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pro vyhledávání: '"Benaim, A."'
We consider a population spreading across a finite number of sites. Individuals can move from one site to the other according to a network (oriented links between the sites) that vary periodically over time. On each site, the population experiences a
Externí odkaz:
http://arxiv.org/abs/2411.07821
Autor:
Galun, Ran, Benaim, Sagie
Text-to-image diffusion models have demonstrated an impressive ability to produce high-quality outputs. However, they often struggle to accurately follow fine-grained spatial information in an input text. To this end, we propose a compositional appro
Externí odkaz:
http://arxiv.org/abs/2410.09792
This paper is a follow-up to a previous work where we considered populations with time-varying growth rates living in patches and irreducible migration matrix between the patches. Each population, when isolated, would become extinct. Dispersal-induce
Externí odkaz:
http://arxiv.org/abs/2407.07553
Autor:
Benaim, Michel, Bichard, Améthyste
In a paper entitled singularities of invariant densities for random switching between two linear odes in 2D, Bakhtin et al [5], consider a Markov process obtained by random switching between two stable linear vector fields in the plane and characteri
Externí odkaz:
http://arxiv.org/abs/2406.17891
Commonsense reasoning is fundamentally based on multimodal knowledge. However, existing large language models (LLMs) are primarily trained using textual data only, limiting their ability to incorporate essential visual information. In contrast, Visua
Externí odkaz:
http://arxiv.org/abs/2406.13621
Autor:
Loeschcke, Sebastian, Wang, Dan, Leth-Espensen, Christian, Belongie, Serge, Kastoryano, Michael J., Benaim, Sagie
The ability to learn compact, high-quality, and easy-to-optimize representations for visual data is paramount to many applications such as novel view synthesis and 3D reconstruction. Recent work has shown substantial success in using tensor networks
Externí odkaz:
http://arxiv.org/abs/2406.04332
We tackle the task of learning dynamic 3D semantic radiance fields given a single monocular video as input. Our learned semantic radiance field captures per-point semantics as well as color and geometric properties for a dynamic 3D scene, enabling th
Externí odkaz:
http://arxiv.org/abs/2405.19321
Autor:
Benaïm, Michel, Miclo, Laurent
Let $U$ be a Morse function on a compact connected $m$-dimensional Riemannian manifold, $m \geq 2,$ satisfying $\min U=0$ and let $\mathcal{U} = \{x \in M \: : U(x) = 0\}$ be the set of global minimizers. Consider the stochastic algorithm $X^{(\beta)
Externí odkaz:
http://arxiv.org/abs/2401.12605
Publikováno v:
Journal of Mathematical Biology (2024) 88:19
We consider populations with time-varying growth rates living in sinks. Each population, when isolated, would become extinct. Dispersal-induced growth (DIG) occurs when the populations are able to persist and grow exponentially when dispersal among t
Externí odkaz:
http://arxiv.org/abs/2311.04706
Autor:
Luo, Katie Z, Liu, Zhenzhen, Chen, Xiangyu, You, Yurong, Benaim, Sagie, Phoo, Cheng Perng, Campbell, Mark, Sun, Wen, Hariharan, Bharath, Weinberger, Kilian Q.
Recent advances in machine learning have shown that Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences. Although very successful for Large Language Models (LLMs), these advancem
Externí odkaz:
http://arxiv.org/abs/2310.19080