人们对如何分配其有限的计算资源的决定对人类智慧至关重要。这种元认知能力的一个重要组成部分决定是否继续考虑该做什么并继续下去决定。在这里，我们展示人们通过学习和反向工程师来获得这种能力的潜在的学习机制。使用外在人类规划的过程跟踪范式，我们发现人们迅速适应他们对规划成本和利益的规划。为了发现潜在的元认知学习机制，我们增强了一组具有元认知功能的加强学习模型，并执行了贝叶斯模型选择。我们的结果表明，调整规划量的元认知能力可能通过策略梯度机制来学习，该决策机制是通过传达规划价值的元认知伪奖励引导的。

Real-world tasks are largely composed of multiple models, each performing a sub-task in a larger chain of tasks, i.e., using the output from a model as input for another model in a multi-model pipeline. A model like MATRa performs the task of Crosslingual Transliteration in two stages, using English as an intermediate transliteration target when transliterating between two indic languages. We propose a novel distillation technique, EPIK, that condenses two-stage pipelines for hierarchical tasks into a single end-to-end model without compromising performance. This method can create end-to-end models for tasks without needing a dedicated end-to-end dataset, solving the data scarcity problem. The EPIK model has been distilled from the MATra model using this technique of knowledge distillation. The MATra model can perform crosslingual transliteration between 5 languages - English, Hindi, Tamil, Kannada and Bengali. The EPIK model executes the task of transliteration without any intermediate English output while retaining the performance and accuracy of the MATra model. The EPIK model can perform transliteration with an average CER score of 0.015 and average phonetic accuracy of 92.1%. In addition, the average time for execution has reduced by 54.3% as compared to the teacher model and has a similarity score of 97.5% with the teacher encoder. In a few cases, the EPIK model (student model) can outperform the MATra model (teacher model) even though it has been distilled from the MATra model.

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.

神经架构搜索（NAS）在神经网络（NN）的设计和部署方面具有显着提高的生产率。由于NAS通常通过部分或完全训练多个模型来评估多个模型，因此提高的生产率是以大量碳足迹为代价的。为了减轻这种昂贵的训练例程，零击/成本代理在初始化时分析了NN以产生分数，这与其真正的准确性高度相关。零成本代理目前是由专家设计的，这些专家对可能的算法，数据集和神经体系结构设计空间进行了多个经验测试。这降低了生产率，并且是对零成本代理设计的一种不可持续的方法，因为深度学习用例本质上多样化。此外，现有的零成本代理无法跨越神经体系结构设计空间。在本文中，我们提出了一个基因编程框架，以自动化发现零成本代理以进行神经体系结构评分。我们的方法有效地发现了一个可解释且可推广的零成本代理，该代理在NASBENCH-2010和网络设计空间（NDS）的所有数据集和搜索空间上提供了最高得分 - 准确性的相关性。我们认为，这项研究表明了自动发现可以跨网络体系结构设计空间，数据集和任务的零成本代理的有希望的方向。

我们旨在通过引入全面的分布式深度学习（DDL）探索器来解决此问题，该研究人员可以确定DDL在公共云上运行时遭受的各种执行“失速”。我们已经通过扩展先前的工作来估算两种类型的通信失速 - 互连和网络摊位来实现剖面。我们使用Profiler培训流行的DNN模型来表征各种AWS GPU实例，并列出了用户做出明智决定的优势和缺点。我们观察到，较昂贵的GPU实例可能不是所有DNN型号的性能最多，并且AWS可能会在次优的硬件互连资源分配次优。具体而言，与单个实例的培训相比，机内互连可以引入高达90％的DNN培训时间和网络连接的实例的通信开销，而与网络连接的实例可能会遭受高达5倍的速度。此外，我们对DNN宏观特征的影响进行建模，例如层的数量和通信摊位上的梯度数量。最后，我们为用户提出了一个基于衡量的建议模型，以降低DDL的公共云货币成本。

音译是NLP域中的一项任务，其中输出单词是使用任何外语字母编写的类似单词。如今，该系统已针对多种语言对开发，涉及英语作为源或目标单词，并在Google Translate和聊天机器人等多个地方部署。但是，在指示语言的领域进行的研究很少进行，将其译为其他指示语言。本文展示了一个基于变压器（具有一些修改）的多语言模型，该模型比该域中的所有现有模型都可以显着更高的性能和准确性，并且比最先进的模型获得了更好的结果。本文显示了一个模型，该模型可以在以下五种语言之间进行任何一对 - 英语，印地语，孟加拉语，卡纳达语和泰米尔语之间的音译。它适用于语言在任何书面任务中都是通信的障碍的情况。该模型击败了最先进的（对于上述五种语言中的所有对 - 英语，印地语，孟加拉语，卡纳达语和泰米尔语），并获得了80.7％的前1位准确性得分，比比当前最佳结果。此外，该模型在语音准确性方面达到了93.5％（音译主要是基于语音/声音的任务）。

通过捕获文本表示的组成性，大型语言模型在各种自然语言处理任务中取得了成功。尽管它们取得了巨大的成功，但这些向量表示未能捕获惯用多字表达式（MWES）的含义。在本文中，我们专注于使用二进制分类检测惯用表达式。我们使用一个数据集，该数据集包括英语和葡萄牙语中MWE的字面用法和惯用性。此后，我们在两个不同的设置中执行分类：零射门和一个镜头，以确定给定的句子是否包含成语。 n个任务的n射击分类是由训练和测试集之间的n个常见成语数定义的。在本文中，我们在设置中训练多个大型语言模型，并在零射击设置中获得0.73的F1分数（宏），一个射击设置为0.85的F1分数（宏）。可以在https://github.com/ashwinpathak20/idiomation_detection_using_using_few_shot_learning上找到我们工作的实现。

最近的神经结构搜索（NAS）解决方案已经生产出令人印象深刻的结果培训超级网络，然后派生子网，A.K.A.儿童模型从预定义的搜索空间中胜过专家制作的模型。可以为资源受限的边缘设备选择高效且强大的子网，允许它们在野外执行良好。然而，构建任意架构的超级网络仍然是一种挑战，通常可以防止采用这些方法。为了解决这一挑战，我们呈现Bootstrapnas，这是一种自动生成NAS的超网络的软件框架。 Bootstrapnas从流行的体系结构，例如Reset-50或有效的自定义设计中获取预先训练的模型，并自动创建超网络，然后使用最先进的NAS技术来训练超级网络，导致子网，显着优于给定的预先训练模型。我们通过从任意模型存储库生成超级网络并提供结果的超网络来展示解决方案，以获得结果的再现性。

Our paper aims to analyze political polarization in US political system using Language Models, and thereby help candidates make an informed decision. The availability of this information will help voters understand their candidates views on the economy, healthcare, education and other social issues. Our main contributions are a dataset extracted from Wikipedia that spans the past 120 years and a Language model based method that helps analyze how polarized a candidate is. Our data is divided into 2 parts, background information and political information about a candidate, since our hypothesis is that the political views of a candidate should be based on reason and be independent of factors such as birthplace, alma mater, etc. We further split this data into 4 phases chronologically, to help understand if and how the polarization amongst candidates changes. This data has been cleaned to remove biases. To understand the polarization we begin by showing results from some classical language models in Word2Vec and Doc2Vec. And then use more powerful techniques like the Longformer, a transformer based encoder, to assimilate more information and find the nearest neighbors of each candidate based on their political view and their background.

Object movement identification is one of the most researched problems in the field of computer vision. In this task, we try to classify a pixel as foreground or background. Even though numerous traditional machine learning and deep learning methods already exist for this problem, the two major issues with most of them are the need for large amounts of ground truth data and their inferior performance on unseen videos. Since every pixel of every frame has to be labeled, acquiring large amounts of data for these techniques gets rather expensive. Recently, Zhao et al. [1] proposed one of a kind Arithmetic Distribution Neural Network (ADNN) for universal background subtraction which utilizes probability information from the histogram of temporal pixels and achieves promising results. Building onto this work, we developed an intelligent video surveillance system that uses ADNN architecture for motion detection, trims the video with parts only containing motion, and performs anomaly detection on the trimmed video.