原则上，将变异自动编码器（VAE）应用于顺序数据提供了一种用于控制序列生成，操纵和结构化表示学习的方法。但是，训练序列VAE具有挑战性：自回归解码器通常可以解释数据而无需使用潜在空间，即后置倒塌。为了减轻这种情况，最新的模型通过将均匀的随机辍学量应用于解码器输入来削弱强大的解码器。从理论上讲，我们表明，这可以消除解码器输入提供的点式互信息，该信息通过利用潜在空间来补偿。然后，我们提出了一种对抗性训练策略，以实现基于信息的随机辍学。与标准文本基准数据集上的均匀辍学相比，我们的目标方法同时提高了序列建模性能和潜在空间中捕获的信息。

极端分类（XC）试图用最大的标签集中标记标签的子集标记数据点。通过使用稀疏，手工制作的功能的XC方法优越，用密集，学习的数据来进行深度XC，以数据点和标签的形式吸引了很多关注。负挖掘技术已成为所有深XC方法的关键组成部分，使它们可以扩展到数百万个标签。然而，尽管最近进步，但培训具有大型编码器体系结构（例如变形金刚）的深入XC模型仍然具有挑战性。本文确定，流行负面挖掘技术的内存通常迫使小型批量尺寸保持小且缓慢的训练。作为回应，本文介绍了Ngame，这是一种轻巧的迷你批次创建技术，可证明可证明准确的内部负面样品。这使得与现有负面采样技术相比，具有更大的迷你批次培训，提供更快的收敛性和更高的精度。发现Ngame的准确性比各种基准数据集的最先进方法要高16％，以进行极端分类，并且在回答搜索引擎查询以响应用户网页时检索搜索引擎查询更准确3％显示个性化广告。在流行搜索引擎的实时A/B测试中，Ngame在点击率率中的收益最高可达23％。

我们考虑对表格数据的自我监督表示学习（SSL）的任务：表格-SSL。典型的基于学习的SSL方法需要实例数据增强，这对于非结构化表格数据很难设计。现有的表格SSL方法以相对临时的方式设计这种增强性，并且无法捕获基础数据歧管。我们提出了一种新的基于重建的方法，而不是针对表格SSL的基于增强的方法，称为表格数据（MET），不需要增强。 MET基于视觉-SSL的流行MAE方法[He等，2021]，并使用两个关键想法：（i）由于表格数据集中的每个坐标都具有独特的含义，因此我们需要为所有坐标使用单独的表示形式，（ii）除了标准损失外，还使用对抗性重建损失。五个不同表格数据集的经验结果表明，MET在所有这些数据集上实现了新的最新技术（SOTA），并且比当前的SOTA方法提高了9％。我们通过实验在精心设计的简单数据集上进行了更多的启示。

可扩展性和准确性在深度极端多标签学习中得到了很好的认可挑战，其中目标是培训架构，以便自动注释具有来自极大的标签集的最相关标签子集的数据点。本文通过将深度极端多标签任务分解为四个更简单的子任务，开发了解决这些挑战的DeepXML框架，每个挑战可以准确且有效地培训。为四个子任务选择不同的组件允许DeepXML生成一个算法系列，在准确性和可扩展性之间产生不同的权衡。特别是，DeepXML产生了ASTEC算法，可以比公开可用的短文本数据集上的领先深度极端分类器更准确，5-30倍更快地进行5-30倍。 ASTEC还可以有效地在Bing短文本数据集上培训，该数据集包含多达6200万个标签，同时在商品硬件上进行数十亿用户和数据点的预测。这允许ASTEC部署在Bing搜索引擎上，以获取许多短文本应用程序，范围从匹配用户查询到广告商出价短语，以显示个性化广告，其中它在点击率，覆盖范围，收入和其他在线指标中产生了显着的收益目前在生产中的最先进技术。 Deepxml的代码可在https://github.com/extreme-classification/deepxml上获得

Multiple studies have focused on predicting the prospective popularity of an online document as a whole, without paying attention to the contributions of its individual parts. We introduce the task of proactively forecasting popularities of sentences within online news documents solely utilizing their natural language content. We model sentence-specific popularity forecasting as a sequence regression task. For training our models, we curate InfoPop, the first dataset containing popularity labels for over 1.7 million sentences from over 50,000 online news documents. To the best of our knowledge, this is the first dataset automatically created using streams of incoming search engine queries to generate sentence-level popularity annotations. We propose a novel transfer learning approach involving sentence salience prediction as an auxiliary task. Our proposed technique coupled with a BERT-based neural model exceeds nDCG values of 0.8 for proactive sentence-specific popularity forecasting. Notably, our study presents a non-trivial takeaway: though popularity and salience are different concepts, transfer learning from salience prediction enhances popularity forecasting. We release InfoPop and make our code publicly available: https://github.com/sayarghoshroy/InfoPopularity

Speech systems are sensitive to accent variations. This is especially challenging in the Indian context, with an abundance of languages but a dearth of linguistic studies characterising pronunciation variations. The growing number of L2 English speakers in India reinforces the need to study accents and L1-L2 interactions. We investigate the accents of Indian English (IE) speakers and report in detail our observations, both specific and common to all regions. In particular, we observe the phonemic variations and phonotactics occurring in the speakers' native languages and apply this to their English pronunciations. We demonstrate the influence of 18 Indian languages on IE by comparing the native language pronunciations with IE pronunciations obtained jointly from existing literature studies and phonetically annotated speech of 80 speakers. Consequently, we are able to validate the intuitions of Indian language influences on IE pronunciations by justifying pronunciation rules from the perspective of Indian language phonology. We obtain a comprehensive description in terms of universal and region-specific characteristics of IE, which facilitates accent conversion and adaptation of existing ASR and TTS systems to different Indian accents.

Voice assistants are deployed widely and provide useful functionality. However, recent work has shown that commercial systems like Amazon Alexa and Google Home are vulnerable to voice-based confusion attacks that exploit design issues. We propose a systems-oriented defense against this class of attacks and demonstrate its functionality for Amazon Alexa. We ensure that only the skills a user intends execute in response to voice commands. Our key insight is that we can interpret a user's intentions by analyzing their activity on counterpart systems of the web and smartphones. For example, the Lyft ride-sharing Alexa skill has an Android app and a website. Our work shows how information from counterpart apps can help reduce dis-ambiguities in the skill invocation process. We build SkilIFence, a browser extension that existing voice assistant users can install to ensure that only legitimate skills run in response to their commands. Using real user data from MTurk (N = 116) and experimental trials involving synthetic and organic speech, we show that SkillFence provides a balance between usability and security by securing 90.83% of skills that a user will need with a False acceptance rate of 19.83%.

In molecular research, simulation \& design of molecules are key areas with significant implications for drug development, material science, and other fields. Current classical computational power falls inadequate to simulate any more than small molecules, let alone protein chains on hundreds of peptide. Therefore these experiment are done physically in wet-lab, but it takes a lot of time \& not possible to examine every molecule due to the size of the search area, tens of billions of dollars are spent every year in these research experiments. Molecule simulation \& design has lately advanced significantly by machine learning models, A fresh perspective on the issue of chemical synthesis is provided by deep generative models for graph-structured data. By optimising differentiable models that produce molecular graphs directly, it is feasible to avoid costly search techniques in the discrete and huge space of chemical structures. But these models also suffer from computational limitations when dimensions become huge and consume huge amount of resources. Quantum Generative machine learning in recent years have shown some empirical results promising significant advantages over classical counterparts.

Developing and least developed countries face the dire challenge of ensuring that each child in their country receives required doses of vaccination, adequate nutrition and proper medication. International agencies such as UNICEF, WHO and WFP, among other organizations, strive to find innovative solutions to determine which child has received the benefits and which have not. Biometric recognition systems have been sought out to help solve this problem. To that end, this report establishes a baseline accuracy of a commercial contactless palmprint recognition system that may be deployed for recognizing children in the age group of one to five years old. On a database of contactless palmprint images of one thousand unique palms from 500 children, we establish SOTA authentication accuracy of 90.85% @ FAR of 0.01%, rank-1 identification accuracy of 99.0% (closed set), and FPIR=0.01 @ FNIR=0.3 for open-set identification using PalmMobile SDK from Armatura.

Concept bottleneck models (CBMs) (Koh et al. 2020) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions.We extend CBMs to interactive prediction settings where the model can query a human collaborator for the label to some concepts. We develop an interaction policy that, at prediction time, chooses which concepts to request a label for so as to maximally improve the final prediction. We demonstrate thata simple policy combining concept prediction uncertainty and influence of the concept on the final prediction achieves strong performance and outperforms a static approach proposed in Koh et al. (2020) as well as active feature acquisition methods proposed in the literature. We show that the interactiveCBM can achieve accuracy gains of 5-10% with only 5 interactions over competitive baselines on the Caltech-UCSDBirds, CheXpert and OAI datasets.