AIMC Topic: Learning
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Representational drift as a window into neural and behavioural plasticity.
Current opinion in neurobiology
Jun 29, 2023
Large-scale recordings of neural activity over days and weeks have revealed that neural representations of familiar tasks, precepts and actions continually evolve without obvious changes in behaviour. We hypothesise that this steady drift in neural a...
A spatio-temporal network for video semantic segmentation in surgical videos.
International journal of computer assisted radiology and surgery
Jun 22, 2023
PURPOSE: Semantic segmentation in surgical videos has applications in intra-operative guidance, post-operative analytics and surgical education. Models need to provide accurate predictions since temporally inconsistent identification of anatomy can h...
Role of Reference Frames for a Safe Human-Robot Interaction.
Sensors (Basel, Switzerland)
Jun 20, 2023
Safety plays a key role in human-robot interactions in collaborative robot (cobot) applications. This paper provides a general procedure to guarantee safe workstations allowing human operations, robot contributions, the dynamical environment, and tim...
Modified Dynamic Movement Primitives: Robot Trajectory Planning and Force Control Under Curved Surface Constraints.
IEEE transactions on cybernetics
Jun 15, 2023
Dynamic movement primitives (DMPs) have been widely applied in robot motion planning and control. However, in some special cases, original discrete DMP fails to generalize proper trajectories. Moreover, it is difficult to produce trajectories on the ...
Training Novel Adaptive Fuzzy Cognitive Map by Knowledge-Guidance Learning Mechanism for Large-Scale Time-Series Forecasting.
IEEE transactions on cybernetics
Jun 15, 2023
A fuzzy cognitive map (FCM) is a graph-based knowledge representation model wherein the connections of the nodes (edges) represent casual relationships between the knowledge items associated with the nodes. This model has been applied to solve variou...
Learning Performance of Weighted Distributed Learning With Support Vector Machines.
IEEE transactions on cybernetics
Jun 15, 2023
The divide-and-conquer strategy is a very effective method of dealing with big data. Noisy samples in big data usually have a great impact on algorithmic performance. In this article, we introduce Markov sampling and different weights for distributed...
FrMLNet: Framelet-Based Multilevel Network for Pansharpening.
IEEE transactions on cybernetics
Jun 15, 2023
Most modern satellites can provide two types of images: 1) panchromatic (PAN) image and 2) multispectral (MS) image. The former has high spatial resolution and low spectral resolution, while the latter has high spectral resolution and low spatial res...
Fixed-Time Recurrent NN Learning Control of Uncertain Robotic Manipulators with Time-Varying Constraints: Experimental Verification.
Sensors (Basel, Switzerland)
Jun 15, 2023
This paper proposes a learning control framework for the robotic manipulator's dynamic tracking task demanding fixed-time convergence and constrained output. In contrast with model-dependent methods, the proposed solution deals with unknown manipulat...
Transformer-Based Approach Via Contrastive Learning for Zero-Shot Detection.
International journal of neural systems
Jun 14, 2023
Zero-shot detection (ZSD) aims to locate and classify unseen objects in pictures or videos by semantic auxiliary information without additional training examples. Most of the existing ZSD methods are based on two-stage models, which achieve the detec...
Extreme image transformations affect humans and machines differently.
Biological cybernetics
Jun 13, 2023
Some recent artificial neural networks (ANNs) claim to model aspects of primate neural and human performance data. Their success in object recognition is, however, dependent on exploiting low-level features for solving visual tasks in a way that huma...
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