4 code implementations • 8 Apr 2024 • Bo Peng, Daniel Goldstein, Quentin Anthony, Alon Albalak, Eric Alcaide, Stella Biderman, Eugene Cheah, Xingjian Du, Teddy Ferdinan, Haowen Hou, Przemysław Kazienko, Kranthi Kiran GV, Jan Kocoń, Bartłomiej Koptyra, Satyapriya Krishna, Ronald McClelland Jr., Niklas Muennighoff, Fares Obeid, Atsushi Saito, Guangyu Song, Haoqin Tu, Stanisław Woźniak, Ruichong Zhang, Bingchen Zhao, Qihang Zhao, Peng Zhou, Jian Zhu, Rui-Jie Zhu
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture.
no code implementations • 23 Jan 2021 • Shuhang Wang, Vivek Kumar Singh, Alex Benjamin, Mercy Asiedu, Elham Yousef Kalafi, Eugene Cheah, Viksit Kumar, Anthony Samir
The salient features of our algorithm include: 1)no need for original training data or generative networks, 2) knowledge transfer between different architectures, 3) ease of implementation for downstream tasks by using the downstream task dataset as the transferal dataset, 4) knowledge transfer of an ensemble of models, trained independently, into one student model.
no code implementations • 1 Jan 2021 • Shuhang Wang, Eugene Cheah, Elham Yousef Kalafi, Mercy Asiedu, Alex Benjamin, Vivek Kumar Singh, Ge Zhang, Viksit Kumar, Anthony Edward Samir
Transfer learning often employs all or part of the weights of a pre-trained net-work to the problem at hand; this limits the flexibility of new neural architectures.
no code implementations • 7 Apr 2020 • Shuhang Wang, Szu-Yeu Hu, Eugene Cheah, XiaoHong Wang, JingChao Wang, Lei Chen, Masoud Baikpour, Arinc Ozturk, Qian Li, Shinn-Huey Chou, Constance D. Lehman, Viksit Kumar, Anthony Samir
This paper proposes a novel U-Net variant using stacked dilated convolutions for medical image segmentation (SDU-Net).