Academic Articles & Research Papers

Explainable machine learning in materials science

Abstract Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explaina...

Generating Daily High-Resolution Regional XCO₂ by Deep Neural Network and Multi-Source Data

CO2 is one of the primary greenhouse gases impacting global climate change, making it crucial to understand the spatiotemporal variations of CO2. Currently, commonly used satellites serve as the primary means of CO2 observation, but they of...

Evaluating Uses of Deep Learning Methods for Causal Inference

Logistic regression (LR) is a popular method that is used for estimating causal effects in observational studies using propensity scores. We examine the use of deep learning models such as the deep neural network (DNN), PropensityNet (PN), ...

Discriminating Similar Naval Vessels Using YOLOv8 Deep Neural Network

The next generation of United States Navy uncrewed aerial systems (UASs) is expected to operate in global positioning system and radio frequency-denied maritime environments. In these challenging conditions, these UASs must accurately ident...

Deep Neural Networks for Natural Language Processing

Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During t...

A survey of uncertainty in deep neural networks

Abstract Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Due to the increasing spread, confidence in neural network predictions has become more a...

A Survey on Neural Network Interpretability

Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning systems. It is also related to many ethical problems, e.g.,...

iNNvestigate neural networks!

In recent years, deep neural networks have revolutionized many application domains of machine learning and are key components of many critical decision or predictive processes. Therefore, it is crucial that domain specialists can understand...

Understanding of a convolutional neural network

The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literatur...

Tensorizing Neural Networks

Deep neural networks currently demonstrate state-of-the-art performance in several domains. At the same time, models of this class are very demanding in terms of computational resources. In particular, a large amount of memory is required b...

Intriguing properties of neural networks

Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpr...

On Interpretability of Artificial Neural Networks: A Survey

Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success recently in many important areas that deal with text, images, videos, graphs, and so on. However, the black-box nature of DNNs has become ...

Applied Deep Learning

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks, in this compr...

ℤ₂ × ℤ₂ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks

This paper presents a comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNNs) and Quantum Neural Networks (QNNs), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENNs) and Deep Ne...

Bias and Fairness in Natural Language Processing

Recent advances in data-driven machine learning techniques (e.g., deep neural networks) have revolutionized many natural language processing applications. These approaches automatically learn how to make decisions based on the statistics an...

Comparative Study of CNN and RNN for Natural Language Processing

Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle var...

Adversarial Attacks on Deep-learning Models in Natural Language Processing

With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs are vulne...

Context-Aware Bidirectional Neural Model for Sindhi Named Entity Recognition

Named entity recognition (NER) is a fundamental task in many natural language processing (NLP) applications, such as text summarization and semantic information retrieval. Recently, deep neural networks (NNs) with the attention mechanism yi...

Evaluation system of college english teaching quality based on fuzzy information of artificial intelligence

Abstract The current evaluation of English teaching quality in colleges and universities faces the problems of information uncertainty and fuzziness. Traditional evaluation methods cannot accurately reflect the complex teaching effects, mai...