AIMC Topic: Learning

Clear Filters Showing 1321 to 1330 of 1476 articles

Neural Networks for Navigation: From Connections to Computations.

Annual review of neuroscience
Many animals can navigate toward a goal they cannot see based on an internal representation of that goal in the brain's spatial maps. These maps are organized around networks with stable fixed-point dynamics (attractors), anchored to landmarks, and r...

Analysis of Motor Control and Learning in Human-Robot Interaction during Game Guided Movements.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Robotic devices can be used in upper limb rehabilitation in order to help the total or partial functional recovery. Robots can perform repetitive activities for a long period of time, which may be beneficial for rehabilitation processes. In this cont...

Towards an Action Recognition Framework for Endovascular Surgery.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Objective knowledge about instrument manoeuvres in endovascular surgery is essential for evaluating surgical skills and developing advanced technologies for cathlab routines. To the recent day, endovascular navigation has been exclusively assessed in...

Clinical Risk Prediction Models with Meta-Learning Prototypes of Patient Heterogeneity.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Hospitalized patients sometimes have complex health conditions, such as multiple diseases, underlying diseases, and complications. The heterogeneous patient conditions may have various representations. A generalized model ignores the differences amon...

Learning Representations from Medical Text for Effective Diagnoses and Knowledge Discovery.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Discovering knowledge and effectively predicting target events are two main goals of medical text mining. However, few models can achieve them simultaneously. In this study, we investigated the possibility of discovering knowledge and predicting diag...

Deep transfer learning-based variable Doppler underwater acoustic communications.

The Journal of the Acoustical Society of America
This paper proposes a deep transfer learning (DTL)-based variable Doppler frequency-hopping binary frequency-shift keying underwater acoustic communication system. The system uses a convolutional neural network (CNN) as the demodulation module of the...

Reinforcement learning relieves the vaccination dilemma.

Chaos (Woodbury, N.Y.)
The main goal of this paper is to study how a decision-making rule for vaccination can affect epidemic spreading by exploiting the Bush-Mosteller (BM) model, one of the methodologies in reinforcement learning in artificial intelligence (AI), which ca...

Model-Independent Learning of Quantum Phases of Matter with Quantum Convolutional Neural Networks.

Physical review letters
Quantum convolutional neural networks (QCNNs) have been introduced as classifiers for gapped quantum phases of matter. Here, we propose a model-independent protocol for training QCNNs to discover order parameters that are unchanged under phase-preser...

A data-driven framework for learning hybrid dynamical systems.

Chaos (Woodbury, N.Y.)
The existing data-driven identification methods for hybrid dynamical systems such as sparse optimization are usually limited to parameter identification for coefficients of pre-defined candidate functions or composition of prescribed function forms, ...

Few-Shot and Prompt Training for Text Classification in German Doctor's Letters.

Studies in health technology and informatics
To classify sentences in cardiovascular German doctor's letters into eleven section categories, we used pattern-exploiting training, a prompt-based method for text classification in few-shot learning scenarios (20, 50 and 100 instances per class) usi...