AIMC Topic: Learning

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Learning to track multiple targets.

IEEE transactions on neural networks and learning systems
Monocular multiple-object tracking is a fundamental yet under-addressed computer vision problem. In this paper, we propose a novel learning framework for tracking multiple objects by detection. First, instead of heuristically defining a tracking algo...

Robust sensorimotor representation to physical interaction changes in humanoid motion learning.

IEEE transactions on neural networks and learning systems
This paper proposes a learning from demonstration system based on a motion feature, called phase transfer sequence. The system aims to synthesize the knowledge on humanoid whole body motions learned during teacher-supported interactions, and apply th...

Combining extreme learning machines using support vector machines for breast tissue classification.

Computer methods in biomechanics and biomedical engineering
In this paper, we present a new approach for breast tissue classification using the features derived from electrical impedance spectroscopy. This method is composed of a feature extraction method, feature selection phase and a classification step. Th...

Neural Associative Skill Memories for Safer Robotics and Modeling Human Sensorimotor Repertoires.

Neural computation
Modern robots face a challenge shared by biological systems: how to learn and adaptively express multiple sensorimotor skills. A key aspect of this is developing an internal model of expected sensorimotor experiences to detect and react to unexpected...

Sequential Learning in the Dense Associative Memory.

Neural computation
Sequential learning involves learning tasks in a sequence and proves challenging for most neural networks. Biological neural networks regularly succeed at the sequential learning challenge and are even capable of transferring knowledge both forward a...

Exploring the Role and Potential of Chatbots in Learning From the Perspective of Nursing Students: A Systematic Review of Qualitative Studies.

International nursing review
AIM: This study explores nursing students' experiences and needs regarding chatbot-assisted learning, while evaluating the opportunities and challenges they encounter.

Cerebellar circuit computations for predictive motor control.

Nature reviews. Neuroscience
The rise of the deep neural network as the workhorse of artificial intelligence has brought increased attention to how network architectures serve specialized functions. The cerebellum, with its largely shallow, feedforward architecture, provides a c...

Mindset matters: exploring the link between mindsets, learning intentions, and performance in biomedical science students.

Advances in physiology education
Students' "mindset" (self-beliefs and attitudes toward their abilities) can impact academic achievement, with those possessing a growth mindset more likely to succeed. It has been postulated that students with a growth mindset, who believe they can i...

Introducing societal issues in an upper level STEM course increases student engagement and knowledge transfer.

Developmental biology
The ability of students to transfer their knowledge and understanding learned from one context to a novel context is the ultimate goal of education. Creating assignments that engage students through something they care about can help create the envir...