Zobrazeno 1 - 10
of 421
pro vyhledávání: '"Tanner, Jared"'
Attention layers are the core component of transformers, the current state-of-the-art neural network architecture. However, \softmaxx-based attention puts transformers' trainability at risk. Even \textit{at initialisation}, the propagation of signals
Externí odkaz:
http://arxiv.org/abs/2410.07799
Inducing and leveraging sparse activations during training and inference is a promising avenue for improving the computational efficiency of deep networks, which is increasingly important as network sizes continue to grow and their application become
Externí odkaz:
http://arxiv.org/abs/2402.16184
The infinitely wide neural network has been proven a useful and manageable mathematical model that enables the understanding of many phenomena appearing in deep learning. One example is the convergence of random deep networks to Gaussian processes th
Externí odkaz:
http://arxiv.org/abs/2310.16597
Autor:
Zhang, Yuxin, Zhao, Lirui, Lin, Mingbao, Sun, Yunyun, Yao, Yiwu, Han, Xingjia, Tanner, Jared, Liu, Shiwei, Ji, Rongrong
The ever-increasing large language models (LLMs), though opening a potential path for the upcoming artificial general intelligence, sadly drops a daunting obstacle on the way towards their on-device deployment. As one of the most well-established pre
Externí odkaz:
http://arxiv.org/abs/2310.08915
Autor:
Saada, Thiziri Nait, Tanner, Jared
The edge-of-chaos dynamics of wide randomly initialized low-rank feedforward networks are analyzed. Formulae for the optimal weight and bias variances are extended from the full-rank to low-rank setting and are shown to follow from multiplicative sca
Externí odkaz:
http://arxiv.org/abs/2301.13710
We investigate properties of neural networks that use both ReLU and $x^2$ as activation functions and build upon previous results to show that both analytic functions and functions in Sobolev spaces can be approximated by such networks of constant de
Externí odkaz:
http://arxiv.org/abs/2301.13091
Autor:
Peoples, Jessica1 (AUTHOR), Tanner, Jared J.2 (AUTHOR), Bartley, Emily J.3,4 (AUTHOR), Domenico, Lisa H.1 (AUTHOR), Gonzalez, Cesar E.5 (AUTHOR), Cardoso, Josue S.6 (AUTHOR), Lopez-Quintero, Catalina7 (AUTHOR), Losin, Elizabeth A. Reynolds6 (AUTHOR), Staud, Roland8 (AUTHOR), Goodin, Burel R.9 (AUTHOR), Fillingim, Roger B.3 (AUTHOR), Terry, Ellen L.1,3 (AUTHOR) elterry@ufl.edu
Publikováno v:
BMC Musculoskeletal Disorders. 11/7/2024, Vol. 25 Issue 1, p1-13. 13p.
Autor:
Price, Ilan, Tanner, Jared
The requirement to repeatedly move large feature maps off- and on-chip during inference with convolutional neural networks (CNNs) imposes high costs in terms of both energy and time. In this work we explore an improved method for compressing all feat
Externí odkaz:
http://arxiv.org/abs/2210.15170
Magnetic Resonance Imaging (MRI) has excellent soft tissue contrast but is hindered by an inherently slow data acquisition process. Compressed sensing, which reconstructs sparse signals from incoherently sampled data, has been widely applied to accel
Externí odkaz:
http://arxiv.org/abs/2203.04180
Autor:
Jones, Jolynn, Nielson, Spencer A., Trout, Jonathan, Tanner, Jared J., Bowers, Dawn, Kay, Daniel B.
Publikováno v:
In Heliyon 15 August 2024 10(15)