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Found 11 papers, 1 papers with code
Ultimatum game: regret or fairness?
We explain this rejection behavior via the following principle: if the responder regrets less about losing the offer than the proposer regrets not offering the best option, the offer is rejected.
Unsupervised extraction of local and global keywords from a single text
We propose an unsupervised, corpus-independent method to extract keywords from a single text.
Regret theory, Allais' Paradox, and Savage's omelet
We study a sufficiently general regret criterion for choosing between two probabilistic lotteries.
Optimal alphabet for single text compression
We also show that the codebook representation is important -- switching from a naive representation to a compact one significantly improves the matters for alphabets with large number of symbols, most notably the words.
Two halves of a meaningful text are statistically different
The differences reveal a temporal asymmetry in meaningful texts, which is confirmed by showing that texts are much better compressible in their natural way (i. e. along the narrative) than in the word-inverted form.
Active image restoration
Within this regime the strategy is outperformed by its local (adaptive) version, which supervises pixels that do not agree with their Bayesian estimate.
Relating Zipf's law to textual information
In all studied texts we saw that for the first half Zipf's law applies from smaller ranks than in the second half, i. e. the law applies better to the first half.
Stochastic model for phonemes uncovers an author-dependency of their usage
We study rank-frequency relations for phonemes, the minimal units that still relate to linguistic meaning.
Active Inference for Binary Symmetric Hidden Markov Models
We consider active maximum a posteriori (MAP) inference problem for Hidden Markov Models (HMM), where, given an initial MAP estimate of the hidden sequence, we select to label certain states in the sequence to improve the estimation accuracy of the remaining states.
Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs
We present an asymptotic analysis of Viterbi Training (VT) and contrast it with a more conventional Maximum Likelihood (ML) approach to parameter estimation in Hidden Markov Models.
Phase Transitions in Community Detection: A Solvable Toy Model
It predicts a first-order detectability transition whenever $q > 2$, while the finite-temperature cavity method shows that this is the case only when $q > 4$.
