The ''Propose-Test-Release'' (PTR) framework is a classic recipe for designing differentially private (DP) algorithms that are data-adaptive, i.e. those that add less noise when the input dataset is nice. We extend PTR to a more general setting by privately testing data-dependent privacy losses rather than local sensitivity, hence making it applicable beyond the standard noise-adding mechanisms, e.g. to queries with unbounded or undefined sensitivity. We demonstrate the versatility of generalized PTR using private linear regression as a case study. Additionally, we apply our algorithm to solve an open problem from ''Private Aggregation of Teacher Ensembles (PATE)'' -- privately releasing the entire model with a delicate data-dependent analysis.

最近结束语音合成的最新进步使得能够产生高度自然的语音。然而，训练这些模型通常需要大量的高保真语音数据，并且对于看不见的文本，合成语音的韵律相对不自然。为了解决这些问题，我们建议将基于精细的BERT基前端与基于预先训练的FastSeech2的声学模型结合起来，以改善韵律建模。在多任务学习中，预训练的伯爵在多电话消歧任务中，联合中文词组分割任务，联合中文字分割（CWS）和演讲（POS）标记任务，以及在多任务学习中的韵律结构预测（PSP）任务框架。FastSeech 2在大规模的外部数据上预先培训，这些数据很少，但更容易获得。实验结果表明，微调BERT模型和预训练的禁止轴2可以改善韵律，特别是对于那些结构复杂的句子。

特征在于构图的隐私劣化，即隐私会计，是差异隐私（DP）的基本话题，许多应用于差异私有机器学习和联合学习。我们提出了近期进步（Renyi DP，Privacy Compiles，$-D $ -dp和Pld形式主义）的统一，通过\ emph {phi $ \ phi $ -function）{占主导地位}隐私损失随机变量。我们展示了我们的方法允许\ emph {natural}自适应组成等renyi dp，提供\ emph {完全紧张}隐私会计，如pld，并且可以（通常是\ memph {docklyly}）转换为隐私权概况和$ f $ -dp ，从而提供$（\ epsilon，\ delta）$ - DP保证和可解释的权衡职能。算法，我们提出了一个\ xper {分析傅里叶会计师}，它象征性地表示$ \ phi $ -functions的\ icph {complex}对数，并使用高斯正交进行数值计算。在几个受欢迎的DP机制及其撤销的对应物上，我们展示了我们在理论和实验中的方法的灵活性和紧张性。

Image token removal is an efficient augmentation strategy for reducing the cost of computing image features. However, this efficient augmentation strategy has been found to adversely affect the accuracy of CLIP-based training. We hypothesize that removing a large portion of image tokens may improperly discard the semantic content associated with a given text description, thus constituting an incorrect pairing target in CLIP training. To address this issue, we propose an attentive token removal approach for CLIP training, which retains tokens with a high semantic correlation to the text description. The correlation scores are computed in an online fashion using the EMA version of the visual encoder. Our experiments show that the proposed attentive masking approach performs better than the previous method of random token removal for CLIP training. The approach also makes it efficient to apply multiple augmentation views to the image, as well as introducing instance contrastive learning tasks between these views into the CLIP framework. Compared to other CLIP improvements that combine different pre-training targets such as SLIP and MaskCLIP, our method is not only more effective, but also much more efficient. Specifically, using ViT-B and YFCC-15M dataset, our approach achieves $43.9\%$ top-1 accuracy on ImageNet-1K zero-shot classification, as well as $62.7/42.1$ and $38.0/23.2$ I2T/T2I retrieval accuracy on Flickr30K and MS COCO, which are $+1.1\%$, $+5.5/+0.9$, and $+4.4/+1.3$ higher than the SLIP method, while being $2.30\times$ faster. An efficient version of our approach running $1.16\times$ faster than the plain CLIP model achieves significant gains of $+5.3\%$, $+11.3/+8.0$, and $+9.5/+4.9$ on these benchmarks.

Full-body reconstruction is a fundamental but challenging task. Owing to the lack of annotated data, the performances of existing methods are largely limited. In this paper, we propose a novel method named Full-body Reconstruction from Part Experts~(FuRPE) to tackle this issue. In FuRPE, the network is trained using pseudo labels and features generated from part-experts. An simple yet effective pseudo ground-truth selection scheme is proposed to extract high-quality pseudo labels. In this way, a large-scale of existing human body reconstruction datasets can be leveraged and contribute to the model training. In addition, an exponential moving average training strategy is introduced to train the network in a self-supervised manner, further boosting the performance of the model. Extensive experiments on several widely used datasets demonstrate the effectiveness of our method over the baseline. Our method achieves the state-of-the-art performance. Code will be publicly available for further research.

In recent years, applying deep learning (DL) to assess structural damages has gained growing popularity in vision-based structural health monitoring (SHM). However, both data deficiency and class-imbalance hinder the wide adoption of DL in practical applications of SHM. Common mitigation strategies include transfer learning, over-sampling, and under-sampling, yet these ad-hoc methods only provide limited performance boost that varies from one case to another. In this work, we introduce one variant of the Generative Adversarial Network (GAN), named the balanced semi-supervised GAN (BSS-GAN). It adopts the semi-supervised learning concept and applies balanced-batch sampling in training to resolve low-data and imbalanced-class problems. A series of computer experiments on concrete cracking and spalling classification were conducted under the low-data imbalanced-class regime with limited computing power. The results show that the BSS-GAN is able to achieve better damage detection in terms of recall and $F_\beta$ score than other conventional methods, indicating its state-of-the-art performance.

In recent years, there is a surge of generation-based information extraction work, which allows a more direct use of pre-trained language models and efficiently captures output dependencies. However, previous generative methods using lexical representation do not naturally fit document-level relation extraction (DocRE) where there are multiple entities and relational facts. In this paper, we investigate the root cause of the underwhelming performance of the existing generative DocRE models and discover that the culprit is the inadequacy of the training paradigm, instead of the capacities of the models. We propose to generate a symbolic and ordered sequence from the relation matrix which is deterministic and easier for model to learn. Moreover, we design a parallel row generation method to process overlong target sequences. Besides, we introduce several negative sampling strategies to improve the performance with balanced signals. Experimental results on four datasets show that our proposed method can improve the performance of the generative DocRE models. We have released our code at https://github.com/ayyyq/DORE.

In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. To ease communication burden, over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication. Based on the proposed system, the system implementation over wireless network is introduced and we provide the problem formulation. In particular, we show that the inter-layer connection in a NN of any size can be mathematically decomposed into a set of linear precoding and combining transformations over MIMO channels. Therefore, the precoding matrix at the transmitter and the combining matrix at the receiver of each MIMO link, as well as the channel matrix itself, can jointly serve as a fully connected layer of the NN. The generalization of the proposed scheme to the conventional NNs is also introduced. Finally, we extend the proposed scheme to the widely used convolutional neural networks and demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations. In such a split ML system, the precoding and combining matrices are regarded as trainable parameters, while MIMO channel matrix is regarded as unknown (implicit) parameters.

考虑到数据注释的成本以及几乎没有标记的样本所提供的准确性提高，几乎没有射击的成本，几乎没有射击的转移学习越来越多。尤其是在少量分类（FSC）中，最近的作品探索了旨在最大程度地相对于未知参数的可能性或后二阶段的特征分布。遵循这种静脉，并考虑到FSC和聚类之间的平行，我们寻求更好地考虑到由于缺乏数据而导致的估计不确定性，以及与每个类相关的群集的统计属性更好。因此，在本文中，我们提出了一种基于变异贝叶斯推论的新聚类方法，基于概率线性判别分析，自适应维度降低进一步改善。当应用于先前研究中使用的功能时，我们提出的方法可显着提高在各种少量基准测试的现实不平衡转导设置中的准确性，其准确性高达$ 6 \％$。此外，当应用于平衡设置时，我们将获得非常有竞争力的结果，而无需使用对实际用例的级别平衡伪像。我们还提供了方法的性能，以高性能的主链链链，其报告的结果进一步超过了当前的最新准确性，这表明该方法的通用性。

电子商务搜索的关键是如何最好地利用大型但嘈杂的日志数据。在本文中，我们在Instacart介绍了基于嵌入的杂货搜索模型。该系统通过基于两个塔式变压器的编码器体系结构学习查询和产品表示。为了解决冷门问题，我们专注于基于内容的功能。为了在嘈杂的数据上有效地培训模型，我们提出了一种自我分歧学习方法和级联培训方法。Accon是一个离线人类评估数据集，我们在召回@20方面取得了10％的相对改善，对于在线A/B测试，我们每次搜索（CAPS）获得4.1％的Cart-Addds（CAPS）和1.5％的总商品价值（GMV）改进。我们描述了如何训练和部署基于嵌入的搜索模型，并对我们方法的有效性进行详细分析。