Zobrazeno 1 - 10
of 370
pro vyhledávání: '"Li, Shigang"'
Disconnectivity and distortion are the two problems which must be coped with when processing 360 degrees equirectangular images. In this paper, we propose a method of estimating the depth of monocular panoramic image with a teacher-student model fusi
Externí odkaz:
http://arxiv.org/abs/2405.16858
Autor:
Wu, Baodong, Xia, Lei, Li, Qingping, Li, Kangyu, Chen, Xu, Guo, Yongqiang, Xiang, Tieyao, Chen, Yuheng, Li, Shigang
Large language models (LLMs) with hundreds of billions or trillions of parameters, represented by chatGPT, have achieved profound impact on various fields. However, training LLMs with super-large-scale parameters requires large high-performance GPU c
Externí odkaz:
http://arxiv.org/abs/2310.10046
Autor:
Blach, Nils, Besta, Maciej, De Sensi, Daniele, Domke, Jens, Harake, Hussein, Li, Shigang, Iff, Patrick, Konieczny, Marek, Lakhotia, Kartik, Kubicek, Ales, Ferrari, Marcel, Petrini, Fabrizio, Hoefler, Torsten
Publikováno v:
Proceedings of the 21st USENIX Symposium on Networked Systems Design and Implementation (NSDI '24) Santa Clara, CA, USA April 16-18, 2024
Novel low-diameter network topologies such as Slim Fly (SF) offer significant cost and power advantages over the established Fat Tree, Clos, or Dragonfly. To spearhead the adoption of low-diameter networks, we design, implement, deploy, and evaluate
Externí odkaz:
http://arxiv.org/abs/2310.03742
Autor:
Jiang, Wenqi, Li, Shigang, Zhu, Yu, Licht, Johannes de Fine, He, Zhenhao, Shi, Runbin, Renggli, Cedric, Zhang, Shuai, Rekatsinas, Theodoros, Hoefler, Torsten, Alonso, Gustavo
Vector search has emerged as the foundation for large-scale information retrieval and machine learning systems, with search engines like Google and Bing processing tens of thousands of queries per second on petabyte-scale document datasets by evaluat
Externí odkaz:
http://arxiv.org/abs/2306.11182
Gradient preconditioning is a key technique to integrate the second-order information into gradients for improving and extending gradient-based learning algorithms. In deep learning, stochasticity, nonconvexity, and high dimensionality lead to a wide
Externí odkaz:
http://arxiv.org/abs/2305.04684
Nowadays, panoramic images can be easily obtained by panoramic cameras. However, when the panoramic camera orientation is tilted, a non-upright panoramic image will be captured. Existing upright adjustment models focus on how to estimate more accurat
Externí odkaz:
http://arxiv.org/abs/2304.05556
Recent advances in deep learning are driven by the growing scale of computation, data, and models. However, efficiently training large-scale models on distributed systems requires an intricate combination of data, operator, and pipeline parallelism,
Externí odkaz:
http://arxiv.org/abs/2301.06813
Pipeline parallelism enables efficient training of Large Language Models (LLMs) on large-scale distributed accelerator clusters. Yet, pipeline bubbles during startup and tear-down reduce the utilization of accelerators. Although efficient pipeline sc
Externí odkaz:
http://arxiv.org/abs/2211.14133
The exponentially growing model size drives the continued success of deep learning, but it brings prohibitive computation and memory cost. From the algorithm perspective, model sparsification and quantization have been studied to alleviate the proble
Externí odkaz:
http://arxiv.org/abs/2209.06979
Autor:
Hoefler, Torsten, Bonato, Tommaso, De Sensi, Daniele, Di Girolamo, Salvatore, Li, Shigang, Heddes, Marco, Belk, Jon, Goel, Deepak, Castro, Miguel, Scott, Steve
Numerous microarchitectural optimizations unlocked tremendous processing power for deep neural networks that in turn fueled the AI revolution. With the exhaustion of such optimizations, the growth of modern AI is now gated by the performance of train
Externí odkaz:
http://arxiv.org/abs/2209.01346