Recent advances in deep learning have led to the development of models approaching human level of accuracy. However, healthcare remains an area lacking in widespread adoption. The safety-critical nature of healthcare results in a natural reticence to put these black-box deep learning models into practice. In this paper, we explore interpretable methods for a clinical decision support system, sleep staging, based on physiological signals such as EEG, EOG, and EMG. A recent work has shown sleep staging using simple models and an exhaustive set of features can perform nearly as well as deep learning approaches but only for certain datasets. Moreover, the utility of these features from a clinical standpoint is unclear. On the other hand, the proposed framework, NormIntSleep shows that by representing deep learning embeddings using normalized features, great performance can be obtained across different datasets. NormIntSleep performs 4.5% better than the exhaustive feature-based approach and 1.5% better than other representation learning approaches. An empirical comparison between the utility of the interpretations of these models highlights the improved alignment with clinical expectations when performance is traded-off slightly.

最近基于深度学习的临床决策支持系统的准确性是有希望的。但是，缺乏模型可解释性仍然是医疗保健中人工智能广泛采用的障碍。使用睡眠作为案例研究，我们提出了一种可推广的方法，将临床解释性与黑盒深度学习得出的高精度相结合。多聚词（PSG）的临床医生确定的睡眠阶段仍然是评估睡眠质量的金标准。但是，专家的PSG手册注释既昂贵又过时。我们建议使用嵌入式，规则和功能来读取PSG的农奴，可解释的睡眠分期。农奴通过从AASM手册中得出的有意义的特征来解释分类的睡眠阶段，用于睡眠和相关事件的评分。在农奴中，从卷积和复发性神经网络的混合体获得的嵌入被转移到可解释的特征空间。这些代表性的可解释功能用于训练简单的模型，例如浅决策树进行分类。模型结果将在两个公开可用的数据集上进行验证。农奴超过了可解释的睡眠分期的当前最新时间。 Serf使用梯度增压树作为分类器，在当前最新的黑盒模型的2％以内，获得了0.766 $ \ kappa $和0.870 AUC-ROC。

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.

最大化模型准确性的常规配方是（1）具有各种超参数的多个模型，以及（2）选择在固定验证集中表现最佳的单个模型，从而丢弃其余部分。在本文中，我们在微调大型预训练的模型的背景下重新审视了该过程的第二步，其中微调模型通常位于单个低误差盆地中。我们表明，平均多种模型的权重以不同的超参数配置进行了微调通常提高准确性和鲁棒性。与传统的合奏不同，我们可能会平均许多模型，而不会产生任何其他推理或记忆成本 - 我们将结果称为“模型汤”。当微调大型预训练的模型，例如夹子，Align和VIT-G在JFT上预先训练的VIT-G时，我们的汤食谱可为ImageNet上的超参数扫描中的最佳模型提供显着改进。所得的VIT-G模型在Imagenet上达到90.94％的TOP-1准确性，实现了新的最新状态。此外，我们表明，模型汤方法扩展到多个图像分类和自然语言处理任务，改善分发性能，并改善新下游任务的零局部性。最后，我们通过分析将权重平衡和与logit浓度的性能相似与预测的损失和信心的平坦度联系起来，并经过经验验证这种关系。代码可从https://github.com/mlfoundations/model-soups获得。

放射线学使用定量医学成像特征来预测临床结果。目前，在新的临床应用中，必须通过启发式试验和纠正过程手动完成各种可用选项的最佳放射组方法。在这项研究中，我们提出了一个框架，以自动优化每个应用程序的放射线工作流程的构建。为此，我们将放射线学作为模块化工作流程，并为每个组件包含大量的常见算法。为了优化每个应用程序的工作流程，我们使用随机搜索和结合使用自动化机器学习。我们在十二个不同的临床应用中评估我们的方法，从而在曲线下导致以下区域：1）脂肪肉瘤（0.83）； 2）脱粘型纤维瘤病（0.82）; 3）原发性肝肿瘤（0.80）; 4）胃肠道肿瘤（0.77）； 5）结直肠肝转移（0.61）; 6）黑色素瘤转移（0.45）; 7）肝细胞癌（0.75）; 8）肠系膜纤维化（0.80）; 9）前列腺癌（0.72）； 10）神经胶质瘤（0.71）; 11）阿尔茨海默氏病（0.87）;和12）头颈癌（0.84）。我们表明，我们的框架具有比较人类专家的竞争性能，优于放射线基线，并且表现相似或优于贝叶斯优化和更高级的合奏方法。最后，我们的方法完全自动优化了放射线工作流的构建，从而简化了在新应用程序中对放射线生物标志物的搜索。为了促进可重复性和未来的研究，我们公开发布了六个数据集，框架的软件实施以及重现这项研究的代码。

AI正在经历范式转变，随着模型的兴起（例如Bert，Dall-E，GPT-3），这些模型经过大规模的数据训练，并且可以适应广泛的下游任务。我们称这些模型基础模型来强调其至关重要但不完整的特征。该报告提供了基础模型的机会和风险的详尽说明，包括其功能（例如语言，愿景，机器人技术，推理，人类互动）和技术原则（例如，模型架构，培训程序，数据，系统，安全，安全性，评估，理论）对其应用（例如法律，医疗保健，教育）和社会影响（例如不平等，滥用，经济和环境影响，法律和道德考虑）。尽管基础模型基于标准的深度学习和转移学习，但它们的规模导致了新的新兴能力，以及它们在许多任务中的有效性都激发了同质化。同质化提供了强大的杠杆作用，但要求谨慎，因为基础模型的缺陷均由下游的所有适应模型继承。尽管即将广泛地部署基础模型，但我们目前对它们的工作方式，失败以及由于其新兴属性的影响而缺乏清晰的了解。为了解决这些问题，我们认为基础模型的许多批判性研究都需要与他们的基本社会技术性质相称。

Designing experiments often requires balancing between learning about the true treatment effects and earning from allocating more samples to the superior treatment. While optimal algorithms for the Multi-Armed Bandit Problem (MABP) provide allocation policies that optimally balance learning and earning, they tend to be computationally expensive. The Gittins Index (GI) is a solution to the MABP that can simultaneously attain optimality and computationally efficiency goals, and it has been recently used in experiments with Bernoulli and Gaussian rewards. For the first time, we present a modification of the GI rule that can be used in experiments with exponentially-distributed rewards. We report its performance in simulated 2- armed and 3-armed experiments. Compared to traditional non-adaptive designs, our novel GI modified design shows operating characteristics comparable in learning (e.g. statistical power) but substantially better in earning (e.g. direct benefits). This illustrates the potential that designs using a GI approach to allocate participants have to improve participant benefits, increase efficiencies, and reduce experimental costs in adaptive multi-armed experiments with exponential rewards.

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.

Quadruped robots are currently used in industrial robotics as mechanical aid to automate several routine tasks. However, presently, the usage of such a robot in a domestic setting is still very much a part of the research. This paper discusses the understanding and virtual simulation of such a robot capable of detecting and understanding human emotions, generating its gait, and responding via sounds and expression on a screen. To this end, we use a combination of reinforcement learning and software engineering concepts to simulate a quadruped robot that can understand emotions, navigate through various terrains and detect sound sources, and respond to emotions using audio-visual feedback. This paper aims to establish the framework of simulating a quadruped robot that is emotionally intelligent and can primarily respond to audio-visual stimuli using motor or audio response. The emotion detection from the speech was not as performant as ERANNs or Zeta Policy learning, still managing an accuracy of 63.5%. The video emotion detection system produced results that are almost at par with the state of the art, with an accuracy of 99.66%. Due to its "on-policy" learning process, the PPO algorithm was extremely rapid to learn, allowing the simulated dog to demonstrate a remarkably seamless gait across the different cadences and variations. This enabled the quadruped robot to respond to generated stimuli, allowing us to conclude that it functions as predicted and satisfies the aim of this work.

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset will be made available upon acceptance.