Search Results for author:
Found 7 papers, 3 papers with code
Exploring Explainability in Video Action Recognition
To address these, we introduce Video-TCAV, by building on TCAV for Image Classification tasks, which aims to quantify the importance of specific concepts in the decision-making process of Video Action Recognition models.
LIGHTCODE: Light Analytical and Neural Codes for Channels with Feedback
First, we demonstrate that POWERBLAST, an analytical coding scheme inspired by Schalkwijk-Kailath (SK) and Gallager-Nakiboglu (GN) schemes, achieves notable reliability improvements over both SK and GN schemes, outperforming neural codes in high signal-to-noise ratio (SNR) regions.
DeepPolar: Inventing Nonlinear Large-Kernel Polar Codes via Deep Learning
Polar codes, developed on the foundation of Arikan's polarization kernel, represent a breakthrough in coding theory and have emerged as the state-of-the-art error-correction-code in short-to-medium block length regimes.
Nested Construction of Polar Codes via Transformers
Tailoring polar code construction for decoding algorithms beyond successive cancellation has remained a topic of significant interest in the field.
Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy Approach
In this work, we consider the problem of network parameter optimization for rate maximization.
TinyTurbo: Efficient Turbo Decoders on Edge
In this paper, we introduce a neural-augmented decoder for Turbo codes called TINYTURBO .
Interpreting Neural Min-Sum Decoders
In decoding linear block codes, it was shown that noticeable reliability gains can be achieved by introducing learnable parameters to the Belief Propagation (BP) decoder.
