Zobrazeno 1 - 10
of 6 894
pro vyhledávání: '"A. A. Molchanov"'
Publikováno v:
Вестник хирургии имени И.И. Грекова, Vol 180, Iss 5, Pp 111-117 (2022)
The article presents a literature review of studies on the use of local destruction methods (photodynamic therapy and radiofrequency ablation) in the treatment of patients with unresectable extrahepatic cholangiocarcinoma. Currently, many studies hav
Externí odkaz:
https://doaj.org/article/d6fe788cb92f4e6697dfbfd88671b55b
Autor:
Margarint, Vlad, Molchanov, Stanislav
The first step in the formulation and study of the Riemann Hypothesis is the analytic continuation of the Riemann Zeta Function (RZF) in the full Complex Plane with a pole at $s=1$. In the current work, we study the analytic continuation of two rando
Externí odkaz:
http://arxiv.org/abs/2410.03044
Autor:
Ranzinger, Mike, Barker, Jon, Heinrich, Greg, Molchanov, Pavlo, Catanzaro, Bryan, Tao, Andrew
Various visual foundation models have distinct strengths and weaknesses, both of which can be improved through heterogeneous multi-teacher knowledge distillation without labels, termed "agglomerative models." We build upon this body of work by studyi
Externí odkaz:
http://arxiv.org/abs/2410.01680
Autor:
Fang, Gongfan, Yin, Hongxu, Muralidharan, Saurav, Heinrich, Greg, Pool, Jeff, Kautz, Jan, Molchanov, Pavlo, Wang, Xinchao
Large Language Models (LLMs) are distinguished by their massive parameter counts, which typically result in significant redundancy. This work introduces MaskLLM, a learnable pruning method that establishes Semi-structured (or ``N:M'') Sparsity in LLM
Externí odkaz:
http://arxiv.org/abs/2409.17481
Autor:
Li, Jiefeng, Yuan, Ye, Rempe, Davis, Zhang, Haotian, Molchanov, Pavlo, Lu, Cewu, Kautz, Jan, Iqbal, Umar
Estimating global human motion from moving cameras is challenging due to the entanglement of human and camera motions. To mitigate the ambiguity, existing methods leverage learned human motion priors, which however often result in oversmoothed motion
Externí odkaz:
http://arxiv.org/abs/2408.16426
Autor:
Sreenivas, Sharath Turuvekere, Muralidharan, Saurav, Joshi, Raviraj, Chochowski, Marcin, Patwary, Mostofa, Shoeybi, Mohammad, Catanzaro, Bryan, Kautz, Jan, Molchanov, Pavlo
We present a comprehensive report on compressing the Llama 3.1 8B and Mistral NeMo 12B models to 4B and 8B parameters, respectively, using pruning and distillation. We explore two distinct pruning strategies: (1) depth pruning and (2) joint hidden/at
Externí odkaz:
http://arxiv.org/abs/2408.11796
Autor:
Xue, Fuzhao, Chen, Yukang, Li, Dacheng, Hu, Qinghao, Zhu, Ligeng, Li, Xiuyu, Fang, Yunhao, Tang, Haotian, Yang, Shang, Liu, Zhijian, He, Ethan, Yin, Hongxu, Molchanov, Pavlo, Kautz, Jan, Fan, Linxi, Zhu, Yuke, Lu, Yao, Han, Song
Long-context capability is critical for multi-modal foundation models, especially for long video understanding. We introduce LongVILA, a full-stack solution for long-context visual-language models by co-designing the algorithm and system. For model t
Externí odkaz:
http://arxiv.org/abs/2408.10188
Scatterplots provide a visual representation of bivariate data (or 2D embeddings of multivariate data) that allows for effective analyses of data dependencies, clusters, trends, and outliers. Unfortunately, classical scatterplots suffer from scalabil
Externí odkaz:
http://arxiv.org/abs/2408.06513
Autor:
Fang, Yunhao, Zhu, Ligeng, Lu, Yao, Wang, Yan, Molchanov, Pavlo, Cho, Jang Hyun, Pavone, Marco, Han, Song, Yin, Hongxu
Visual language models (VLMs) have rapidly progressed, driven by the success of large language models (LLMs). While model architectures and training infrastructures advance rapidly, data curation remains under-explored. When data quantity and quality
Externí odkaz:
http://arxiv.org/abs/2407.17453
Autor:
Siddiqui, Shoaib Ahmed, Dong, Xin, Heinrich, Greg, Breuel, Thomas, Kautz, Jan, Krueger, David, Molchanov, Pavlo
Large Language Models (LLMs) are not only resource-intensive to train but even more costly to deploy in production. Therefore, recent work has attempted to prune blocks of LLMs based on cheap proxies for estimating block importance, effectively remov
Externí odkaz:
http://arxiv.org/abs/2407.16286