Zobrazeno 1 - 10
of 2 802
pro vyhledávání: '"Marchisio P"'
Financial market prediction and optimal trading strategy development remain challenging due to market complexity and volatility. Our research in quantum finance and reinforcement learning for decision-making demonstrates the approach of quantum-class
Externí odkaz:
http://arxiv.org/abs/2408.03088
Autor:
Maouaki, Walid El, Innan, Nouhaila, Marchisio, Alberto, Said, Taoufik, Bennai, Mohamed, Shafique, Muhammad
In this study, we develop a novel quantum machine learning (QML) framework to analyze cybersecurity vulnerabilities using data from the 2022 CISA Known Exploited Vulnerabilities catalog, which includes detailed information on vulnerability types, sev
Externí odkaz:
http://arxiv.org/abs/2408.02314
Portfolio Optimization (PO) is a financial problem aiming to maximize the net gains while minimizing the risks in a given investment portfolio. The novelty of Quantum algorithms lies in their acclaimed potential and capability to solve complex proble
Externí odkaz:
http://arxiv.org/abs/2407.19857
Autonomous embedded systems (e.g., robots) typically necessitate intelligent computation with low power/energy processing for completing their tasks. Such requirements can be fulfilled by embodied neuromorphic intelligence with spiking neural network
Externí odkaz:
http://arxiv.org/abs/2407.05262
Publikováno v:
2024 IEEE International Conference on Image Processing (ICIP)
Recent advancements in quantum computing have led to the emergence of hybrid quantum neural networks, such as Quanvolutional Neural Networks (QuNNs), which integrate quantum and classical layers. While the susceptibility of classical neural networks
Externí odkaz:
http://arxiv.org/abs/2407.03875
Autor:
Marchisio, Kelly, Dash, Saurabh, Chen, Hongyu, Aumiller, Dennis, Üstün, Ahmet, Hooker, Sara, Ruder, Sebastian
Quantization techniques are widely used to improve inference speed and deployment of large language models. While a wide body of work examines the impact of quantization on LLMs in English, none have evaluated across languages. We conduct a thorough
Externí odkaz:
http://arxiv.org/abs/2407.03211
Preference optimization techniques have become a standard final stage for training state-of-art large language models (LLMs). However, despite widespread adoption, the vast majority of work to-date has focused on first-class citizen languages like En
Externí odkaz:
http://arxiv.org/abs/2407.02552
We investigate a surprising limitation of LLMs: their inability to consistently generate text in a user's desired language. We create the Language Confusion Benchmark (LCB) to evaluate such failures, covering 15 typologically diverse languages with e
Externí odkaz:
http://arxiv.org/abs/2406.20052
Autor:
Aryabumi, Viraat, Dang, John, Talupuru, Dwarak, Dash, Saurabh, Cairuz, David, Lin, Hangyu, Venkitesh, Bharat, Smith, Madeline, Campos, Jon Ander, Tan, Yi Chern, Marchisio, Kelly, Bartolo, Max, Ruder, Sebastian, Locatelli, Acyr, Kreutzer, Julia, Frosst, Nick, Gomez, Aidan, Blunsom, Phil, Fadaee, Marzieh, Üstün, Ahmet, Hooker, Sara
This technical report introduces Aya 23, a family of multilingual language models. Aya 23 builds on the recent release of the Aya model (\"Ust\"un et al., 2024), focusing on pairing a highly performant pre-trained model with the recently released Aya
Externí odkaz:
http://arxiv.org/abs/2405.15032
Recent trends have shown that autonomous agents, such as Autonomous Ground Vehicles (AGVs), Unmanned Aerial Vehicles (UAVs), and mobile robots, effectively improve human productivity in solving diverse tasks. However, since these agents are typically
Externí odkaz:
http://arxiv.org/abs/2404.09331