发帖
雷达卡
摘要翻译：
最近在语言建模方面的成功，尤其是深度学习方法，与从概率表示到加权表示的转变不谋而合。我们在这里提出了这种进化的重要性的问题，鉴于经典和简单的概率建模方法对蛋白质序列分类的实际局限性，并与学习非概率模型的原则性方法的需要有关。
---
英文标题：
《Deep learning languages: a key fundamental shift from probabilities to
weights?》
---
作者：
Fran\c{c}ois Coste (Dyliss)
---
最新提交年份：
2019
---
分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
--
一级分类：Computer Science 计算机科学
二级分类：Computation and Language 计算与语言
分类描述：Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.
涵盖自然语言处理。大致包括ACM科目I.2.7类的材料。请注意，人工语言（编程语言、逻辑学、形式系统）的工作，如果没有明确地解决广义的自然语言问题（自然语言处理、计算语言学、语音、文本检索等），就不适合这个领域。
--
一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
--
---
英文摘要：
Recent successes in language modeling, notably with deep learning methods, coincide with a shift from probabilistic to weighted representations. We raise here the question of the importance of this evolution, in the light of the practical limitations of a classical and simple probabilistic modeling approach for the classification of protein sequences and in relation to the need for principled methods to learn non-probabilistic models.
---
PDF链接：
https://arxiv.org/pdf/1908.00785
京公网安备 11010802022788号
