TU4.R1.4

Dense KO Codes: Faster Convergence and Reduced Complexity through Dense Connectivity

Shubham Srivastava, Adrish Banerjee, Indian Institute of Technology Kanpur, India
Session:
Deep Learning in Coding
Track:
8: Deep Learning (such as understanding large language models)
Location:
Ballroom II & III
Presentation Time:
Tue, 9 Jul, 17:05 - 17:25
Session Chair:
Natasha Devroy, University of Illinois Chicago
Presentation
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Discussion
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Session TU4.R1
TU4.R1.1: Section-wise Revolving NBP-like Decoders for QC-LDPC Codes
Qinshan Zhang, Tsinghua University, China; Bin Chen, Harbin Institute of Technology (Shenzhen), China; Tianqu Zhuang, Yong Jiang, Shu-Tao Xia, Tsinghua University, China
TU4.R1.2: Interpreting Deepcode, a learned feedback code
Yingyao Zhou, Natasha Devroye, Gyorgy Turan, Milos Zefran, University of Illinois at Chicago, United States
TU4.R1.3: Nested Construction of Polar Codes via Transformers
Sravan Ankireddy, University of Texas at Austin, United States; Ashwin Hebbar, Princeton University, United States; Heping Wan, Joonyoung Cho, Charlie Zhang, Samsung Research America, United States
TU4.R1.4: Dense KO Codes: Faster Convergence and Reduced Complexity through Dense Connectivity
Shubham Srivastava, Adrish Banerjee, Indian Institute of Technology Kanpur, India
Resources
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