AIMC Topic: Learning

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What is the simplest model that can account for high-fidelity imitation?

The Behavioral and brain sciences
What inductive biases must be incorporated into multi-agent artificial intelligence models to get them to capture high-fidelity imitation? We think very little is needed. In the right environments, both instrumental- and ritual-stance imitation can e...

Efficient Perturbation Inference and Expandable Network for continual learning.

Neural networks : the official journal of the International Neural Network Society
Although humans are capable of learning new tasks without forgetting previous ones, most neural networks fail to do so because learning new tasks could override the knowledge acquired from previous data. In this work, we alleviate this issue by propo...

Factors of Influence for Transfer Learning Across Diverse Appearance Domains and Task Types.

IEEE transactions on pattern analysis and machine intelligence
Transfer learning enables to re-use knowledge learned on a source task to help learning a target task. A simple form of transfer learning is common in current state-of-the-art computer vision models, i.e., pre-training a model for image classificatio...

Ada-LISTA: Learned Solvers Adaptive to Varying Models.

IEEE transactions on pattern analysis and machine intelligence
Neural networks that are based on the unfolding of iterative solvers as LISTA (Learned Iterative Soft Shrinkage), are widely used due to their accelerated performance. These networks, trained with a fixed dictionary, are inapplicable in varying model...

A Novel Deep Neural Network Method for HAR-Based Team Training Using Body-Worn Inertial Sensors.

Sensors (Basel, Switzerland)
Human activity recognition (HAR) became a challenging issue in recent years. In this paper, we propose a novel approach to tackle indistinguishable activity recognition based on human wearable sensors. Generally speaking, vision-based solutions strug...

An emotion analysis in learning environment based on theme-specified drawing by convolutional neural network.

Frontiers in public health
Emotion in the learning process can directly influence the learner's attention, memory, and cognitive activities. Several literatures indicate that hand-drawn painting could reflect the learner's emotional status. But, such an evaluation of emotional...

Capturing advanced human cognitive abilities with deep neural networks.

Trends in cognitive sciences
How can artificial neural networks capture the advanced cognitive abilities of pioneering scientists? I suggest they must learn to exploit human-invented tools of thought and human-like ways of using them, and must engage in explicit goal-directed pr...

Prediction of drug-target interactions through multi-task learning.

Scientific reports
Identifying the binding between the target proteins and molecules is essential in drug discovery. The multi-task learning method has been introduced to facilitate knowledge sharing among tasks when the amount of information for each task is small. Ho...

ReLMole: Molecular Representation Learning Based on Two-Level Graph Similarities.

Journal of chemical information and modeling
Molecular representation is a critical part of various prediction tasks for physicochemical properties of molecules and drug design. As graph notations are common in expressing the structural information of chemical compounds, graph neural networks (...