The mathematical theory of communication

By Claude E. Shannon. 1950

Subfields: Information Theory

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Theory

1. Daniel Polani
2. Merouane Debbah
3. M. Gerhardt
4. Yannis Bilias
5. L. Zanna
6. C. Sudtongkong
7. K. Kock
8. A. Galindo

Citations in the corpus

• A note on the evaluation of generative models (2015, methodology): “The negative of this log-likelihood is equivalent to the average number of bits (assuming base-2 logarithm) required to losslessly compress the discrete data with an entropy coding scheme optimized for Q (Shannon, 2001).”
• Unsupervised Semantic Parsing of Video Collections (2015, methodology): “g of evaluation videos as well as the ground truth parsing. To visualize the content of each activity step, we display key-frames from different videos. We also train a 3rdorder Markov language model [53] using the subtitles and employ it to generate a caption for each activity step by sampling this”
• A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (2020, methodology): “We also tried exemplar selection techniques inspired from active learning approaches such as: entropy [65], min margin [64], coreset [63], k-means [81].”
• Simplicity versus likelihood in visual perception: from surprisals to precisals. (2000, background): “Then, I sketch how this concept led to Shannons (1948)Noiseless Coding Theorem (see also Shannon & Weaver, 1949), which, in the fourth part, turns out to be relevant to the visual short-term memory.”
• Information transfer rate in a five-classes brain-computer interface (2001, methodology): “The information transfer rate (derived from [ 10 ]) in bits per trial was calculated by”
• Translation techniques in cross-language information retrieval (2012, methodology): “12This formula is based on Shannon’s noisy-channel coding theorem [Shannon and Weaver 1963].”