Zobrazeno 1 - 10
of 36
pro vyhledávání: '"Agneeswaran, Vijay"'
Sequence modeling is a crucial area across various domains, including Natural Language Processing (NLP), speech recognition, time series forecasting, music generation, and bioinformatics. Recurrent Neural Networks (RNNs) and Long Short Term Memory Ne
Externí odkaz:
http://arxiv.org/abs/2404.16112
Azure Core workload insights have time-series data with different metric units. Faults or Anomalies are observed in these time-series data owing to faults observed with respect to metric name, resources region, dimensions, and its dimension value ass
Externí odkaz:
http://arxiv.org/abs/2404.09302
Transformers have revolutionized image modeling tasks with adaptations like DeIT, Swin, SVT, Biformer, STVit, and FDVIT. However, these models often face challenges with inductive bias and high quadratic complexity, making them less efficient for hig
Externí odkaz:
http://arxiv.org/abs/2403.18063
Autor:
Patro, Badri N., Agneeswaran, Vijay S.
Transformers have widely adopted attention networks for sequence mixing and MLPs for channel mixing, playing a pivotal role in achieving breakthroughs across domains. However, recent literature highlights issues with attention networks, including low
Externí odkaz:
http://arxiv.org/abs/2403.15360
Vision transformers have gained significant attention and achieved state-of-the-art performance in various computer vision tasks, including image classification, instance segmentation, and object detection. However, challenges remain in addressing at
Externí odkaz:
http://arxiv.org/abs/2311.01310
Vision transformers have been applied successfully for image recognition tasks. There have been either multi-headed self-attention based (ViT \cite{dosovitskiy2020image}, DeIT, \cite{touvron2021training}) similar to the original work in textual model
Externí odkaz:
http://arxiv.org/abs/2304.06446
Transformers are widely used for solving tasks in natural language processing, computer vision, speech, and music domains. In this paper, we talk about the efficiency of transformers in terms of memory (the number of parameters), computation cost (nu
Externí odkaz:
http://arxiv.org/abs/2302.08374
Autor:
Choudhary, Vishwas, Gupta, Binay, Chatterjee, Anirban, Paul, Subhadip, Banerjee, Kunal, Agneeswaran, Vijay
Missing values, widely called as \textit{sparsity} in literature, is a common characteristic of many real-world datasets. Many imputation methods have been proposed to address this problem of data incompleteness or sparsity. However, the accuracy of
Externí odkaz:
http://arxiv.org/abs/2207.13287
Autor:
Gupta, Binay, Chatterjee, Anirban, Matha, Harika, Banerjee, Kunal, Parsai, Lalitdutt, Agneeswaran, Vijay
Reducing the number of failures in a production system is one of the most challenging problems in technology driven industries, such as, the online retail industry. To address this challenge, change management has emerged as a promising sub-field in
Externí odkaz:
http://arxiv.org/abs/2108.07951
Autor:
Srinivas, Agneeswaran Vijay
Publikováno v:
Communications of the ACM. May2008, Vol. 51 Issue 5, p11-11. 1/3p.
