Attention-based Random Forest and Contamination Model

TL;DR: In this paper , a generalized stochastic dominance (GSD) order is proposed to exploit the entire information encoded in the non-standard spaces of multidimensional data, based on the sets of expectations of random variables mapping into such spaces.

TL;DR: In this article , the authors explored the relationship between m6A methylation modification and peripheral immune cells in patients with advanced sepsis and mine potential epigenetic therapeutic targets by analyzing the differential expression patterns.

TL;DR: The main idea behind the proposed AGBoost model is to assign attention weights with trainable parameters to iterations of GBM under condition that decision trees are base learners in GBM.

TL;DR: In this article , a new attention-based model for the gradient boosting machine (GBM) called AGBoost is proposed for solving regression problems, which assigns attention weights with trainable parameters to iterations of GBM under condition that decision trees are base learners in GBM.

TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.

TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.

TL;DR: In this paper, the authors propose to use a soft-searching model to find the parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.