Text-guided molecule generation is a task where molecules are generated to
match specific textual descriptions. Recently, most existing SMILES-based
molecule generation methods rely on an autoregressive architecture. In this
work, we propose the Text-Guided Molecule Generation with Diffusion Language
Model (TGM-DLM), a novel approach that leverages diffusion models to address
the limitations of autoregressive methods. TGM-DLM updates token embeddings
within the SMILES string collectively and iteratively, using a two-phase
diffusion generation process. The first phase optimizes embeddings from random
noise, guided by the text description, while the second phase corrects invalid
SMILES strings to form valid molecular representations. We demonstrate that
TGM-DLM outperforms MolT5-Base, an autoregressive model, without the need for
additional data resources. Our findings underscore the remarkable effectiveness
of TGM-DLM in generating coherent and precise molecules with specific
properties, opening new avenues in drug discovery and related scientific
domains. Code will be released at: this https URL

本文介绍了一种基于扩散语言模型的文本导向分子生成方法 (TGM-DLM)，该方法在生成具有特定文本描述的分子时克服了自回归方法的局限性，并通过两阶段的扩散生成过程共同和迭代地更新 SMILES 字符串中的标记嵌入。实验证明，TGM-DLM 模型在生成具有特定属性的连贯且准确的分子方面优于自回归模型 MolT5-Base，无需额外的数据资源，为药物发现和相关科学领域开辟了新的途径。

基于扩散语言模型的文本引导分子生成

Text-Guided Molecule Generation with Diffusion Language Model

Simultaneous machine translation (SiMT) models are trained to strike a
balance between latency and translation quality. However, training these models
to achieve high quality while maintaining low latency often leads to a tendency
for aggressive anticipation. We argue that such issue stems from the
autoregressive architecture upon which most existing SiMT models are built. To
address those issues, we propose non-autoregressive streaming Transformer
(NAST) which comprises a unidirectional encoder and a non-autoregressive
decoder with intra-chunk parallelism. We enable NAST to generate the blank
token or repetitive tokens to adjust its READ/WRITE strategy flexibly, and
train it to maximize the non-monotonic latent alignment with an alignment-based
latency loss. Experiments on various SiMT benchmarks demonstrate that NAST
outperforms previous strong autoregressive SiMT baselines.

提出了一种非自回归流式 Transformer（NAST）用于同时机器翻译（SiMT），通过通过一种新的编码器和非自回归解码器拓宽了读 / 写策略并降低延迟损失，实验证明 NAST 优于以往自回归 SiMT 基线模型。

非自回归流式 Transformer 用于同声翻译

Non-autoregressive Streaming Transformer for Simultaneous Translation

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。