Top Research Papers on Learning

Compared to traditional machine learning, deep learning can learn deeper abstract data representation and understand scattered data properties. It has gained considerable attention for its extraordinary performances. However, existing deep learning algorithms perform poorly on new tasks. Meta-learning, known as learning to learn, is one of the effective techniques to overcome this issue. Meta-learning’s generalization ability to unknown tasks is improved by employing prior knowledge to assist the learning of new tasks. There are mainly three types of meta-learning methods: metric-based, model-...

A state-of-the-art survey on an intelligent robot with the capability of autonomous deciding and learning reveals that the latest research in deep learning and reinforcement learning has paved the way for highly complex tasks to be performed by robots.

Dysample, an ultra-lightweight and effective dynamic upsampler that requires no customized CUDA package and has much fewer parameters, FLOPs, GPU memory, and latency, outperforms other upsamplers across five dense prediction tasks.

This work introduces MetaICL (Meta-training for In-Context Learning), a new meta-training framework for few-shot learning where a pretrained language model is tuned to do in-context learning on a large set of training tasks.

This work presents a new paradigm for continual learning that aims to train a more succinct memory system without accessing task identity at test time, and achieves competitive results against rehearsal-based methods even without a re-hearsal buffer.

This article provides a conceptual distinction between relevant terms and concepts, explains the process of automated analytical model building through machine learning and deep learning, and discusses the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business.

The findings show LA research was conducted mainly to measure rather than to support SRL, and there is a critical need to exploit the LA support mechanisms further in order to ultimately use them to foster student SRL in online learning environments.

A novel meta-learning method to learn how to learn task-adaptive classifier–predictor to generate classifier weights for few-shot classification that can better capture characteristics of each category in a novel task and thus generate a more accurate and effective classifier.

Organisational Learning (OL) is essential for the survival of an organisation and has led to a significant amount of conceptual and empirical studies. However, no attempt has yet been made to track the overall evolution of OL literature along with the inter-related concepts of learning organisation and organisational learning orientation. Therefore, the present study attempts to fill this gap and track the interdisciplinary flow of knowledge by applying a structural methodology called Systematic Literature Network Analysis (SLNA). The results reveal four main areas of investigation within the ...

Strategies of the self-regulated learning (SRL) framework for K-12 students learning in online environments to support remote learning with online and digital tools during the COVID-19 pandemic are described.

Surprisingly, the best gossip variants perform comparably to the best federated learning variants overall, thus providing a fully decentralized alternative to federatedLearning.

A new method named adversarial domain augmentation is proposed to solve the Out-of-Distribution (OOD) generalization problem by leveraging adversarial training to create "fictitious" yet "challenging" populations, from which a model can learn to generalize with theoretical guarantees.

This work proposes an efficient method for retrieving prompts for in-context learning using annotated data and an LM, and trains an efficient dense retriever from this data, which is used to retrieve training examples as prompts at test time.

MetaLoc is the first fingerprinting-based localization framework that leverages the Model-Agnostic Meta-Learning (MAML), built on a deep neural network with strong representation capabilities, and is trained on historical data sourced from well-calibrated environments, employing a two-loop optimization mechanism to obtain the meta-parameters.

It is concluded that SRL support is beneficial for students' learning but that it should be carefully designed to avoid students’ dissatisfaction, which could potentially nullify these beneficial effects on learning.

The authors have identified new attributes and scaled-down the attributes identified earlier, which would help identify the learner's learning style, and implemented classification algorithms and compared the accuracy of the different algorithms on the dataset.

This review paper discusses personalised and adaptive learning platforms, approaches, and solutions implemented in the prevailing eLearning systems, describing personalisation with basic concepts, describing competency-based learning, customised web service solutions, and presentation approaches.

This article provides a concise but holistic review of the recent advances made in using machine learning to achieve safe decision-making under uncertainties, with a focus on unifying the language and frameworks used in control theory and reinforcement learning research.

This paper is using deep learning to uncover higher-level representational features, to clearly explain transfer learning, to provide current solutions and evaluate applications in diverse areas of transfer learning as well as deep learning.

Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects of reinforcement learning in numerous domains such as robotics and game-playing, transfer learning has arisen to tackle various challenges faced by reinforcement learning, by transferring knowledge from external expertise to facilitate the efficiency and effectiveness of the learning process. In this survey, we systematically investigate the r...