Stochastic learning to rank (LTR) is a recent branch in the LTR field that
concerns the optimization of probabilistic ranking models. Their probabilistic
behavior enables certain ranking qualities that are impossible with
deterministic models. For example, they can increase the diversity of displayed
documents, increase fairness of exposure over documents, and better balance
exploitation and exploration through randomization. A core difficulty in LTR is
gradient estimation, for this reason, existing stochastic LTR methods have been
limited to differentiable ranking models (e.g., neural networks). This is in
stark contrast with the general field of LTR where Gradient Boosted Decision
Trees (GBDTs) have long been considered the state-of-the-art.
In this work, we address this gap by introducing the first stochastic LTR
method for GBDTs. Our main contribution is a novel estimator for the
second-order derivatives, i.e., the Hessian matrix, which is a requirement for
effective GBDTs. To efficiently compute both the first and second-order
derivatives simultaneously, we incorporate our estimator into the existing
PL-Rank framework, which was originally designed for first-order derivatives
only. Our experimental results indicate that stochastic LTR without the Hessian
has extremely poor performance, whilst the performance is competitive with the
current state-of-the-art with our estimated Hessian. Thus, through the
contribution of our novel Hessian estimation method, we have successfully
introduced GBDTs to stochastic LTR.

本文介绍了在概率排序模型优化中引入了第一个基于 GBDT 的随机学习排序方法，通过引入 Hessian 矩阵的新估算器，有效地计算一阶和二阶导数，并且实验证明具有估算的 Hessian 矩阵的随机学习排序在性能上与当前最先进的方法相竞争，成功地将 GBDT 引入随机学习排序。

基于梯度提升树的随机排序学习目标海森矩阵估计

Estimating the Hessian Matrix of Ranking Objectives for Stochastic  Learning to Rank with Gradient Boosted Trees

Previous probabilistic models for 3D Human Pose Estimation (3DHPE) aimed to
enhance pose accuracy by generating multiple hypotheses. However, most of the
hypotheses generated deviate substantially from the true pose. Compared to
deterministic models, the excessive uncertainty in probabilistic models leads
to weaker performance in single-hypothesis prediction. To address these two
challenges, we propose a diffusion-based refinement framework called DRPose,
which refines the output of deterministic models by reverse diffusion and
achieves more suitable multi-hypothesis prediction for the current pose
benchmark by multi-step refinement with multiple noises. To this end, we
propose a Scalable Graph Convolution Transformer (SGCT) and a Pose Refinement
Module (PRM) for denoising and refining. Extensive experiments on Human3.6M and
MPI-INF-3DHP datasets demonstrate that our method achieves state-of-the-art
performance on both single and multi-hypothesis 3DHPE. Code is available at
this https URL

我们提出了一种基于扩散的优化框架 DRPose 来改进确定性模型的性能，并借助多噪声的多步骤优化和多假设预测，实现了适用于当前姿势基准的更合适的多假设预测。

基于扩散的姿态优化与多假设生成的三维人体姿态估计

Diffusion-based Pose Refinement and Muti-hypothesis Generation for 3D  Human Pose Estimaiton

The nondeterminism of Deep Learning (DL) training algorithms and its
influence on the explainability of neural network (NN) models are investigated
in this work with the help of image classification examples. To discuss the
issue, two convolutional neural networks (CNN) have been trained and their
results compared. The comparison serves the exploration of the feasibility of
creating deterministic, robust DL models and deterministic explainable
artificial intelligence (XAI) in practice. Successes and limitation of all here
carried out efforts are described in detail. The source code of the attained
deterministic models has been listed in this work. Reproducibility is indexed
as a development-phase-component of the Model Governance Framework, proposed by
the EU within their excellence in AI approach. Furthermore, reproducibility is
a requirement for establishing causality for the interpretation of model
results and building of trust towards the overwhelming expansion of AI systems
applications. Problems that have to be solved on the way to reproducibility and
ways to deal with some of them, are examined in this work.

本文通过图像分类示例调查深度学习（DL）训练算法的不确定性及其对神经网络（NN）模型可解释性的影响，探讨了创建实用性强的确定性，鲁棒性高的 DL 模型和确定性可解释人工智能（XAI）的可行性。并说明了其工作的成功与局限性，确定性模型的源代码已在本文中列出，同时介绍了欧盟在卓越人工智能的框架内提出的模型治理框架开发阶段组件所标注的可重复性，并探讨了在实现可重复性的过程中需要解决的问题以及处理某些问题的方法。