Zobrazeno 1 - 10
of 17 726
pro vyhledávání: '"Catania, A."'
Wildfires pose a significantly increasing hazard to global ecosystems due to the climate crisis. Due to its complex nature, there is an urgent need for innovative approaches to wildfire prediction, such as machine learning. This research took a uniqu
Externí odkaz:
http://arxiv.org/abs/2411.09844
This work analyzes the use of large language models (LLMs) for detecting domain generation algorithms (DGAs). We perform a detailed evaluation of two important techniques: In-Context Learning (ICL) and Supervised Fine-Tuning (SFT), showing how they c
Externí odkaz:
http://arxiv.org/abs/2411.03307
Autor:
Dcruz, Julian Gerald, Mahoney, Sam, Chua, Jia Yun, Soukhabandith, Adoundeth, Mugabe, John, Guo, Weisi, Arana-Catania, Miguel
Autonomous operations of robots in unknown environments are challenging due to the lack of knowledge of the dynamics of the interactions, such as the objects' movability. This work introduces a novel Causal Reinforcement Learning approach to enhancin
Externí odkaz:
http://arxiv.org/abs/2409.13423
Large Language Models (LLMs) have shown remarkable potential across various domains, including cybersecurity. Using commercial cloud-based LLMs may be undesirable due to privacy concerns, costs, and network connectivity constraints. In this paper, we
Externí odkaz:
http://arxiv.org/abs/2409.11276
Pre-trained deep learning embeddings have consistently shown superior performance over handcrafted acoustic features in speech emotion recognition (SER). However, unlike acoustic features with clear physical meaning, these embeddings lack clear inter
Externí odkaz:
http://arxiv.org/abs/2409.09511
Autor:
Catania, Elise
Given a finite poset, Greene introduced a rational function obtained by summing certain rational functions over the linear extensions of the poset. This function has interesting interpretations, and for certain families of posets, it simplifies surpr
Externí odkaz:
http://arxiv.org/abs/2409.04907
Autor:
Platanitis, Konstantinos, Arana-Catania, Miguel, Capicchiano, Leonardo, Upadhyay, Saurabh, Felicetti, Leonard
This paper presents a machine learning approach to estimate the inertial parameters of a spacecraft in cases when those change during operations, e.g. multiple deployments of payloads, unfolding of appendages and booms, propellant consumption as well
Externí odkaz:
http://arxiv.org/abs/2408.03445
Autor:
Nazari, Ali A., Catania, Jeremy, Sadeghian, Soroush, Jalali, Amir, Masnavi, Houman, Janabi-Sharifi, Farrokh, Zareinia, Kourosh
Minimally invasive robotic surgery has gained significant attention over the past two decades. Telerobotic systems, combined with robot-mediated minimally invasive techniques, have enabled surgeons and clinicians to mitigate radiation exposure for me
Externí odkaz:
http://arxiv.org/abs/2407.13162
In this work we demonstrate the possibility of estimating the wind environment of a UAV without specialised sensors, using only the UAV's trajectory, applying a causal machine learning approach. We implement the causal curiosity method which combines
Externí odkaz:
http://arxiv.org/abs/2407.01154
Autor:
Fang, Zheng, Arana-Catania, Miguel, van Lier, Felix-Anselm, Velarde, Juliana Outes, Bregazzi, Harry, Airoldi, Mara, Carter, Eleanor, Procter, Rob
The sheer number of research outputs published every year makes systematic reviewing increasingly time- and resource-intensive. This paper explores the use of machine learning techniques to help navigate the systematic review process. ML has previous
Externí odkaz:
http://arxiv.org/abs/2406.16527