ML solutions must accommodate; and (ii) task-relevant metrics for guiding and evaluating ML models.
PySINDy: A comprehensive Python package for robust sparse system identification
Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community.
A toolkit for data-driven discovery of governing equations in high-noise regimes
Second, we propose a technique, applicable to any model discovery method based on x' = f(x), to assess the accuracy of a discovered model in the context of non-unique solutions due to noisy data.
Insect Cyborgs: Bio-mimetic Feature Generators Improve ML Accuracy on Limited Data
In this work we deploy MothNet, a computational model of the moth olfactory network, as an automatic feature generator.
Fully-automated patient-level malaria assessment on field-prepared thin blood film microscopy images, including Supplementary Information
Malaria is a life-threatening disease affecting millions.
Money on the Table: Statistical information ignored by Softmax can improve classifier accuracy
To explore this potential resource, we develop a hybrid classifier (Softmax-Pooling Hybrid, $SPH$) that uses Softmax on high-scoring samples, but on low-scoring samples uses a log-likelihood method that pools the information from the full array $D$.
Insect cyborgs: Bio-mimetic feature generators improve machine learning accuracy on limited data
In this work, we deployed MothNet, a computational model of the insect olfactory network, as an automatic feature generator: Attached as a front-end pre-processor, its Readout Neurons provided new features, derived from the original features, for use by standard ML classifiers.
Putting a bug in ML: The moth olfactory network learns to read MNIST
The Moth Olfactory Network is among the simplest biological neural systems that can learn, and its architecture includes key structural elements and mechanisms widespread in biological neural nets, such as cascaded networks, competitive inhibition, high intrinsic noise, sparsity, reward mechanisms, and Hebbian plasticity.
Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, with Applications to Neural Nets
From a biological perspective, the model provides a valuable tool for examining the role of neuromodulators, like octopamine, in learning, and gives insight into critical interactions between sparsity, Hebbian growth, and stimulation during learning.
