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Publikováno v:
International Journal of Nephrology and Renovascular Disease, Vol Volume 15, Pp 253-266 (2022)
Harshad Chaudhari, Smita Mahendrakar, Stuart E Baskin, Alluru S Reddi Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USACorrespondence: Harshad Chaudhari, Email hc835@njms.rutgers.eduAbstract: The role of contrast-induced neph
Externí odkaz:
https://doaj.org/article/730e5aba4a9e456aad79fe6dd5ad3055
Autor:
Huckelberry, Jacob, Zhang, Yuke, Sansone, Allison, Mickens, James, Beerel, Peter A., Reddi, Vijay Janapa
Tiny Machine Learning (TinyML) systems, which enable machine learning inference on highly resource-constrained devices, are transforming edge computing but encounter unique security challenges. These devices, restricted by RAM and CPU capabilities tw
Externí odkaz:
http://arxiv.org/abs/2411.07114
The intriguing in-context learning (ICL) abilities of deep Transformer models have lately garnered significant attention. By studying in-context linear regression on unimodal Gaussian data, recent empirical and theoretical works have argued that ICL
Externí odkaz:
http://arxiv.org/abs/2410.21698
Autor:
Rawat, Ankit Singh, Sadhanala, Veeranjaneyulu, Rostamizadeh, Afshin, Chakrabarti, Ayan, Jitkrittum, Wittawat, Feinberg, Vladimir, Kim, Seungyeon, Harutyunyan, Hrayr, Saunshi, Nikunj, Nado, Zachary, Shivanna, Rakesh, Reddi, Sashank J., Menon, Aditya Krishna, Anil, Rohan, Kumar, Sanjiv
A primary challenge in large language model (LLM) development is their onerous pre-training cost. Typically, such pre-training involves optimizing a self-supervised objective (such as next-token prediction) over a large corpus. This paper explores a
Externí odkaz:
http://arxiv.org/abs/2410.18779
The remarkable generalization ability of neural networks is usually attributed to the implicit bias of SGD, which often yields models with lower complexity using simpler (e.g. linear) and low-rank features. Recent works have provided empirical and th
Externí odkaz:
http://arxiv.org/abs/2410.16401
Autor:
Jabbour, Jason, Reddi, Vijay Janapa
The integration of Generative Artificial Intelligence (AI) into autonomous machines represents a major paradigm shift in how these systems operate and unlocks new solutions to problems once deemed intractable. Although generative AI agents provide un
Externí odkaz:
http://arxiv.org/abs/2410.15489
Autor:
Tschand, Arya, Rajan, Arun Tejusve Raghunath, Idgunji, Sachin, Ghosh, Anirban, Holleman, Jeremy, Kiraly, Csaba, Ambalkar, Pawan, Borkar, Ritika, Chukka, Ramesh, Cockrell, Trevor, Curtis, Oliver, Fursin, Grigori, Hodak, Miro, Kassa, Hiwot, Lokhmotov, Anton, Miskovic, Dejan, Pan, Yuechao, Manmathan, Manu Prasad, Raymond, Liz, John, Tom St., Suresh, Arjun, Taubitz, Rowan, Zhan, Sean, Wasson, Scott, Kanter, David, Reddi, Vijay Janapa
Rapid adoption of machine learning (ML) technologies has led to a surge in power consumption across diverse systems, from tiny IoT devices to massive datacenter clusters. Benchmarking the energy efficiency of these systems is crucial for optimization
Externí odkaz:
http://arxiv.org/abs/2410.12032
The remarkable capability of Transformers to do reasoning and few-shot learning, without any fine-tuning, is widely conjectured to stem from their ability to implicitly simulate a multi-step algorithms -- such as gradient descent -- with their weight
Externí odkaz:
http://arxiv.org/abs/2410.08292
Autor:
Saunshi, Nikunj, Karp, Stefani, Krishnan, Shankar, Miryoosefi, Sobhan, Reddi, Sashank J., Kumar, Sanjiv
Given the increasing scale of model sizes, novel training strategies like gradual stacking [Gong et al., 2019, Reddi et al., 2023] have garnered interest. Stacking enables efficient training by gradually growing the depth of a model in stages and usi
Externí odkaz:
http://arxiv.org/abs/2409.19044
The end of Moore's Law and Dennard Scaling has combined with advances in agile hardware design to foster a golden age of domain-specific acceleration. However, this new frontier of computing opportunities is not without pitfalls. As computer architec
Externí odkaz:
http://arxiv.org/abs/2407.17311