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pro vyhledávání: '"Freifeld, A."'
We show that the flip chain for non-crossing spanning trees of $n+1$ points in convex position mixes in time $O(n^8\log n)$.
Comment: 19 pages, 6 figures
Comment: 19 pages, 6 figures
Externí odkaz:
http://arxiv.org/abs/2409.07892
The unsupervised task of Joint Alignment (JA) of images is beset by challenges such as high complexity, geometric distortions, and convergence to poor local or even global optima. Although Vision Transformers (ViT) have recently provided valuable fea
Externí odkaz:
http://arxiv.org/abs/2407.11850
Nonlinear activation functions are pivotal to the success of deep neural nets, and choosing the appropriate activation function can significantly affect their performance. Most networks use fixed activation functions (e.g., ReLU, GELU, etc.), and thi
Externí odkaz:
http://arxiv.org/abs/2407.07564
In recent years, there have been attempts to increase the kernel size of Convolutional Neural Nets (CNNs) to mimic the global receptive field of Vision Transformers' (ViTs) self-attention blocks. That approach, however, quickly hit an upper bound and
Externí odkaz:
http://arxiv.org/abs/2407.05848
We present two randomised approximate counting algorithms with $\widetilde{O}(n^{2-c}/\varepsilon^2)$ running time for some constant $c>0$ and accuracy $\varepsilon$: (1) for the hard-core model with fugacity $\lambda$ on graphs with maximum degree $
Externí odkaz:
http://arxiv.org/abs/2306.14867
In video analysis, background models have many applications such as background/foreground separation, change detection, anomaly detection, tracking, and more. However, while learning such a model in a video captured by a static camera is a fairly-sol
Externí odkaz:
http://arxiv.org/abs/2209.07923
Autor:
Hoffman Azik, Nativ Omri, Malshy Kamil, Haifler Miki, Golan Shay, Mano Roy, Freifeld Yuval, Rosenzweig Barak, Shalom Ben, Stabholz Yariv, Ben-David Reuven, Amiel E. Gilad
Publikováno v:
Discover Oncology, Vol 15, Iss 1, Pp 1-7 (2024)
Abstract Introduction Bilateral testicular germ cell tumor (BGCT) is a rare disease, occasionally considered to be more aggressive than unilateral germ cell tumors (GCT) in some reports. Among BGCT, a synchronous disease might be diagnosed at a highe
Externí odkaz:
https://doaj.org/article/a198d971a9ae474897ba9e419ebf1577
In the realm of unsupervised learning, Bayesian nonparametric mixture models, exemplified by the Dirichlet Process Mixture Model (DPMM), provide a principled approach for adapting the complexity of the model to the data. Such models are particularly
Externí odkaz:
http://arxiv.org/abs/2204.08988
Deep Learning (DL) has shown great promise in the unsupervised task of clustering. That said, while in classical (i.e., non-deep) clustering the benefits of the nonparametric approach are well known, most deep-clustering methods are parametric: namel
Externí odkaz:
http://arxiv.org/abs/2203.14309
The Dirichlet Process Gaussian Mixture Model (DPGMM) is often used to cluster data when the number of clusters is unknown. One main DPGMM inference paradigm relies on sampling. Here we consider a known state-of-art sampler (proposed by Chang and Fish
Externí odkaz:
http://arxiv.org/abs/2203.13661