Due to their high predictive performance and flexibility, machine learning
models are an appropriate and efficient tool for ecologists. However,
implementing a machine learning model is not yet a trivial task and may seem
intimidating to ecologists with no previous experience in this area. Here we
provide a series of tips to help ecologists in implementing machine learning
models. We focus on classification problems as many ecological studies aim to
assign data into predefined classes such as ecological states or biological
entities. Each of the nine tips identifies a common error, trap or challenge in
developing machine learning models and provides recommendations to facilitate
their use in ecological studies.

本文提供了九个实现机器学习模型的技巧，旨在帮助生态学家实现高效的分类问题处理，以提高预测表现和灵活性。

生态学家使用机器学习的九个技巧

Nine tips for ecologists using machine learning

Anytime neural networks (AnytimeNNs) are a promising solution to adaptively
adjust the model complexity at runtime under various hardware resource
constraints. However, the manually-designed AnytimeNNs are biased by designers'
prior experience and thus provide sub-optimal solutions. To address the
limitations of existing hand-crafted approaches, we first model the training
process of AnytimeNNs as a discrete-time Markov chain (DTMC) and use it to
identify the paths that contribute the most to the training of AnytimeNNs.
Based on this new DTMC-based analysis, we further propose TIPS, a framework to
automatically design AnytimeNNs under various hardware constraints. Our
experimental results show that TIPS can improve the convergence rate and test
accuracy of AnytimeNNs. Compared to the existing AnytimeNNs approaches, TIPS
improves the accuracy by 2%-6.6% on multiple datasets and achieves SOTA
accuracy-FLOPs tradeoffs.

提出了一种基于马尔可夫链的分析方法，用于自动设计具有任意模型复杂度的神经网络，并且实验证明该方法在多种数据集上能够提高神经网络的准确性和收敛速度。

TIPS: 任意时刻神经网络的拓扑重要路径采样

TIPS: Topologically Important Path Sampling for Anytime Neural Networks

Recently, some E-commerce sites launch a new interaction box called Tips on
their mobile apps. Users can express their experience and feelings or provide
suggestions using short texts typically several words or one sentence. In
essence, writing some tips and giving a numerical rating are two facets of a
user's product assessment action, expressing the user experience and feelings.
Jointly modeling these two facets is helpful for designing a better
recommendation system. While some existing models integrate text information
such as item specifications or user reviews into user and item latent factors
for improving the rating prediction, no existing works consider tips for
improving recommendation quality. We propose a deep learning based framework
named NRT which can simultaneously predict precise ratings and generate
abstractive tips with good linguistic quality simulating user experience and
feelings. For abstractive tips generation, gated recurrent neural networks are
employed to "translate" user and item latent representations into a concise
sentence. Extensive experiments on benchmark datasets from different domains
show that NRT achieves significant improvements over the state-of-the-art
methods. Moreover, the generated tips can vividly predict the user experience
and feelings.

提出了一种名为 NRT 的基于深度学习的框架，该框架同时可以预测精确的评分和生成具有良好语言质量的抽象提示，从而帮助设计更好的推荐系统。在基准数据集上的广泛实验表明，NRT 方法在不同领域均实现了显着改进。此外，生成的提示可以生动地预测用户的体验和感受。