AIMC Topic: Learning

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Learning to activate logic rules for textual reasoning.

Neural networks : the official journal of the International Neural Network Society
Most current textual reasoning models cannotlearn human-like reasoning process, and thus lack interpretability and logical accuracy. To help address this issue, we propose a novel reasoning model which learns to activate logic rules explicitly via de...

Emergent Solutions to High-Dimensional Multitask Reinforcement Learning.

Evolutionary computation
Algorithms that learn through environmental interaction and delayed rewards, or reinforcement learning (RL), increasingly face the challenge of scaling to dynamic, high-dimensional, and partially observable environments. Significant attention is bein...

Attractor Dynamics in Networks with Learning Rules Inferred from In Vivo Data.

Neuron
The attractor neural network scenario is a popular scenario for memory storage in the association cortex, but there is still a large gap between models based on this scenario and experimental data. We study a recurrent network model in which both lea...

Deep Neural Networks for Modeling Visual Perceptual Learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Understanding visual perceptual learning (VPL) has become increasingly more challenging as new phenomena are discovered with novel stimuli and training paradigms. Although existing models aid our knowledge of critical aspects of VPL, the connections ...

The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users.

PLoS biology
This work aims at corroborating the importance and efficacy of mutual learning in motor imagery (MI) brain-computer interface (BCI) by leveraging the insights obtained through our participation in the BCI race of the Cybathlon event. We hypothesized ...

Behavioral Learning in a Cognitive Neuromorphic Robot: An Integrative Approach.

IEEE transactions on neural networks and learning systems
We present here a learning system using the iCub humanoid robot and the SpiNNaker neuromorphic chip to solve the real-world task of object-specific attention. Integrating spiking neural networks with robots introduces considerable complexity for ques...

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Matched Filtering and Convolutional Neural Network.

Sensors (Basel, Switzerland)
As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. However, there are still two probl...

Effect of dilution in asymmetric recurrent neural networks.

Neural networks : the official journal of the International Neural Network Society
We study with numerical simulation the possible limit behaviors of synchronous discrete-time deterministic recurrent neural networks composed of N binary neurons as a function of a network's level of dilution and asymmetry. The network dilution measu...

Spiking neural networks for handwritten digit recognition-Supervised learning and network optimization.

Neural networks : the official journal of the International Neural Network Society
We demonstrate supervised learning in Spiking Neural Networks (SNNs) for the problem of handwritten digit recognition using the spike triggered Normalized Approximate Descent (NormAD) algorithm. Our network that employs neurons operating at sparse bi...

Personalized response generation by Dual-learning based domain adaptation.

Neural networks : the official journal of the International Neural Network Society
Open-domain conversation is one of the most challenging artificial intelligence problems, which involves language understanding, reasoning, and the utilization of common sense knowledge. The goal of this paper is to further improve the response gener...