在本文中,我们提出了一种与渔业相关数据的方法,该方法使我们能够通过多个可以利用众包接口的培训和生产循环在数据集上迭代标记的图像数据集。我们将算法及其结果介绍在使用海底自动水下车辆收集的两组单独的图像数据上。第一个数据集由2,026个完全未标记的图像组成,而第二个数据集由21,968张图像组成,这些图像由专家注释。我们的结果表明,使用小子集进行培训,并迭代以构建较大的标记数据,从而使我们能够收敛到带有少量迭代的完全注释数据集。即使在专家标记的数据集的情况下,该方法论的单个迭代也通过发现与鱼层相关的鱼类相关标签的其他复杂示例,也很小,或者被与水下图像相关的对比度限制所掩盖,从而改善了标签。
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The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.
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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.
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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。
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通用形态(UNIMORPH)项目是一项合作的努力,可为数百种世界语言实例化覆盖范围的标准化形态拐角。该项目包括两个主要的推力:一种无独立的特征架构,用于丰富的形态注释,并以各种语言意识到该模式的各种语言的带注释数据的类型级别资源。本文介绍了过去几年对几个方面的扩张和改进(自McCarthy等人(2020年)以来)。众多语言学家的合作努力增加了67种新语言,其中包括30种濒危语言。我们已经对提取管道进行了一些改进,以解决一些问题,例如缺少性别和马克龙信息。我们还修改了模式,使用了形态学现象所需的层次结构,例如多肢体协议和案例堆叠,同时添加了一些缺失的形态特征,以使模式更具包容性。鉴于上一个UniMorph版本,我们还通过16种语言的词素分割增强了数据库。最后,这个新版本通过通过代表来自metphynet的派生过程的实例丰富数据和注释模式来推动将衍生物形态纳入UniMorph中。
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机器学习模型可能涉及决策边界,这些界限由于对规则和规则的更新而随时间而变化,例如在贷款批准或索赔管理中。然而,在这种情况下,可能需要足够的训练数据来累积时的时间,以便重新恢复模型以反映新的决策边界。虽然已经完成了加强现有决策边界的工作,但已经介绍了ML模型的决策边界应该改变的这些方案,以便反映新规则。在本文中,我们专注于用户提供的反馈规则作为加快ML模型更新过程的方式,我们正式介绍预处理训练数据的问题,以响应于反馈规则,使得模型一旦模型在预处理的数据上被培训,其决策边界与规则更紧密地对齐。为了解决这个问题,我们提出了一种新的数据增强方法,基于反馈规则的过采样技术。使用不同ML模型和现实世界数据集的广泛实验证明了该方法的有效性,特别是增强的好处和处理许多反馈规则的能力。
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深度学习(DL)模型为各种医学成像基准挑战提供了最先进的性能,包括脑肿瘤细分(BRATS)挑战。然而,局灶性病理多隔室分割(例如,肿瘤和病变子区)的任务特别具有挑战性,并且潜在的错误阻碍DL模型转化为临床工作流程。量化不确定形式的DL模型预测的可靠性,可以实现最不确定的地区的临床审查,从而建立信任并铺平临床翻译。最近,已经引入了许多不确定性估计方法,用于DL医学图像分割任务。开发指标评估和比较不确定性措施的表现将有助于最终用户制定更明智的决策。在本研究中,我们探索并评估在Brats 2019-2020任务期间开发的公制,以对不确定量化量化(Qu-Brats),并旨在评估和排列脑肿瘤多隔室分割的不确定性估计。该公制(1)奖励不确定性估计,对正确断言产生高置信度,以及在不正确的断言处分配低置信水平的估计数,(2)惩罚导致更高百分比的无关正确断言百分比的不确定性措施。我们进一步基准测试由14个独立参与的Qu-Brats 2020的分割不确定性,所有这些都参与了主要的Brats细分任务。总体而言,我们的研究结果证实了不确定性估计提供了分割算法的重要性和互补价值,因此突出了医学图像分析中不确定性量化的需求。我们的评估代码在HTTPS://github.com/ragmeh11/qu-brats公开提供。
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超过30亿人缺乏护理皮肤病。AI诊断工具可能有助于早期皮肤癌检测;然而,大多数模型尚未在不同肤色或罕见疾病的图像上进行评估。为了解决这个问题,我们策划了多样化的皮肤科(DDI)DataSet - 这是一种具有不同皮肤色调的第一个公开的,病理证实的图像。我们展示了最先进的皮肤科AI模型在DDI上表现得很糟糕,ROC-AUC与模型的原始结果相比下降29-40%。我们发现暗肤色和罕见的疾病,在DDI数据集中提供良好,导致性能下降。此外,我们表明,无需多样化培训数据,我们表明最先进的强大培训方法无法纠正这些偏差。我们的研究结果确定了需要解决的皮肤病学AI中的重要弱点和偏见,以确保可靠应用于各种患者和所有疾病。
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疟疾是一种威胁生命的疾病,影响了数百万。基于显微镜的薄膜评估是(i)确定疟疾物种和(ii)定量高寄生虫感染的标准方法。通过机器学习(ML)对疟疾显微镜的完全自动化是一项具有挑战性的任务,因为预先准备的滑动在质量和表现方面差异很大,并且伪像通常超过相对较少的寄生虫。在这项工作中,我们描述了一个用于薄膜疟疾分析的完整,完全自动化的框架,该框架应用了ML方法,包括卷积神经网(CNN),该方法在大型且多样化的田间预先准备的薄膜数据集中进行了训练。定量和物种鉴定结果几乎足够准确地满足了耐药性监测和临床用例的混凝土需求。我们将方法和性能指标集中在现场用例要求上。我们讨论了将ML方法应用于疟疾显微镜的关键问题和重要指标。
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The recent increase in public and academic interest in preserving biodiversity has led to the growth of the field of conservation technology. This field involves designing and constructing tools that utilize technology to aid in the conservation of wildlife. In this article, we will use case studies to demonstrate the importance of designing conservation tools with human-wildlife interaction in mind and provide a framework for creating successful tools. These case studies include a range of complexities, from simple cat collars to machine learning and game theory methodologies. Our goal is to introduce and inform current and future researchers in the field of conservation technology and provide references for educating the next generation of conservation technologists. Conservation technology not only has the potential to benefit biodiversity but also has broader impacts on fields such as sustainability and environmental protection. By using innovative technologies to address conservation challenges, we can find more effective and efficient solutions to protect and preserve our planet's resources.
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