我们提出SERP，这是3D点云的自我监督学习的框架。 SERP由编码器编码器架构组成，该体系结构将被扰动或损坏的点云作为输入和旨在重建原始点云而无需损坏。编码器在低维子空间中学习了点云的高级潜在表示，并恢复原始结构。在这项工作中，我们使用了基于变压器和基于点网的自动编码器。所提出的框架还解决了基于变形金刚的掩盖自动编码器的一些局限性，这些框架容易泄漏位置信息和不均匀的信息密度。我们在完整的Shapenet数据集上训练了模型，并将它们作为下游分类任务评估。我们已经表明，审慎的模型比从头开始训练的网络实现了0.5-1％的分类精度。此外，我们还提出了VASP：对矢量定量的自动编码器，用于对点云进行自我监督的表示学习，这些学习用于基于变压器的自动编码器的离散表示学习。

跨语言嵌入可以应用于多种语言的几种自然语言处理应用程序。与先前使用基于欧几里得空间嵌入单词嵌入的作品不同，这篇简短的论文提出了一种简单有效的跨语言2VEC模型，该模型适应了PoinCar \'E Ball of双曲空间的球模型，从 - 英语平行语料库。已经表明，双曲线嵌入可以捕获和保留分层关系。我们在高呼气和类比任务上评估了模型。所提出的模型在跨语言类比任务上与香草word2Vec模型实现了可比的性能，超呼气任务表明，跨语义的poincar \'e Word2vec模型可以从跨语言中捕获潜在的层次结构，而这些文本跨越跨语言，这些结构是从跨语言中捕获的基于欧几里得的Word2Vec表示。我们的结果表明，通过保留潜在的分层信息，双曲线空间可以为跨语性嵌入提供更好的表示。

t-SNE remains one of the most popular embedding techniques for visualizing high-dimensional data. Most standard packages of t-SNE, such as scikit-learn, use the Barnes-Hut t-SNE (BH t-SNE) algorithm for large datasets. However, existing CPU implementations of this algorithm are inefficient. In this work, we accelerate the BH t-SNE on CPUs via cache optimizations, SIMD, parallelizing sequential steps, and improving parallelization of multithreaded steps. Our implementation (Acc-t-SNE) is up to 261x and 4x faster than scikit-learn and the state-of-the-art BH t-SNE implementation from daal4py, respectively, on a 32-core Intel(R) Icelake cloud instance.

Position modeling plays a critical role in Transformers. In this paper, we focus on length extrapolation, i.e., training on short texts while evaluating longer sequences. We define attention resolution as an indicator of extrapolation. Then we propose two designs to improve the above metric of Transformers. Specifically, we introduce a relative position embedding to explicitly maximize attention resolution. Moreover, we use blockwise causal attention during inference for better resolution. We evaluate different Transformer variants with language modeling. Experimental results show that our model achieves strong performance in both interpolation and extrapolation settings. The code will be available at https://aka.ms/LeX-Transformer.

A true interpreting agent not only understands sign language and translates to text, but also understands text and translates to signs. Much of the AI work in sign language translation to date has focused mainly on translating from signs to text. Towards the latter goal, we propose a text-to-sign translation model, SignNet, which exploits the notion of similarity (and dissimilarity) of visual signs in translating. This module presented is only one part of a dual-learning two task process involving text-to-sign (T2S) as well as sign-to-text (S2T). We currently implement SignNet as a single channel architecture so that the output of the T2S task can be fed into S2T in a continuous dual learning framework. By single channel, we refer to a single modality, the body pose joints. In this work, we present SignNet, a T2S task using a novel metric embedding learning process, to preserve the distances between sign embeddings relative to their dissimilarity. We also describe how to choose positive and negative examples of signs for similarity testing. From our analysis, we observe that metric embedding learning-based model perform significantly better than the other models with traditional losses, when evaluated using BLEU scores. In the task of gloss to pose, SignNet performed as well as its state-of-the-art (SoTA) counterparts and outperformed them in the task of text to pose, by showing noteworthy enhancements in BLEU 1 - BLEU 4 scores (BLEU 1: 31->39; ~26% improvement and BLEU 4: 10.43->11.84; ~14\% improvement) when tested on the popular RWTH PHOENIX-Weather-2014T benchmark dataset

Workloads in modern cloud data centers are becoming increasingly complex. The number of workloads running in cloud data centers has been growing exponentially for the last few years, and cloud service providers (CSP) have been supporting on-demand services in real-time. Realizing the growing complexity of cloud environment and cloud workloads, hardware vendors such as Intel and AMD are increasingly introducing cloud-specific workload acceleration features in their CPU platforms. These features are typically targeted towards popular and commonly-used cloud workloads. Nonetheless, uncommon, customer-specific workloads (unknown workloads), if their characteristics are different from common workloads (known workloads), may not realize the potential of the underlying platform. To address this problem of realizing the full potential of the underlying platform, we develop a machine learning based technique to characterize, profile and predict workloads running in the cloud environment. Experimental evaluation of our technique demonstrates good prediction performance. We also develop techniques to analyze the performance of the model in a standalone manner.

With the rising adoption of Machine Learning across the domains like banking, pharmaceutical, ed-tech, etc, it has become utmost important to adopt responsible AI methods to ensure models are not unfairly discriminating against any group. Given the lack of clean training data, generative adversarial techniques are preferred to generate synthetic data with several state-of-the-art architectures readily available across various domains from unstructured data such as text, images to structured datasets modelling fraud detection and many more. These techniques overcome several challenges such as class imbalance, limited training data, restricted access to data due to privacy issues. Existing work focusing on generating fair data either works for a certain GAN architecture or is very difficult to tune across the GANs. In this paper, we propose a pipeline to generate fairer synthetic data independent of the GAN architecture. The proposed paper utilizes a pre-processing algorithm to identify and remove bias inducing samples. In particular, we claim that while generating synthetic data most GANs amplify bias present in the training data but by removing these bias inducing samples, GANs essentially focuses more on real informative samples. Our experimental evaluation on two open-source datasets demonstrates how the proposed pipeline is generating fair data along with improved performance in some cases.

元加强学习（META-RL）是一种方法，即从解决各种任务中获得的经验被蒸馏成元政策。当仅适应一个小（或仅一个）数量的步骤时，元派利赛能够在新的相关任务上近距离执行。但是，采用这种方法来解决现实世界中的问题的主要挑战是，它们通常与稀疏的奖励功能相关联，这些功能仅表示任务是部分或完全完成的。我们考虑到某些数据可能由亚最佳代理生成的情况，可用于每个任务。然后，我们使用示范（EMRLD）开发了一类名为“增强元RL”的算法，即使在训练过程中获得了次优的指导，也可以利用此信息。我们展示了EMRLD如何共同利用RL和在离线数据上进行监督学习，以生成一个显示单调性能改进的元数据。我们还开发了一个称为EMRLD-WS的温暖开始的变体，该变体对于亚最佳演示数据特别有效。最后，我们表明，在包括移动机器人在内的各种稀疏奖励环境中，我们的EMRLD算法显着优于现有方法。

通常使用卷积神经网络（CNN）进行计算机视觉。 CNN是计算密集型的，并且在移动和互联网（IoT）设备等电力控制系统上部署。 CNN是计算密集型的，因为它们不加选择地计算输入图像的所有像素上的许多特征。我们观察到，鉴于计算机视觉任务，图像通常包含与任务无关的像素。例如，如果任务正在寻找汽车，那么天空中的像素不是很有用。因此，我们建议对CNN进行修改以仅在相关像素上操作以节省计算和能量。我们提出了一种研究三个流行的计算机视觉数据集的方法，发现48％的像素无关紧要。我们还提出了重点卷积，以修改CNN的卷积层，以拒绝明显无关的像素。在嵌入式设备上，我们没有观察到准确性的损失，而推论潜伏期，能耗和倍增add计数均减少了约45％。

现代语言模型利用越来越多的参数来实现自然语言理解任务的性能。在特定配置中完成这些模型以进行下游任务，甚至可以进一步改进性能。在本文中，我们对包装语言模型进行分析，并将单语言模型与在最终模型大小上大致相同的包装合奏进行比较。我们探索一系列模型包装配置，用于自然语言理解任务，最终合奏尺寸从300m参数到1.5B参数，并确定我们的结合方法最多大致相当于单个LM基线。我们注意到，根据我们的实验中的发现，例如降低方差和较小的绩效改善，在特定情况下进行包装和修剪的其他积极影响。