AIMC Topic: Machine Learning

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Predictive Coding Approximates Backprop Along Arbitrary Computation Graphs.

Neural computation
Backpropagation of error (backprop) is a powerful algorithm for training machine learning architectures through end-to-end differentiation. Recently it has been shown that backprop in multilayer perceptrons (MLPs) can be approximated using predictive...

Hypothesis Test and Confidence Analysis With Wasserstein Distance on General Dimension.

Neural computation
We develop a general framework for statistical inference with the 1-Wasserstein distance. Recently, the Wasserstein distance has attracted considerable attention and has been widely applied to various machine learning tasks because of its excellent p...

Advancements in Algorithms and Neuromorphic Hardware for Spiking Neural Networks.

Neural computation
Artificial neural networks (ANNs) have experienced a rapid advancement for their success in various application domains, including autonomous driving and drone vision. Researchers have been improving the performance efficiency and computational requi...

Potential for Machine Learning in Burn Care.

Journal of burn care & research : official publication of the American Burn Association
Burn-related injuries are a leading cause of morbidity across the globe. Accurate assessment and treatment have been demonstrated to reduce the morbidity and mortality. This essay explores the forms of artificial intelligence to be implemented the fi...

Development of an Architecture to Implement Machine Learning Based Risk Prediction in Clinical Routine: A Service-Oriented Approach.

Studies in health technology and informatics
BACKGROUND: Patients at risk of developing a disease have to be identified at an early stage to enable prevention. One way of early detection is the use of machine learning based prediction models trained on electronic health records.

SAINTENS: Self-Attention and Intersample Attention Transformer for Digital Biomarker Development Using Tabular Healthcare Real World Data.

Studies in health technology and informatics
BACKGROUND: Deep learning currently struggles with tabular data, but it can benefit from multimodal learning. SAINT is a deep learning model for tabular data on which we base our presented developments.

Promoting the Importance of Recall Visits Among Dental Patients in India Using a Semi-Autonomous AI System.

Studies in health technology and informatics
In many developing countries like India, there is a widespread lack of general awareness about the importance of good oral health, which causes dental patients to neglect their oral hygiene, thus precipitating many long-term ailments. We developed an...

Transfer learning using attentions across atomic systems with graph neural networks (TAAG).

The Journal of chemical physics
Recent advances in Graph Neural Networks (GNNs) have transformed the space of molecular and catalyst discovery. Despite the fact that the underlying physics across these domains remain the same, most prior work has focused on building domain-specific...

Boost-RS: boosted embeddings for recommender systems and its application to enzyme-substrate interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Despite experimental and curation efforts, the extent of enzyme promiscuity on substrates continues to be largely unexplored and under documented. Providing computational tools for the exploration of the enzyme-substrate interaction space...