在这项工作中，我们展示了一种新的神经机翻译方法（NMT），使用去噪扩散概率模型（DDPM），调整了文本数据，在该领域的最近进步之后。我们表明，可以使用在源句子上的扩散模型来无自动增加句子。我们还表明，我们的模型能够在培训期间无奈的语言成对（零拍摄学习）之间翻译。

单频语音分离在过去几年中经历了很大的进展。然而，为大量扬声器训练神经言语分离（例如，超过10个扬声器）对当前方法遥不可及，依赖于置换不变丢失（PIT）。在这项工作中，我们提出了一种私奔不变的培训，采用匈牙利算法，以便用$ o（c ^ 3）$时间复杂度训练，其中$ c $是扬声器的数量，与$ o相比（c！）基于坑的方法。此外，我们提出了一种可以处理增加数量的扬声器的修改后的架构。我们的方法将高达20美元的发言者分开，并通过广泛的保证金提高了以上的额外费用的前面的结果。

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.

联合学习（FL）是使用Edge设备上可能可用的私人数据训练机器学习模型的新兴范式。 FL的分布式操作引起了集中式机器学习中未遇到的挑战，包括需要保留本地数据集的隐私以及由于重复交换更新模型而导致的通信负载。这些挑战通常通过引起更新模型的某些失真的技术来单独解决，例如当地差异隐私（LDP）机制和有损压缩。在这项工作中，我们提出了一种方法创造的联合隐私增强和量化（JOPEQ），该隐私和量化共同实现了FL环境中的有损压缩和隐私增强。特别是，Jopeq利用基于随机晶格的矢量量化，这是一种通用压缩技术，其副产品失真在统计学上等同于加性噪声。通过使用专用的多元隐私保护噪声来增强模型更新，可以利用这种失真来增强隐私。我们表明，JOPEQ在持有所需的隐私级别的同时，根据所需的比特率同时量化数据，而不会特别影响学习模型的实用性。这是通过分析的LDP保证，失真和收敛范围的推导以及数值研究所示的。最后，我们从经验上断言，乔普克（Jopeq）拆除了已知的普通攻击，以利用隐私泄漏。

Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort. With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features. However, deep neural networks with embeddings can over-generalize and recommend less relevant items when the user-item interactions are sparse and high-rank. In this paper, we present Wide & Deep learning-jointly trained wide linear models and deep neural networks-to combine the benefits of memorization and generalization for recommender systems. We productionized and evaluated the system on Google Play, a commercial mobile app store with over one billion active users and over one million apps. Online experiment results show that Wide & Deep significantly increased app acquisitions compared with wide-only and deep-only models. We have also open-sourced our implementation in TensorFlow.