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pro vyhledávání: '"Freifeld A"'
In underwater images, most useful features are occluded by water. The extent of the occlusion depends on imaging geometry and can vary even across a sequence of burst images. As a result, 3D reconstruction methods robust on in-air scenes, like Neural
Externí odkaz:
http://arxiv.org/abs/2411.19588
Autor:
Barash, Danny, Manning, Emilie, Van Vleck, Aidan, Hirsch, Omri, Aye, Kyi Lei, Li, Jingxi, Scumpia, Philip O., Ozcan, Aydogan, Aasi, Sumaira, Rieger, Kerri E., Sarin, Kavita Y., Freifeld, Oren, Winetraub, Yonatan
Noninvasive optical imaging modalities can probe patient's tissue in 3D and over time generate gigabytes of clinically relevant data per sample. There is a need for AI models to analyze this data and assist clinical workflow. The lack of expert label
Externí odkaz:
http://arxiv.org/abs/2411.11613
Autor:
Behzadi, Shakila, Ho, Jacquelin, Tanvir, Zainab, Haspel, Gal, Freifeld, Limor, Severi, Kristen E.
Expansion Microscopy is a super-resolution technique in which physically enlarging samples in an isotropic manner increases inter-molecular distances such that nano-scale structures can be resolved using light microscopy. This is particularly useful
Externí odkaz:
http://arxiv.org/abs/2411.06676
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
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