tSNE and UMAP are popular dimensionality reduction algorithms due to their
speed and interpretable low-dimensional embeddings. Despite their popularity,
however, little work has been done to study their full span of differences. We
theoretically and experimentally evaluate the space of parameters in both tSNE
and UMAP and observe that a single one -- the normalization -- is responsible
for switching between them. This, in turn, implies that a majority of the
algorithmic differences can be toggled without affecting the embeddings. We
discuss the implications this has on several theoretic claims behind UMAP, as
well as how to reconcile them with existing tSNE interpretations.
Based on our analysis, we provide a method (\ourmethod) that combines
previously incompatible techniques from tSNE and UMAP and can replicate the
results of either algorithm. This allows our method to incorporate further
improvements, such as an acceleration that obtains either method's outputs
faster than UMAP. We release improved versions of tSNE, UMAP, and \ourmethod
that are fully plug-and-play with the traditional libraries at
this https URL

本文通过对 tSNE 和 UMAP 算法中的参数空间进行理论和实证研究，发现仅有的一个参数 -- 规范化参数，可以在两个算法之间进行切换，而不会对嵌入产生任何影响。基于此，提出一种方法（ourmethod）结合 tSNE 和 UMAP 的技术，可以复制任一算法的结果，并在速度上取得更快的响应。

ActUp：分析和合并 tSNE 和 UMAP

ActUp: Analyzing and Consolidating tSNE and UMAP

Model-based reinforcement learning (MBRL) is widely seen as having the
potential to be significantly more sample efficient than model-free RL.
However, research in model-based RL has not been very standardized. It is
fairly common for authors to experiment with self-designed environments, and
there are several separate lines of research, which are sometimes
closed-sourced or not reproducible. Accordingly, it is an open question how
these various existing MBRL algorithms perform relative to each other. To
facilitate research in MBRL, in this paper we gather a wide collection of MBRL
algorithms and propose over 18 benchmarking environments specially designed for
MBRL. We benchmark these algorithms with unified problem settings, including
noisy environments. Beyond cataloguing performance, we explore and unify the
underlying algorithmic differences across MBRL algorithms. We characterize
three key research challenges for future MBRL research: the dynamics
bottleneck, the planning horizon dilemma, and the early-termination dilemma.
Finally, to maximally facilitate future research on MBRL, we open-source our
benchmark in this http URL

本文为了推动模型基强化学习（Model-based Reinforcement Learning, MBRL）的研究，收集了大量 MBRL 算法，并提出了 18 个为 MBRL 特别设计的基准环境来评估这些算法，并探讨了 MBRL 算法之间的主要差异和研究挑战。