AI Medical Compendium Topic:
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Inferior and Coordinate Distillation for Object Detectors.

Sensors (Basel, Switzerland)
Current distillation methods only distill between corresponding layers, and do not consider the knowledge contained in preceding layers. To solve this problem, we analyzed the guiding effect of the inferior features of a teacher model on the coordina...

Evaluating Ensemble Learning Methods for Multi-Modal Emotion Recognition Using Sensor Data Fusion.

Sensors (Basel, Switzerland)
Automatic recognition of human emotions is not a trivial process. There are many factors affecting emotions internally and externally. Expressing emotions could also be performed in many ways such as text, speech, body gestures or even physiologicall...

Multi-Stimuli-Responsive Synapse Based on Vertical van der Waals Heterostructures.

ACS applied materials & interfaces
Brain-inspired intelligent systems demand diverse neuromorphic devices beyond simple functionalities. Merging biomimetic sensing with weight-updating capabilities in artificial synaptic devices represents one of the key research focuses. Here, we rep...

Benchmarking structural evolution methods for training of machine learned interatomic potentials.

Journal of physics. Condensed matter : an Institute of Physics journal
When creating training data for machine-learned interatomic potentials (MLIPs), it is common to create initial structures and evolve them using molecular dynamics (MD) to sample a larger configuration space. We benchmark two other modalities of evolv...

Semisupervised Feature Selection With Sparse Discriminative Least Squares Regression.

IEEE transactions on cybernetics
In big data time, selecting informative features has become an urgent need. However, due to the huge cost of obtaining enough labeled data for supervised tasks, researchers have turned their attention to semisupervised learning, which exploits both l...

A New Belief-Based Bidirectional Transfer Classification Method.

IEEE transactions on cybernetics
In pattern classification, we may have a few labeled data points in the target domain, but a number of labeled samples are available in another related domain (called the source domain). Transfer learning can solve such classification problems via th...

Riemannian Adaptive Optimization Algorithm and its Application to Natural Language Processing.

IEEE transactions on cybernetics
This article proposes a Riemannian adaptive optimization algorithm to optimize the parameters of deep neural networks. The algorithm is an extension of both AMSGrad in Euclidean space and RAMSGrad on a Riemannian manifold. The algorithm helps to reso...

Frame-Correlation Transfers Trigger Economical Attacks on Deep Reinforcement Learning Policies.

IEEE transactions on cybernetics
Adversarial attack can be deemed as a necessary prerequisite evaluation procedure before the deployment of any reinforcement learning (RL) policy. Most existing approaches for generating adversarial attacks are gradient based and are extensive, viz.,...

Dual-Representation-Based Autoencoder for Domain Adaptation.

IEEE transactions on cybernetics
Domain adaptation aims to facilitate the learning task in an unlabeled target domain by leveraging the auxiliary knowledge in a well-labeled source domain from a different distribution. Almost existing autoencoder-based domain adaptation approaches f...

An analysis of the influence of transfer learning when measuring the tortuosity of blood vessels.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Convolutional Neural Networks (CNNs) can provide excellent results regarding the segmentation of blood vessels. One important aspect of CNNs is that they can be trained on large amounts of data and then be made available, fo...