Time series models aim for accurate predictions of the future given the past,
where the forecasts are used for important downstream tasks like business
decision making. In practice, deep learning based time series models come in
many forms, but at a high level learn some continuous representation of the
past and use it to output point or probabilistic forecasts. In this paper, we
introduce a novel autoregressive architecture, VQ-AR, which instead learns a
\emph{discrete} set of representations that are used to predict the future.
Extensive empirical comparison with other competitive deep learning models
shows that surprisingly such a discrete set of representations gives
state-of-the-art or equivalent results on a wide variety of time series
datasets. We also highlight the shortcomings of this approach, explore its
zero-shot generalization capabilities, and present an ablation study on the
number of representations. The full source code of the method will be available
at the time of publication with the hope that researchers can further
investigate this important but overlooked inductive bias for the time series
domain.

该论文介绍了一种新颖的自回归模型 VQ-AR，使用离散表示来预测未来，并在多个时间序列数据集上展示了其能够取得先进的性能。

VQ-AR: 向量量化自回归概率时间序列预测

VQ-AR: Vector Quantized Autoregressive Probabilistic Time Series Forecasting

The present paper proposes a novel reinforcement learning method with world
models, DreamingV2, a collaborative extension of DreamerV2 and Dreaming.
DreamerV2 is a cutting-edge model-based reinforcement learning from pixels that
uses discrete world models to represent latent states with categorical
variables. Dreaming is also a form of reinforcement learning from pixels that
attempts to avoid the autoencoding process in general world model training by
involving a reconstruction-free contrastive learning objective. The proposed
DreamingV2 is a novel approach of adopting both the discrete representation of
DreamingV2 and the reconstruction-free objective of Dreaming. Compared to
DreamerV2 and other recent model-based methods without reconstruction,
DreamingV2 achieves the best scores on five simulated challenging 3D robot arm
tasks. We believe that DreamingV2 will be a reliable solution for robot
learning since its discrete representation is suitable to describe
discontinuous environments, and the reconstruction-free fashion well manages
complex vision observations.

提出了一种新的强化学习方法 DreamingV2，它是 DreamerV2 和 Dreaming 的协作扩展，与 DreamingV2 的离散表示和 Dreaming 的无重构目标相比，DreamingV2 在 3D 机器人臂任务上取得了最佳成绩，适用于描述不连续环境的离散表示以及管理复杂视觉观察的无重构方式，是一种可靠的机器人学习解决方案。

DreamingV2: 基于离散世界模型的强化学习方法，无需重构

DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction

In this paper, we explore a method for training speech-to-speech translation
tasks without any transcription or linguistic supervision. Our proposed method
consists of two steps: First, we train and generate discrete representation
with unsupervised term discovery with a discrete quantized autoencoder. Second,
we train a sequence-to-sequence model that directly maps the source language
speech to the target language's discrete representation. Our proposed method
can directly generate target speech without any auxiliary or pre-training steps
with a source or target transcription. To the best of our knowledge, this is
the first work that performed pure speech-to-speech translation between
untranscribed unknown languages.

本文提出了一种无需转录或语言监督的训练语音翻译的方法，使用自监督无监督组合的离散量化自动编码器进行训练，然后使用序列到序列模型将源语言语音直接映射到目标语言的离散表示上，最终实现无需先训练或辅助步骤即可直接生成目标语音；该方法是第一次在未经转录的未知语言之间进行纯语音翻译的研究。