Zobrazeno 1 - 10
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pro vyhledávání: '"HY SO"'
Hard combinatorial optimization problems, often mapped to Ising models, promise potential solutions with quantum advantage but are constrained by limited qubit counts in near-term devices. We present an innovative quantum-inspired framework that dyna
Externí odkaz:
http://arxiv.org/abs/2412.18571
Visual Language Models have demonstrated remarkable capabilities across tasks, including visual question answering and image captioning. However, most models rely on text-based instructions, limiting their effectiveness in human-machine interactions.
Externí odkaz:
http://arxiv.org/abs/2412.16771
Emotion Recognition in Conversations (ERC) facilitates a deeper understanding of the emotions conveyed by speakers in each utterance within a conversation. Recently, Graph Neural Networks (GNNs) have demonstrated their strengths in capturing data rel
Externí odkaz:
http://arxiv.org/abs/2412.16444
In the burgeoning field of medical imaging, precise computation of 3D volume holds a significant importance for subsequent qualitative analysis of 3D reconstructed objects. Combining multivariate calculus, marching cube algorithm, and binary indexed
Externí odkaz:
http://arxiv.org/abs/2412.10441
Autor:
Soltis, John, Ntampaka, Michelle, Diemer, Benedikt, ZuHone, John, Bose, Sownak, Delgado, Ana Maria, Hadzhiyska, Boryana, Hernandez-Aguayo, Cesar, Nagai, Daisuke, Trac, Hy
The mass accretion rate of galaxy clusters is a key factor in determining their structure, but a reliable observational tracer has yet to be established. We present a state-of-the-art machine learning model for constraining the mass accretion rate of
Externí odkaz:
http://arxiv.org/abs/2412.05370
Autor:
Lam, Hy
In this paper, we establish the spectral decomposition of the Koopman operator and determine the flat-trace distribution associated with the geodesic flow on the co-circle bundle over the compactification of Poincar\'e upper half-plane $\mathbf{H}^2
Externí odkaz:
http://arxiv.org/abs/2411.11392
Latent space optimization (LSO) is a powerful method for designing discrete, high-dimensional biological sequences that maximize expensive black-box functions, such as wet lab experiments. This is accomplished by learning a latent space from availabl
Externí odkaz:
http://arxiv.org/abs/2411.11265
Scene graphs have proven to be highly effective for various scene understanding tasks due to their compact and explicit representation of relational information. However, current methods often overlook the critical importance of preserving symmetry w
Externí odkaz:
http://arxiv.org/abs/2411.10509
Autor:
Hsu, Alan, Ho, Matthew, Lin, Joyce, Markey, Carleen, Ntampaka, Michelle, Trac, Hy, Póczos, Barnabás
We present a novel approach to reconstruct gas and dark matter projected density maps of galaxy clusters using score-based generative modeling. Our diffusion model takes in mock SZ and X-ray images as conditional observations, and generates realizati
Externí odkaz:
http://arxiv.org/abs/2410.02857
Unsupervised pre-training on vast amounts of graph data is critical in real-world applications wherein labeled data is limited, such as molecule properties prediction or materials science. Existing approaches pre-train models for specific graph domai
Externí odkaz:
http://arxiv.org/abs/2409.19117