Search Results
Variable Selection with Copula Entropy
It is believed that CE based variable selection can help to build more explainable models.
One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling
We propose a new benchmark corpus to be used for measuring progress in statistical language modeling.
Ranked #22 on
Language Modelling
on One Billion Word
A nonparametric framework for inferring orders of categorical data from category-real ordered pairs
Given a dataset of careers and incomes, how large a difference of income between any pair of careers would be?
Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder
We analyze the histogram of the likelihoods of the input images using the generalized mean, which measures the model's accuracy as a function of the relative risk.
Practical and Consistent Estimation of f-Divergences
The estimation of an f-divergence between two probability distributions based on samples is a fundamental problem in statistics and machine learning.
Inference with selection, varying population size and evolving population structure: Application of ABC to a forward-backward coalescent process with interactions
Second, we develop an Approximate Bayesian Computation framework to use our model for analyzing genetic data.
Populations and Evolution Probability Applications 92D25, 60J80, 92D15, 60J75
Automatic structured variational inference
However, the performance of the variational approach depends on the choice of an appropriate variational family.
Jointly Learning to Align and Translate with Transformer Models
The state of the art in machine translation (MT) is governed by neural approaches, which typically provide superior translation accuracy over statistical approaches.
Learning the dynamics of technical trading strategies
A key contribution is that the overall aggregated trading strategies are tested for statistical arbitrage using a novel hypothesis test proposed by Jarrow et al. (2012) on both daily sampled and intraday time-scales.
Mutual Information Scaling and Expressive Power of Sequence Models
Sequence models assign probabilities to variable-length sequences such as natural language texts.
