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％。

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

We address the problem of extracting key steps from unlabeled procedural videos, motivated by the potential of Augmented Reality (AR) headsets to revolutionize job training and performance. We decompose the problem into two steps: representation learning and key steps extraction. We employ self-supervised representation learning via a training strategy that adapts off-the-shelf video features using a temporal module. Training implements self-supervised learning losses involving multiple cues such as appearance, motion and pose trajectories extracted from videos to learn generalizable representations. Our method extracts key steps via a tunable algorithm that clusters the representations extracted from procedural videos. We quantitatively evaluate our approach with key step localization and also demonstrate the effectiveness of the extracted representations on related downstream tasks like phase classification. Qualitative results demonstrate that the extracted key steps are meaningful to succinctly represent the procedural tasks.

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

Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels. This technique is known to improve the generalization performance in many learning paradigms and applications. In this work, we first analyze Mixup and show that it implicitly regularizes infinitely many directional derivatives of all orders. We then propose a new method to improve Mixup based on the novel insight. To demonstrate the effectiveness of the proposed method, we conduct experiments across various domains such as images, tabular data, speech, and graphs. Our results show that the proposed method improves Mixup across various datasets using a variety of architectures, for instance, exhibiting an improvement over Mixup by 0.8% in ImageNet top-1 accuracy.

Multi-Scale and U-shaped Networks are widely used in various image restoration problems, including deblurring. Keeping in mind the wide range of applications, we present a comparison of these architectures and their effects on image deblurring. We also introduce a new block called as NFResblock. It consists of a Fast Fourier Transformation layer and a series of modified Non-Linear Activation Free Blocks. Based on these architectures and additions, we introduce NFResnet and NFResnet+, which are modified multi-scale and U-Net architectures, respectively. We also use three different loss functions to train these architectures: Charbonnier Loss, Edge Loss, and Frequency Reconstruction Loss. Extensive experiments on the Deep Video Deblurring dataset, along with ablation studies for each component, have been presented in this paper. The proposed architectures achieve a considerable increase in Peak Signal to Noise (PSNR) ratio and Structural Similarity Index (SSIM) value.

We address the problem of few-shot classification where the goal is to learn a classifier from a limited set of samples. While data-driven learning is shown to be effective in various applications, learning from less data still remains challenging. To address this challenge, existing approaches consider various data augmentation techniques for increasing the number of training samples. Pseudo-labeling is commonly used in a few-shot setup, where approximate labels are estimated for a large set of unlabeled images. We propose DiffAlign which focuses on generating images from class labels. Specifically, we leverage the recent success of the generative models (e.g., DALL-E and diffusion models) that can generate realistic images from texts. However, naive learning on synthetic images is not adequate due to the domain gap between real and synthetic images. Thus, we employ a maximum mean discrepancy (MMD) loss to align the synthetic images to the real images minimizing the domain gap. We evaluate our method on the standard few-shot classification benchmarks: CIFAR-FS, FC100, miniImageNet, tieredImageNet and a cross-domain few-shot classification benchmark: miniImageNet to CUB. The proposed approach significantly outperforms the stateof-the-art in both 5-shot and 1-shot setups on these benchmarks. Our approach is also shown to be effective in the zero-shot classification setup

A major direction in differentially private machine learning is differentially private fine-tuning: pretraining a model on a source of "public data" and transferring the extracted features to downstream tasks. This is an important setting because many industry deployments fine-tune publicly available feature extractors on proprietary data for downstream tasks. In this paper, we use features extracted from state-of-the-art open source models to solve benchmark tasks in computer vision and natural language processing using differentially private fine-tuning. Our key insight is that by accelerating training, we can quickly drive the model parameters to regions in parameter space where the impact of noise is minimized. In doing so, we recover the same performance as non-private fine-tuning for realistic values of epsilon in [0.01, 1.0] on benchmark image classification datasets including CIFAR100.

Nostradamus, inspired by the French astrologer and reputed seer, is a detailed study exploring relations between environmental factors and changes in the stock market. In this paper, we analyze associative correlation and causation between environmental elements and stock prices based on the US financial market, global climate trends, and daily weather records to demonstrate significant relationships between climate and stock price fluctuation. Our analysis covers short and long-term rises and dips in company stock performances. Lastly, we take four natural disasters as a case study to observe their effect on the emotional state of people and their influence on the stock market.

Federated Learning (FL) is a machine learning paradigm that enables the training of a shared global model across distributed clients while keeping the training data local. While most prior work on designing systems for FL has focused on using stateful always running components, recent work has shown that components in an FL system can greatly benefit from the usage of serverless computing and Function-as-a-Service technologies. To this end, distributed training of models with severless FL systems can be more resource-efficient and cheaper than conventional FL systems. However, serverless FL systems still suffer from the presence of stragglers, i.e., slow clients due to their resource and statistical heterogeneity. While several strategies have been proposed for mitigating stragglers in FL, most methodologies do not account for the particular characteristics of serverless environments, i.e., cold-starts, performance variations, and the ephemeral stateless nature of the function instances. Towards this, we propose FedLesScan, a novel clustering-based semi-asynchronous training strategy, specifically tailored for serverless FL. FedLesScan dynamically adapts to the behaviour of clients and minimizes the effect of stragglers on the overall system. We implement our strategy by extending an open-source serverless FL system called FedLess. Moreover, we comprehensively evaluate our strategy using the 2nd generation Google Cloud Functions with four datasets and varying percentages of stragglers. Results from our experiments show that compared to other approaches FedLesScan reduces training time and cost by an average of 8% and 20% respectively while utilizing clients better with an average increase in the effective update ratio of 17.75%.