A spiking neural network for active efficient coding by Thomas Barbier, Céline Teulière, Jochen Triesch

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Unsupervised Image Super-Resolution for High-Resolution Satellite Imagery via Omnidirectional Real-to-Synthetic Domain Translation by Minkyung Chung, Yongil Kim

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An end-to-end homography estimation method for large baseline scenes with an attention mechanism by Zuojiang LIN, Xu CAO, Wei CHEN, Yu BAI, Bo MI, Xuewei ZHANG, Hao FANG

Subjects: “…computer image processing; homography estimation; unsupervised learning; attention mechanism; data augmentation; deep learning…”
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A Distribution Agnostic Rank-Based Measure for Proximity Search by Mayur Garg, Ashutosh Nayak, Rajasekhara Reddy Duvvuru Muni

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Unveiling user identity across social media: a novel unsupervised gradient semantic model for accurate and efficient user alignment by Yongqiang Peng, Xiaoliang Chen, Duoqian Miao, Xiaolin Qin, Xu Gu, Peng Lu

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Pansharpening Techniques: Optimizing the Loss Function for Convolutional Neural Networks by Rocco Restaino

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Cluster analysis of social determinants of health and HIV/AIDS knowledge among Peruvian youths using Kohonen’s self-organized maps: a data-exploration study based on a Demographic... by Alejandro Aybar-Flores, Alvaro Talavera, Elizabeth Espinoza-Portilla

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Boosting Depth Estimation for Self-Driving in a Self-Supervised Framework via Improved Pose Network by Yazan Dayoub, Andrey V. Savchenko, Ilya Makarov

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Enhanced vehicle routing for medical waste management via hybrid deep reinforcement learning and optimization algorithms by Norhan Khallaf, Osama Abd-El Rouf, Abeer D. Algarni, Mohy Hadhoud, Ahmed Kafafy

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Application of machine learning in asphalt and concrete material testing: A comprehensive review by Khorshidi Meisam, Dave Eshan, Sias Jo

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A BigBiGAN-Based Loop Closure Detection Algorithm for Indoor Visual SLAM by Qiubo Zhong, Xiaoyi Fang

“…Moreover, the unsupervised learning method does solve restrictions on image quality and singleness semantics in many traditional SLAM methods. …”
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Unsupervised Text Embedding Space Generation Using Generative Adversarial Networks for Text Synthesis by Jun-Min Lee, Tae-Bin Ha

“…Furthermore, TESGAN conducts unsupervised learning which does not directly refer to the text of the training data to overcome the data memorization issue. …”
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Detecting outliers in segmented genomes of flu virus using an alignment-free approach by Mosaab Daoud

“…The approach is implemented using supervised and unsupervised learning modes. The experiments show robustness in detecting outliers of the segmented genome of the flu virus.…”
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Gut microbial subtypes and clinicopathological value for colorectal cancer by Shuwen Han, Jing Zhuang, Yifei Song, Xinyue Wu, Xiaojian Yu, Ye Tao, Jian Chu, Zhanbo Qu, Yinhang Wu, Shugao Han, Xi Yang

“…Methods Stool samples from 914 volunteers (376 CRCs, 363 advanced adenomas, and 175 normal controls) were included for 16S rRNA sequencing. Unsupervised learning was used to generate gut microbial subtypes. …”
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Classification of Agricultural Emissions Among OECD Countries with Unsupervised Techniques by Adam Andrzejuk

“…Both techniques are part of unsupervised learning tasks, which group data into multiple clusters. …”
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EFH:an online unsupervised hash learning algorithm by Zhenyu SHOU, Jiangbo QIAN, Yihong DONG, Huahui CHEN

“…Many unsupervised learning to hash algorithm needs to load all data to memory in the training phase,which will occupy a large memory space and cannot be applied to streaming data.An unsupervised online learning to hash algorithm called evolutionary forest hash (EFH) was proposed.In a large-scale data retrieval scenario,the improved evolution tree can be used to learn the spatial topology of the data.A path coding strategy was proposed to map leaf nodes to similarity-preserved binary code.To further improve the querying performance,ensemble learning was combined,and an online evolving forest hashing method was proposed based on the evolving trees.Finally,the feasibility of this method was proved by experiments on two widely used data sets.…”
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Key technologies and development trends of intelligent customer service for operators by Xiaoliang MA, Ying LIU, Dequan DU, Lingling AN

“…The technology development trend of intelligent customer service for operators was discussed, and automatic speech recognition (ASR) transcription error correction technology, semantic extraction technology, encrypted database, and agent access control technology were introduced.Two different research directions of error detection and automatic error correction for speech recognition were analyzed, two directions of supervised learning and unsupervised learning were discussed, and big data encryption and agent access control technologies were introduced.At the same time, the technical directions of multimodal interaction technology, intelligent recommendation technology and disabled-oriented services were prospected.In conclusion, the development and innovation of intelligent customer service technology will bring a more efficient and convenient service experience to the communication industry and strongly promote the progress of the industry service level.…”
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Clustering Gempabumi di Wilayah Regional VII Menggunakan Pendekatan DBSCAN by Ihsan Bagus Fahad Arafat, Mokhamad Amin Hariyadi, Irwan Budi Santoso, Cahyo Crysdian

“…Pada penelitian ini digunakan pendekatan algoritma Unsupervised Learning Clustering berbasis kepadatan yaitu, Density Based Spatial Clustering of Application with Noise atau DBSCAN, algoritma ini membutuhkan parameter input epsilon (ε) dan MinPts. …”
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Recurrences Reveal Shared Causal Drivers of Complex Time Series by William Gilpin

“…We introduce a physics-based unsupervised learning algorithm that reconstructs causal drivers by iteratively building a recurrence graph with glasslike structure. …”
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A hybrid unsupervised machine learning model with spectral clustering and semi-supervised support vector machine for credit risk assessment. by Tao Yu, Wei Huang, Xin Tang, Duosi Zheng

“…Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performance remains limited owing to challenges such as imbalanced data, local optima, and parameter adjustment complexities. …”
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