Existence of spam URLs over emails and Online Social Media (OSM) has become a massive e-crime. To counter the dissemination of long complex URLs in emails and character limit imposed on various OSM (like Twitter), the concept of URL shortening has ga...
Malicious URL classification represents a crucial aspect of cyber security. Although existing work comprises numerous machine learning and deep learning-based URL classification models, most suffer from generalisation and domain-adaptation issues ari...
Malicious URLs host unsolicited content and are used to perpetrate cybercrimes. It is imperative to detect them in a timely manner. Traditionally, this is done through the usage of blacklists, which cannot be exhaustive, and cannot detect newly gener...
Deep metric learning using Triplet network (⭐ 187)
In this paper, we aim to learn a mapping (or embedding) from images to a compact binary space in which Hamming distances correspond to a ranking measure for the image retrieval task. We make use of a triplet loss because this has been shown to be m...
In recent years, deep learning has spread rapidly, and deeper, larger models have been proposed. However, the calculation cost becomes enormous as the size of the models becomes larger. Various techniques for compressing the size of the models have b...
full essay: https://arxiv.org/abs/2511.08892 at first glance I thought it was average slop, but I look deeper and find it quite interesting. they used about 1000 play records of Genshin Mondstadt c...
Points: 4 | Comments: 1 | Author: daveambrose
Chopra studied medicine in India before emigrating in 1970 to the United States, where he completed a residency in internal medicine and a fellowship in endocrinology. As a licensed physician, in 1980, he …
Deep learning techniques have enabled the emergence of state-of-the-art models to address object detection tasks. However, these techniques are data-driven, delegating the accuracy to the training dataset which must resemble the images in the target...
Futures are the ideal instrument for day trading, particularly stock indices or the most heavily traded commodities (maybe skip metals right now if you're not deeply familiar with them because their v...
In Cherry Tale, Little Red Riding Hood brandishes her sawblade and yells about cooking and eating the Big Bad Wolf. Snow White may look innocent and pure on the outside, but deep down she is a …
Although residual connection enables training very deep neural networks, it is not friendly for online inference due to its multi-branch topology. This encourages many researchers to work on designing DNNs without residual connections at inference. F...
Intel's Deep Learning Boost (DL Boost) is a marketing name for instruction set architecture (ISA) features on the x86-64 designed to improve performance
Reducing false positives is essential for enhancing object detector performance, as reflected in the mean Average Precision (mAP) metric. Although object detectors have achieved notable improvements and high mAP scores on the COCO dataset, analysis r...
This study explores object detection in historical aerial photographs of Namibia to identify long-term environmental changes. Specifically, we aim to identify key objects -- Waterholes, Omuti homesteads, and Big trees -- around Oshikango in Namibia u...
Hosting about 50 million songs and 4 billion playlists, there is an enormous amount of data generated at Spotify every single day - upwards of 600 gigabytes of data (harvard.edu). Since the algorithms that Spotify uses in recommendation systems is pr...
We present a set of low resolution empirical SED templates for AGNs and galaxies in the wavelength range from 0.03 to 30 microns based on the multi-wavelength photometric observations of the NOAO Deep-Wide Field Survey Bootes field and the spectros...
Deformable templates are essential to large-scale medical image registration, segmentation, and population analysis. Current conventional and deep network-based methods for template construction use only regularized registration objectives and often...
UCL Module: Applied Deep Learning (⭐ 79)