Zobrazeno 1 - 10
of 15 792
pro vyhledávání: '"A. Omri"'
Autor:
Xiang, Xiaoyu, Gorelik, Liat Sless, Fan, Yuchen, Armstrong, Omri, Iandola, Forrest, Li, Yilei, Lifshitz, Ita, Ranjan, Rakesh
We present Make-A-Texture, a new framework that efficiently synthesizes high-resolution texture maps from textual prompts for given 3D geometries. Our approach progressively generates textures that are consistent across multiple viewpoints with a dep
Externí odkaz:
http://arxiv.org/abs/2412.07766
Autor:
Bercovich, Akhiad, Ronen, Tomer, Abramovich, Talor, Ailon, Nir, Assaf, Nave, Dabbah, Mohammad, Galil, Ido, Geifman, Amnon, Geifman, Yonatan, Golan, Izhak, Haber, Netanel, Karpas, Ehud, Koren, Roi, Levy, Itay, Molchanov, Pavlo, Mor, Shahar, Moshe, Zach, Nabwani, Najeeb, Puny, Omri, Rubin, Ran, Schen, Itamar, Shahaf, Ido, Tropp, Oren, Argov, Omer Ullman, Zilberstein, Ran, El-Yaniv, Ran
Large language models (LLMs) have demonstrated remarkable capabilities, but their adoption is limited by high computational costs during inference. While increasing parameter counts enhances accuracy, it also widens the gap between state-of-the-art c
Externí odkaz:
http://arxiv.org/abs/2411.19146
Vision-Language Models (VLMs) have recently demonstrated remarkable capabilities in comprehending complex visual content. However, the mechanisms underlying how VLMs process visual information remain largely unexplored. In this paper, we conduct a th
Externí odkaz:
http://arxiv.org/abs/2411.17491
Time series forecasting is critical in numerous real-world applications, requiring accurate predictions of future values based on observed patterns. While traditional forecasting techniques work well in in-domain scenarios with ample data, they strug
Externí odkaz:
http://arxiv.org/abs/2411.15743
We present a nearly linear work parallel algorithm for approximating the Held-Karp bound for the Metric TSP problem. Given an edge-weighted undirected graph $G=(V,E)$ on $m$ edges and $\epsilon>0$, it returns a $(1+\epsilon)$-approximation to the Hel
Externí odkaz:
http://arxiv.org/abs/2411.14745
Autor:
Avrahami, Omri, Patashnik, Or, Fried, Ohad, Nemchinov, Egor, Aberman, Kfir, Lischinski, Dani, Cohen-Or, Daniel
Diffusion models have revolutionized the field of content synthesis and editing. Recent models have replaced the traditional UNet architecture with the Diffusion Transformer (DiT), and employed flow-matching for improved training and sampling. Howeve
Externí odkaz:
http://arxiv.org/abs/2411.14430
We prove direct-sum theorems for Wilber's two lower bounds [Wilber, FOCS'86] on the cost of access sequences in the binary search tree (BST) model. These bounds are central to the question of dynamic optimality [Sleator and Tarjan, JACM'85]: the Alte
Externí odkaz:
http://arxiv.org/abs/2411.14387
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:
Piasotski, Kiryl, Lesser, Omri, Reich, Adrian, Ostrovsky, Pavel, Grosfeld, Eytan, Makhlin, Yuriy, Oreg, Yuval, Shnirman, Alexander
We discuss the Josephson vortices in planar superconductor-topological insulator-superconductor (S-TI-S) junctions, where the TI section is narrow and long. We are motivated by recent experiments, especially by those in junctions of Corbino ring geom
Externí odkaz:
http://arxiv.org/abs/2411.10335
Autor:
Gerhardt, Deborah, Marcowitz-Bitton, Miriam, Schuster, W. Michael, Elmalech, Avshalom, Suissa, Omri, Mash, Moshe
This study examines gender disparities in patent law by analyzing the textual content of patent applications. While prior research has primarily focused on the study of metadata (i.e., filing year or technological class), we employ machine learning a
Externí odkaz:
http://arxiv.org/abs/2411.08526