The Mariana Trench is an oceanic trench located in the western Pacific Ocean, about 200 kilometres (124 mi) east of the Mariana Islands; it is the deepest
The aim of this work is to identify main impact factors affecting variations in the geomorphology of the Mariana Trench which is the deepest place of the Earth, located in the west Pacific Ocean: steepness angle and structure of the sediment compress...
Points: 169 | Comments: 126 | Author: bauc
Large deep learning models have shown great potential for delivering exceptional results in various applications. However, the training process can be incredibly challenging due to the models' vast parameter sizes, often consisting of hundreds of bil...
This repositpory entails an implementation of a Deep Learning Pipeline that can be used to evaulate the semantic similarity of two sentenences using Siamese LSTM Neural Network. (⭐ 5)
Insect monitoring is crucial for understanding the consequences of rapid ecological changes, but taxa identification currently requires tedious manual expert work and cannot be scaled-up efficiently. Deep convolutional neural networks (CNNs), provide...
1. Glenn Close (Hook) plays the pirate who gets thrown in the "Boo Box." She's wearing makeup and a fake beard, deepens her voice, and gives no indication that she's a famous (and female) celebrity. ...
Facilitating the design, comparison and sharing of deep text matching models. (⭐ 3856)
oneAPI Deep Neural Network Library (oneDNN) (⭐ 3958)
We present a reinforcement-learning (RL) framework for dynamic hedging of equity index option exposures under realistic transaction costs and position limits. We hedge a normalized option-implied equity exposure (one unit of underlying delta, offset...
We consider a single real scalar field in flat spacetime with a polynomial potential up to $φ^4$, that has a local minimum, the false vacuum, and a deeper global minimum, the true vacuum. When the vacua are almost degenerate we are in the thin wall...
Current cyber-physical systems (CPS) are expected to accomplish complex tasks. To achieve this goal, high performance, but unverified controllers (e.g. deep neural network, black-box controllers from third parties) are applied, which makes it very ch...
The prediction of protein stability changes following single-point mutations plays a pivotal role in computational biology, particularly in areas like drug discovery, enzyme reengineering, and genetic disease analysis. Although deep-learning strategi...
Chemistry for protein structure prediction. As of 2025[update] Jumper serves as director at Google DeepMind. Jumper and his colleagues created AlphaFold, an artificial
program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. It is designed using deep learning techniques. AlphaFold
Recent advances in Deep Neural Networks (DNNs) have led to active development of specialized DNN accelerators, many of which feature a large number of processing elements laid out spatially, together with a multi-level memory hierarchy and flexible i...
Feb 9, 2026 · GitHub Copilot now supports multiple AI models, each optimized for different workflows, from quick edits to deep debugging to multi-step agentic tasks that generate or modify code across …
The machine learning algorithm is gaining prominence in traffic identification research as it offers a way to overcome the shortcomings of port-based and deep packet inspection, especially for P2P-based Skype. However,recent studies have focused main...
It is known that deep neural networks (DNNs) are vulnerable to adversarial attacks. The so-called physical adversarial examples deceive DNN-based decisionmakers by attaching adversarial patches to real objects. However, most of the existing works on...
Objective: Predicting children's future levels of externalizing problems helps to identify children at risk and guide targeted prevention. Existing studies have shown that mothers providing support in response to children's dysregulation was associat...