Value investing with machine learning: the South American market by Chen, Ye

“…The primary machine learning method employed in this study is LSTM (Long Short-Term Memory), which performs well with time series data such as financial data of companies. …”
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Learning Bregman distance functions for semi-supervised clustering by Wu, Lei., Hoi, Steven C. H., Jin, Rong., Zhu, Jianke., Yu, Nenghai.

“…We verify the efficacy of the proposed distance learning method with extensive experiments on semi-supervised clustering. …”
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Deep structured output learning for unconstrained text recognition by Jaderberg, M, Simonyan, K, Vedaldi, A, Zisserman, A

“…To test the generalisation of our model, we also perform experiments with random alpha-numeric strings to evaluate the method when no visual language model is applicable.…”

Learning progressive joint propagation for human motion prediction by Cai, Yujun, Huang, Lin, Wang, Yiwei, Cham, Tat-Jen, Cai, Jianfei, Yuan, Junsong, Liu, Jun, Yang, Xu, Zhu, Yiheng, Shen, Xiaohui, Liu, Ding, Liu, Jing, Thalmann, Nadia Magnenat

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A deep learning approach to image quality assessment by Feng, Yeli

“…Lastly, we present a method that helps diagnosis AI to recognize view types of chest radiographs.…”
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Towards haptic intelligence in robots by learning from demonstration by Turlapati Sri Harsha

“…Another aspect of haptic intelligence is in the learning of contact tasks from human demonstrations. …”
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Prediction analysis of COVID-19 in Selangor by using backpropagation algorithm with conjugate gradient method by Ajmal Khan, Noor Amirah, Marjugi, Siti Mahani

“…The Fletcher-Reeves approach can improve the efficiency of the backpropagation algorithm by having a faster convergence rate than other methods such as the scaled conjugate gradient method. …”
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Robust Markov decision processes: a place where AI and formal methods meet by Suilen, M, Badings, T, Bovy, EM, Parker, D, Jansen, N

“…In particular, we discuss RMDP semantics and how to solve them by extending standard MDP methods such as value iteration and policy iteration. …”

A word-embedding-based steganalysis method for linguistic steganography via synonym substitution by Xiang, Lingyun, Yu, Jingmin, Yang, Chunfang, Zeng, Daojian, Shen, Xiaobo

“…In addition, the detection performance can be further improved by learning better word embeddings.…”
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A data-driven method for automated data superposition with applications in soft matter science by Lennon, Kyle R., McKinley, Gareth H., Swan, James W.

“…In this work, we develop a data-driven, nonparametric method for superposing experimental data with arbitrary coordinate transformations, which employs Gaussian process regression to learn statistical models that describe the data, and then uses maximum a posteriori estimation to optimally superpose the data sets. …”
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Comprehensive analysis of feature extraction methods for emotion recognition from multichannel EEG recordings by Yuvaraj, Rajamanickam, Thagavel, Prasanth, Thomas, John, Fogarty, Jack, Ali, Farhan

“…Advances in signal processing and machine learning have expedited electroencephalogram (EEG)-based emotion recognition research, and numerous EEG signal features have been investigated to detect or characterize human emotions. …”
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Quantum computing and quantum neural networks: their foundation, optimisation, and application by Pointing, J

“…Our results suggest a bias-expressivity tradeoff, highlighting the need for alternative frameworks to make QNNs effective general-purpose learning tools for classical data. We explore the optimisation of quantum circuits by creating a new quantum compiler with a novel technique: we automatically generate quantum circuits that are equivalent and design a tiling method to optimise larger circuits according to a customisable cost function. …”

Additive quantization for truly tiny compressed diffusion models by Hasan, Adil

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Open world object detection: a survey by Li, Yiming, Wang, Yi, Wang, Wenqian, Lin, Dan, Li, Bingbing, Yap, Kim-Hui

“…This survey paper offers a thorough review of the OWOD domain, covering essential aspects, including problem definitions, benchmark datasets, source codes, evaluation metrics, and a comparative study of existing methods. Additionally, we investigate related areas like open set recognition (OSR) and incremental learning (IL), underlining their relevance to OWOD. …”
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Generalizability of deep neural networks for vertical cup-to-disc ratio estimation in ultra-widefield and amartphone-based fundus images by Yap, Boon Peng, Li, Kelvin Zhenghao, Toh, En Qi, Low, Kok Yao, Rani, Sumaya Khan, Goh, Eunice Jin Hui, Hui, Vivien Yip Cherng, Ng, Beng Koon, Lim, Tock Han

“…Purpose: To develop and validate a deep learning system (DLS) for estimation of vertical cup-to-disc ratio (vCDR) in ultra-widefield (UWF) and smartphone-based fundus images. …”
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Side channel attack on mobile devices using machine learning by Muhammad Jazeel Meerasah

“…These methods include methods that make use of a smartwatch and other means of attacks. …”
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A survey of few-shot learning for biomedical time series by Li, C, Denison, T, Zhu, T

“…This survey provides a comprehensive review and comparison of few-shot learning methods for biomedical time series applications. …”

Visual place recognition for autonomous robots using deep learning by Huang, Yifeng

“…This distillation method of global feature matching can reduce the difference between the results of the learning model and the teaching model, and can also be used to enhance the generalization ability.…”
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Optimising public transit using big data and machine learning by Lee, Kelvin

“…Despite decades of research on optimisation of public transit, recent advances in big data collection and machine learning methods have created new possibilities for further optimisation. …”
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Applications of deep learning for physical layer of wireless communication systems by Xie, Yihang

“…The proposed positional encoding method for each OFDM subcarrier offsets the deep fading effect, resulting in improved performance compared to traditional MMSE channel estimation with GAMP receivers and state-of-the-art deep learning-based receivers. …”
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