To facilitate research on text generation, this paper presents a comprehensive and unified library, TextBox 2.0, focusing on the use of pre-trained language models (PLMs). To be comprehensive, our library covers $13$ common text generation tasks and their corresponding $83$ datasets and further incorporates $45$ PLMs covering general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight PLMs. We also implement $4$ efficient training strategies and provide $4$ generation objectives for pre-training new PLMs from scratch. To be unified, we design the interfaces to support the entire research pipeline (from data loading to training and evaluation), ensuring that each step can be fulfilled in a unified way. Despite the rich functionality, it is easy to use our library, either through the friendly Python API or command line. To validate the effectiveness of our library, we conduct extensive experiments and exemplify four types of research scenarios. The project is released at the link: https://github.com/RUCAIBox/TextBox.
translated by 谷歌翻译
Recent work on 4D point cloud sequences has attracted a lot of attention. However, obtaining exhaustively labeled 4D datasets is often very expensive and laborious, so it is especially important to investigate how to utilize raw unlabeled data. However, most existing self-supervised point cloud representation learning methods only consider geometry from a static snapshot omitting the fact that sequential observations of dynamic scenes could reveal more comprehensive geometric details. And the video representation learning frameworks mostly model motion as image space flows, let alone being 3D-geometric-aware. To overcome such issues, this paper proposes a new 4D self-supervised pre-training method called Complete-to-Partial 4D Distillation. Our key idea is to formulate 4D self-supervised representation learning as a teacher-student knowledge distillation framework and let the student learn useful 4D representations with the guidance of the teacher. Experiments show that this approach significantly outperforms previous pre-training approaches on a wide range of 4D point cloud sequence understanding tasks including indoor and outdoor scenarios.
translated by 谷歌翻译
Sky-image-based solar forecasting using deep learning has been recognized as a promising approach in reducing the uncertainty in solar power generation. However, one of the biggest challenges is the lack of massive and diversified sky image samples. In this study, we present a comprehensive survey of open-source ground-based sky image datasets for very short-term solar forecasting (i.e., forecasting horizon less than 30 minutes), as well as related research areas which can potentially help improve solar forecasting methods, including cloud segmentation, cloud classification and cloud motion prediction. We first identify 72 open-source sky image datasets that satisfy the needs of machine/deep learning. Then a database of information about various aspects of the identified datasets is constructed. To evaluate each surveyed datasets, we further develop a multi-criteria ranking system based on 8 dimensions of the datasets which could have important impacts on usage of the data. Finally, we provide insights on the usage of these datasets for different applications. We hope this paper can provide an overview for researchers who are looking for datasets for very short-term solar forecasting and related areas.
translated by 谷歌翻译
Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. Popular loss functions, including the cross-entropy and dice losses, often fall short of boundary detection, thereby limiting high-resolution downstream applications such as automated diagnoses and procedures. We developed a novel loss function that is tailored to reflect the boundary information to enhance the boundary detection. As the contrast between segmentation and background regions along the classification boundary naturally induces heterogeneity over the pixels, we propose the piece-wise two-sample t-test augmented (PTA) loss that is infused with the statistical test for such heterogeneity. We demonstrate the improved boundary detection power of the PTA loss compared to benchmark losses without a t-test component.
translated by 谷歌翻译
In recent years, Multi-Agent Path Finding (MAPF) has attracted attention from the fields of both Operations Research (OR) and Reinforcement Learning (RL). However, in the 2021 Flatland3 Challenge, a competition on MAPF, the best RL method scored only 27.9, far less than the best OR method. This paper proposes a new RL solution to Flatland3 Challenge, which scores 125.3, several times higher than the best RL solution before. We creatively apply a novel network architecture, TreeLSTM, to MAPF in our solution. Together with several other RL techniques, including reward shaping, multiple-phase training, and centralized control, our solution is comparable to the top 2-3 OR methods.
translated by 谷歌翻译
医学视觉和语言预训练提供了一种可行的解决方案，可以从医学图像和文本中提取有效的视觉和语言表示。但是，很少有研究专门研究该领域，以促进医学视觉和语言理解。在本文中，我们提出了一种自我监督的学习范式，该学习范式使用多模式掩盖的自动编码器（M $^3 $ ae），通过从随机掩盖的图像和文本中重新构造缺失的像素和代币来学习跨模式域知识。有三个关键设计可以使这种简单的方法起作用。首先，考虑到视觉和语言的不同信息密度，我们为输入图像和文本采用不同的掩蔽比，其中将较大的掩模比用于图像。其次，我们使用来自不同层的视觉和文本特征来执行重建，以处理视觉和语言中不同级别的抽象。第三，我们为视觉和语言解码器开发了不同的设计（即，视觉的变压器和语言的多层感知器）。为了进行全面的评估并促进进一步的研究，我们构建了包括三个任务的医学视觉和语言基准。实验结果证明了我们方法的有效性，在所有下游任务上都取得了最新的结果。此外，我们进行进一步的分析，以更好地验证方法的不同组成部分和预训练的各种设置。源代码可在〜\ url {https://github.com/zhjohnchan/m3ae}中获得。
translated by 谷歌翻译
我们介绍了软件Robustar的初步发布，该版本旨在通过数据驱动的视角提高视觉分类机器学习模型的鲁棒性。基于最近的理解，即缺乏机器学习模型的鲁棒性是该模型学习虚假特征的趋势，我们旨在通过在训练前从数据中删除数据的杂种特征来从数据角度解决此问题。特别是，我们介绍了一种软件，可以通过允许用户注释图像像素级别的虚假功能来帮助用户更好地为训练图像分类模型准备数据。为了促进这一过程，我们的软件还利用了最近的进步来帮助识别值得关注的潜在图像和像素，并通过新注释的数据继续培训。我们的软件托管在GitHub存储库https://github.com/haohanwang/robustar。
translated by 谷歌翻译
对于许多在线平台（例如，视频共享网站，电子商务系统），学习动态用户的偏好已成为越来越重要的组成部分，以提出顺序建议。先前的工作已经做出了许多努力，以基于各种体系结构（例如，经常性的神经网络和自我注意机制）对用户交互序列进行建模项目项目过渡。最近出现的图形神经网络还用作有用的骨干模型，可在顺序推荐方案中捕获项目依赖性。尽管它们有效，但现有的方法却远远集中在具有单一相互作用类型的项目序列表示上，因此仅限于捕获用户和项目之间的动态异质关系结构（例如，页面视图，添加最佳选择，购买，购买）。为了应对这一挑战，我们设计了多行为超毛力增强的变压器框架（MBHT），以捕获短期和长期跨型行为依赖性。具体而言，多尺度变压器配备了低级别的自我注意力，可从细粒度和粗粒水平的共同编码行为感知的顺序模式。此外，我们将全局多行为依赖性纳入HyperGraph神经体系结构中，以自定义的方式捕获层次长期项目相关性。实验结果证明了我们MBHT在不同环境中的各种最新推荐解决方案的优势。进一步的消融研究证明了我们的模型设计和新MBHT框架的好处的有效性。我们的实施代码在以下网址发布：https：//github.com/yuh-yang/mbht-kdd22。
translated by 谷歌翻译
微小的机器学习（Tinyml）是严格限制的资源和功率的AI工作负载，是一个重要而挑战性的话题。该简介首先提出了一个极其微小的主链，用于为各种视觉任务构建高效率CNN模型。然后，专门设计的神经协调员（NCP）与MCU互连以构建一个超低功率Tinyml系统，该系统将所有功能和权重存储在芯片上，并完全消除芯片内存储器访问中的延迟和功耗。此外，进一步提出了一个特定的指令集，以实现敏捷开发和快速部署。广泛的实验表明，基于我们的模型，NCP和指令集的提议的Tinyml系统可产生相当大的精确性，并在30FPS以实现对象检测和识别时实现了创纪录的160MW的超低功率。演示视频可在\ url {https://www.youtube.com/watch?v=mizpxtj-9ey}上获得。
translated by 谷歌翻译
太阳能的间歇性质挑战了光伏（PV）在电网中的大规模集成。使用深度学习的基于天空图像的太阳预测已被认为是预测短期波动的一种有希望的方法。但是，对于基于图像的太阳预测，几乎没有公开可用的标准化基准数据集，这限制了不同预测模型的比较和预测方法的探索。为了填补这些空白，我们介绍了Skipp'd-天空图像和光伏发电数据集。该数据集包含三年（2017-2019）的质量控制下采样的天空图像和PV发电数据，这些数据可用于使用深度学习的短期太阳能预测。此外，为了支持研究的灵活性，我们还提供了高分辨率，高频天空图像和PV发电数据以及并发的Sky录像。我们还包括一个包含数据处理脚本和基线模型实现的代码库，以供研究人员重现我们以前的工作并加速其在太阳预测中的研究。
translated by 谷歌翻译