我们提出了一项实证研究，以适应现有的经过验证的文本对文本模型，以备长期输入。通过沿预训练管道的三个轴的全面研究 - 模型架构，优化目标和训练式语料库，我们提出了一种有效的食谱，以从现有的短篇小说模型中构建长篇小说模型。具体而言，我们用汇总仪的块关注替换了变压器中的全部注意力，并使用蒙版的跨度预测任务为模型预算，长度不同。就训练训练的语料库而言，我们发现，与使用通常在其域覆盖范围中通常受到限制的现有长文档语料库相比，使用大型开放域语料库的随机串联的短篇小说可以提高性能。通过这些发现，我们建立了一个长篇文本模型，该模型可以在长篇文本质量检查任务上实现竞争性能，并在五个长文本摘要数据集上建立新的最新技术，通常优于先前的方法，具有较大的模型大小。

一种自然语言模型才能理解小说，例如《指环王》？除其他外，这种模型必须能够：（a）识别和记录新字符（实体）及其在文本中引入的属性，以及（b）确定对先前介绍的字符的后续引用并更新其属性。实体跟踪问题对于语言理解至关重要，因此，对于NLP中的各种下游应用程序，例如提问，摘要。在本论文中，我们关注有关促进实体跟踪模型使用的两个关键问题：（i）将实体跟踪模型缩放到长文档，例如小说和（ii）将实体跟踪集成到语言模型中。最近将语言技术应用于长文档，最近引起了兴趣，但是计算限制是扩大当前方法的重要瓶颈。在本论文中，我们认为可以通过代表具有较丰富的，固定维的矢量表示的实体来开发计算高效的实体跟踪模型，并通过审计的语言模型和利用实体的短暂性质来开发。我们还主张将实体跟踪集成到语言模型中，因为它将允许：（i）鉴于当前无处不在的NLP应用程序中使用预处理的语言模型的应用程序，并且（ii）更容易采用，因为更容易交换要容易得多一个新的预审前的语言模型，而不是集成单独的独立实体跟踪模型。

语言模型既展示了定量的改进，又展示了新的定性功能，随着规模的增加。尽管它们具有潜在的变革性影响，但这些新能力的特征却很差。为了为未来的研究提供信息，为破坏性的新模型能力做准备，并改善社会有害的效果，至关重要的是，我们必须了解目前和近乎未来的能力和语言模型的局限性。为了应对这一挑战，我们介绍了超越模仿游戏基准（Big Bench）。 Big Bench目前由204个任务组成，由132家机构的442位作者贡献。任务主题是多样的，从语言学，儿童发展，数学，常识性推理，生物学，物理学，社会偏见，软件开发等等。 Big-Bench专注于被认为超出当前语言模型的功能的任务。我们评估了OpenAI的GPT型号，Google内部密集变压器体系结构和大型基础上的开关稀疏变压器的行为，跨越了数百万到数十亿个参数。此外，一个人类专家评估者团队执行了所有任务，以提供强大的基准。研究结果包括：模型性能和校准都随规模改善，但绝对的术语（以及与评估者的性能相比）；在模型类中的性能非常相似，尽管带有稀疏性。逐渐和预测的任务通常涉及大量知识或记忆成分，而在临界规模上表现出“突破性”行为的任务通常涉及多个步骤或组成部分或脆性指标；社交偏见通常会随着含糊不清的环境而随着规模而增加，但这可以通过提示来改善。

Monitoring water is a complex task due to its dynamic nature, added pollutants, and land build-up. The availability of high-resolu-tion data by Sentinel-2 multispectral products makes implementing remote sensing applications feasible. However, overutilizing or underutilizing multispectral bands of the product can lead to inferior performance. In this work, we compare the performances of ten out of the thirteen bands available in a Sentinel-2 product for water segmentation using eight machine learning algorithms. We find that the shortwave infrared bands (B11 and B12) are the most superior for segmenting water bodies. B11 achieves an overall accuracy of $71\%$ while B12 achieves $69\%$ across all algorithms on the test site. We also find that the Support Vector Machine (SVM) algorithm is the most favourable for single-band water segmentation. The SVM achieves an overall accuracy of $69\%$ across the tested bands over the given test site. Finally, to demonstrate the effectiveness of choosing the right amount of data, we use only B11 reflectance data to train an artificial neural network, BandNet. Even with a basic architecture, BandNet is proportionate to known architectures for semantic and water segmentation, achieving a $92.47$ mIOU on the test site. BandNet requires only a fraction of the time and resources to train and run inference, making it suitable to be deployed on web applications to run and monitor water bodies in localized regions. Our codebase is available at https://github.com/IamShubhamGupto/BandNet.

In this paper, we discuss an imitation learning based method for reducing the calibration error for a mixed reality system consisting of a vision sensor and a projector. Unlike a head mounted display, in this setup, augmented information is available to a human subject via the projection of a scene into the real world. Inherently, the camera and projector need to be calibrated as a stereo setup to project accurate information in 3D space. Previous calibration processes require multiple recording and parameter tuning steps to achieve the desired calibration, which is usually time consuming process. In order to avoid such tedious calibration, we train a CNN model to iteratively correct the extrinsic offset given a QR code and a projected pattern. We discuss the overall system setup, data collection for training, and results of the auto-correction model.

Language-conditioned policies allow robots to interpret and execute human instructions. Learning such policies requires a substantial investment with regards to time and compute resources. Still, the resulting controllers are highly device-specific and cannot easily be transferred to a robot with different morphology, capability, appearance or dynamics. In this paper, we propose a sample-efficient approach for training language-conditioned manipulation policies that allows for rapid transfer across different types of robots. By introducing a novel method, namely Hierarchical Modularity, and adopting supervised attention across multiple sub-modules, we bridge the divide between modular and end-to-end learning and enable the reuse of functional building blocks. In both simulated and real world robot manipulation experiments, we demonstrate that our method outperforms the current state-of-the-art methods and can transfer policies across 4 different robots in a sample-efficient manner. Finally, we show that the functionality of learned sub-modules is maintained beyond the training process and can be used to introspect the robot decision-making process. Code is available at https://github.com/ir-lab/ModAttn.

We propose SparseFusion, a sparse view 3D reconstruction approach that unifies recent advances in neural rendering and probabilistic image generation. Existing approaches typically build on neural rendering with re-projected features but fail to generate unseen regions or handle uncertainty under large viewpoint changes. Alternate methods treat this as a (probabilistic) 2D synthesis task, and while they can generate plausible 2D images, they do not infer a consistent underlying 3D. However, we find that this trade-off between 3D consistency and probabilistic image generation does not need to exist. In fact, we show that geometric consistency and generative inference can be complementary in a mode-seeking behavior. By distilling a 3D consistent scene representation from a view-conditioned latent diffusion model, we are able to recover a plausible 3D representation whose renderings are both accurate and realistic. We evaluate our approach across 51 categories in the CO3D dataset and show that it outperforms existing methods, in both distortion and perception metrics, for sparse-view novel view synthesis.

The people in the world who are hearing impaired face many obstacles in communication and require an interpreter to comprehend what a person is saying. There has been constant scientific research and the existing models lack the ability to make accurate predictions. So we propose a deep learning model trained on ASL i.e. American Sign Language which will take actions in the form of ASL as input and translate it into text. To achieve the translation a Convolution Neural Network model and a transfer learning model based on the VGG16 architecture are used. There has been an improvement in accuracy from 94% of CNN to 98.7% of Transfer Learning, an improvement of 5%. An application with the deep learning model integrated has also been built.

Recent video+language datasets cover domains where the interaction is highly structured, such as instructional videos, or where the interaction is scripted, such as TV shows. Both of these properties can lead to spurious cues to be exploited by models rather than learning to ground language. In this paper, we present GrOunded footbAlL commentaries (GOAL), a novel dataset of football (or `soccer') highlights videos with transcribed live commentaries in English. As the course of a game is unpredictable, so are commentaries, which makes them a unique resource to investigate dynamic language grounding. We also provide state-of-the-art baselines for the following tasks: frame reordering, moment retrieval, live commentary retrieval and play-by-play live commentary generation. Results show that SOTA models perform reasonably well in most tasks. We discuss the implications of these results and suggest new tasks for which GOAL can be used. Our codebase is available at: https://gitlab.com/grounded-sport-convai/goal-baselines.

Industries must follow government rules and regulations around the world to classify products when assessing duties and taxes for international shipment. Harmonized System (HS) is the most standardized numerical method of classifying traded products among industry classification systems. A hierarchical ensemble model comprising of Bert- transformer, NER, distance-based approaches, and knowledge-graphs have been developed to address scalability, coverage, ability to capture nuances, automation and auditing requirements when classifying unknown text-descriptions as per HS method.