Zobrazeno 1 - 10
of 157 564
pro vyhledávání: '"A. D'Emanuele"'
Autor:
Simone, Lorenzo, Miglior, Luca, Gervasi, Vincenzo, Moroni, Luca, Vignali, Emanuele, Gasparotti, Emanuele, Celi, Simona
This research introduces an innovative method for the early screening of cardiorespiratory diseases based on an acquisition protocol, which leverages commodity smartphone's Inertial Measurement Units (IMUs) and deep learning techniques. We collected,
Externí odkaz:
http://arxiv.org/abs/2408.15357
We explore to which extent it is possible to construct efficient classical simulation of quantum many body systems using a combination of tensor network methods and the stabilizer formalism. For this we study both quantum circuit and Hamiltonian dyna
Externí odkaz:
http://arxiv.org/abs/2410.09001
The structure of many real-world datasets is intrinsically hierarchical, making the modeling of such hierarchies a critical objective in both unsupervised and supervised machine learning. Recently, novel approaches for hierarchical clustering with de
Externí odkaz:
http://arxiv.org/abs/2410.07858
Recent theoretical studies have predicted the existence of caustics in many-body quantum dynamics, where they manifest as extended regions of enhanced probability density that obey temporal and spatial scaling relations. Focusing on the transverse-fi
Externí odkaz:
http://arxiv.org/abs/2410.06803
Autor:
Zhou, Fang, Huang, Yaning, Liang, Dong, Li, Dai, Zhang, Zhongke, Wang, Kai, Xin, Xiao, Aboelela, Abdallah, Jiang, Zheliang, Wang, Yang, Song, Jeff, Zhang, Wei, Liang, Chen, Li, Huayu, Sun, ChongLin, Yang, Hang, Qu, Lei, Shu, Zhan, Yuan, Mindi, Maccherani, Emanuele, Hayat, Taha, Guo, John, Puvvada, Varna, Pashkevich, Uladzimir
The increasing complexity of deep learning models used for calculating user representations presents significant challenges, particularly with limited computational resources and strict service-level agreements (SLAs). Previous research efforts have
Externí odkaz:
http://arxiv.org/abs/2410.06497
We study the robustness of parametrized tests of General Relativity (GR) with gravitational waves due to waveform inaccuracy. In particular, we determine the properties of the signal (signal-to-noise ratio (SNR) and source parameters) such that we ar
Externí odkaz:
http://arxiv.org/abs/2410.06254
We identify sufficient conditions to avoid known failure modes, including representation, dimensional, cluster and intracluster collapses, occurring in non-contrastive self-supervised learning. Based on these findings, we propose a principled design
Externí odkaz:
http://arxiv.org/abs/2410.04959
Deep neural networks often learn similar internal representations, both across different models and within their own layers. While inter-network similarities have enabled techniques such as model stitching and merging, intra-network similarities pres
Externí odkaz:
http://arxiv.org/abs/2410.04941
Autor:
He, Sizhuang, Levine, Daniel, Vrkic, Ivan, Bressana, Marco Francesco, Zhang, David, Rizvi, Syed Asad, Zhang, Yangtian, Zappala, Emanuele, van Dijk, David
We introduce CaLMFlow (Causal Language Models for Flow Matching), a novel framework that casts flow matching as a Volterra integral equation (VIE), leveraging the power of large language models (LLMs) for continuous data generation. CaLMFlow enables
Externí odkaz:
http://arxiv.org/abs/2410.05292
Autor:
Zhang, Shiyang, Patel, Aakash, Rizvi, Syed A, Liu, Nianchen, He, Sizhuang, Karbasi, Amin, Zappala, Emanuele, van Dijk, David
We explore the emergence of intelligent behavior in artificial systems by investigating how the complexity of rule-based systems influences the capabilities of models trained to predict these rules. Our study focuses on elementary cellular automata (
Externí odkaz:
http://arxiv.org/abs/2410.02536