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Found 5 papers, 1 papers with code
Successive Refinement in Large-Scale Computation: Advancing Model Inference Applications
This innovation notably enhances the deadline-based systems, as if a computational job is terminated due to time constraints, an approximate version of the final result can still be generated.
TexShape: Information Theoretic Sentence Embedding for Language Models
With the exponential growth in data volume and the emergence of data-intensive applications, particularly in the field of machine learning, concerns related to resource utilization, privacy, and fairness have become paramount.
Analog Compressed Sensing for Sparse Frequency Shift Keying Modulation Schemes
Our results show that using a compressed sensing receiver allows for the simplification of the analog receiver with the trade off of a slight degradation in recovery performance.
NeuraCrypt: Hiding Private Health Data via Random Neural Networks for Public Training
We propose to approximate this family of encoding functions through random deep neural networks.
Network Maximal Correlation
For jointly Gaussian variables, we show that under some conditions the NMC optimization is an instance of the Max-Cut problem.
