赤道等离子体气泡（EPB）是低密度血浆的羽毛，它们从F层的底部升至Exosphere。 EPB是无线电波闪烁的已知原因，可以降低与航天器的通信。我们构建了一个随机的森林回归剂，以预测和预测IBI处理器在船上检测到的EPB [0-1]的可能性。我们使用从2014年到2021年的8年群数据，并将数据从时间序列转换为5维空间，该空间包括纬度，经度，MLT，年份和年度。我们还增加了KP，F10.7厘米和太阳风速。关于地理位置，当地时间，季节和太阳活动的EPB的观察主要与现有工作一致，而链接的地磁活动尚不清楚。该预测的精度为88％，并且在EPB特异性时空尺度上的性能很好。这证明了XGBoost方法能够成功捕获群EPB的气候和每日变异性。由于电离层内的局部和随机特征，捕获每日方差长期以来一直逃避研究人员。我们利用Shapley值来解释该模型并深入了解EPB的物理学。我们发现，随着太阳能速度的增加，EPB的概率降低。我们还确定了EPB概率周围的尖峰。这两个见解直接源自XGBoost和Shapley技术。

我们提出了分支机构 - 培训 - 合并（BTM），这是一种用于对大型语言模型（LLMS）平行训练的沟通效率算法。我们表明，有可能在不同的数据子集上独立训练新的LLMS的子部分，从而消除了训练LLMS当前所需的大量多节点同步。 BTM学习了一组独立的专家LMS（ELMS），每个LMS（ELMS）专门针对不同的文本领域，例如科学或法律文本。可以添加和删除这些榆树以更新数据覆盖范围，并结合概括为新域，或者平均折叠回到单个LM以进行有效推理。通过从当前集合中的（混合物）分支，进一步训练新域的数据参数，然后将结果模型归还到该集合以备将来使用，从而学习新的榆树。实验表明，在控制训练成本时，与GPT型变压器LMS相比，BTM改善了与GPT风格的变压器LMS相比，可以改善内部和外部困惑。通过广泛的分析，我们表明这些结果对不同的ELM初始化方案是可靠的，但需要专家领域的专业化。具有随机数据拆分的LM合奏表现不佳。我们还提出了将BTM缩放到64个领域的新语料库（总计192B居民分开的代币）的研究；所得的LM（22.4B总参数）以及经过2.5倍计算训练的变压器LM。这些收益随域的数量增长，表明可以使用更具侵略性的并行性来有效地在未来的工作中训练更大的模型。

我们介绍了Galaxy动物园贴花：SDSS DR8占地面积的星系中的黑色能量相机传统调查图像的详细视觉形态学分类。更深的贴花图像（R = 23.6与SDSS的r = 22.2）显示螺旋臂，弱杆和在SDSS成像中未见的潮汐功能。为了最佳利用较大的贴花图像，志愿者从一套新的答案中选择，旨在提高对合并和酒吧的敏感性。 Galaxy动物园志愿者提供750万个单独的分类超过314,000个星系。 140,000个星系收到至少30分类，足以准确测量像条状的详细的形态，其余的收到约5.所有分类都用于培训贝叶斯卷积神经网络的集合（一种最先进的深度学习方法）预测所有314,000个星系的详细形态的后海外。当衡量自信的志愿者分类时，每个问题的网络大约有99％。形态学是每个星系的基本特征;我们的人机和机器分类是理解星系如何发展的准确和详细资源。

We propose a method for training a deterministic deep model that can find and reject out of distribution data points at test time with a single forward pass. Our approach, deterministic uncertainty quantification (DUQ), builds upon ideas of RBF networks. We scale training in these with a novel loss function and centroid updating scheme and match the accuracy of softmax models. By enforcing detectability of changes in the input using a gradient penalty, we are able to reliably detect out of distribution data. Our uncertainty quantification scales well to large datasets, and using a single model, we improve upon or match Deep Ensembles in out of distribution detection on notable difficult dataset pairs such as Fashion-MNIST vs. MNIST, and CIFAR-10 vs. SVHN.

In this work we introduce reinforcement learning techniques for solving lexicographic multi-objective problems. These are problems that involve multiple reward signals, and where the goal is to learn a policy that maximises the first reward signal, and subject to this constraint also maximises the second reward signal, and so on. We present a family of both action-value and policy gradient algorithms that can be used to solve such problems, and prove that they converge to policies that are lexicographically optimal. We evaluate the scalability and performance of these algorithms empirically, demonstrating their practical applicability. As a more specific application, we show how our algorithms can be used to impose safety constraints on the behaviour of an agent, and compare their performance in this context with that of other constrained reinforcement learning algorithms.

High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs. However, extracting representative features from high content images that can capture subtle nuances in phenotypes remains challenging. The lack of high-quality labels makes it difficult to achieve satisfactory results with supervised deep learning. Self-Supervised learning methods, which learn from automatically generated labels has shown great success on natural images, offer an attractive alternative also to microscopy images. However, we find that self-supervised learning techniques underperform on high content imaging assays. One challenge is the undesirable domain shifts present in the data known as batch effects, which may be caused by biological noise or uncontrolled experimental conditions. To this end, we introduce Cross-Domain Consistency Learning (CDCL), a novel approach that is able to learn in the presence of batch effects. CDCL enforces the learning of biological similarities while disregarding undesirable batch-specific signals, which leads to more useful and versatile representations. These features are organised according to their morphological changes and are more useful for downstream tasks - such as distinguishing treatments and mode of action.

Machine learning methods have seen increased application to geospatial environmental problems, such as precipitation nowcasting, haze forecasting, and crop yield prediction. However, many of the machine learning methods applied to mosquito population and disease forecasting do not inherently take into account the underlying spatial structure of the given data. In our work, we apply a spatially aware graph neural network model consisting of GraphSAGE layers to forecast the presence of West Nile virus in Illinois, to aid mosquito surveillance and abatement efforts within the state. More generally, we show that graph neural networks applied to irregularly sampled geospatial data can exceed the performance of a range of baseline methods including logistic regression, XGBoost, and fully-connected neural networks.

Large "instruction-tuned" language models (finetuned to respond to instructions) have demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they depend heavily on human-written instruction data that is limited in quantity, diversity, and creativity, therefore hindering the generality of the tuned model. We introduce Self-Instruct, a framework for improving the instruction-following capabilities of pretrained language models by bootstrapping off its own generations. Our pipeline generates instruction, input, and output samples from a language model, then prunes them before using them to finetune the original model. Applying our method to vanilla GPT3, we demonstrate a 33% absolute improvement over the original model on Super-NaturalInstructions, on par with the performance of InstructGPT_001, which is trained with private user data and human annotations. For further evaluation, we curate a set of expert-written instructions for novel tasks, and show through human evaluation that tuning GPT3 with Self-Instruct outperforms using existing public instruction datasets by a large margin, leaving only a 5% absolute gap behind InstructGPT_001. Self-Instruct provides an almost annotation-free method for aligning pre-trained language models with instructions, and we release our large synthetic dataset to facilitate future studies on instruction tuning.

Large-scale models combining text and images have made incredible progress in recent years. However, they can still fail at tasks requiring compositional knowledge, such as correctly picking out a red cube from a picture of multiple shapes. We examine the ability of CLIP (Radford et al., 2021), to caption images requiring compositional knowledge. We implement five compositional language models to probe the kinds of structure that CLIP may be using, and develop a novel training algorithm, Compositional Skipgram for Images (CoSI), to train these models. We look at performance in attribute-based tasks, requiring the identification of a particular combination of attribute and object (such as "red cube"), and in relational settings, where the spatial relation between two shapes (such as "cube behind sphere") must be identified. We find that in some conditions, CLIP is able to learn attribute-object labellings, and to generalize to unseen attribute-object combinations. However, we also see evidence that CLIP is not able to bind features together reliably. Moreover, CLIP is not able to reliably learn relations between objects, whereas some compositional models are able to learn these perfectly. Of the five models we developed, none were able to generalize to unseen relations.

Prior work has shown that it is possible to expand pretrained Masked Language Models (MLMs) to new languages by learning a new set of embeddings, while keeping the transformer body frozen. Despite learning a small subset of parameters, this approach is not compute-efficient, as training the new embeddings requires a full forward and backward pass over the entire model. In this work, we propose mini-model adaptation, a compute-efficient alternative that builds a shallow mini-model from a fraction of a large model's parameters. New language-specific embeddings can then be efficiently trained over the mini-model, and plugged into the aligned large model for rapid cross-lingual transfer. We explore two approaches to learn mini-models: MiniJoint, which jointly pretrains the primary model and the mini-model using a single transformer with a secondary MLM head at a middle layer; and MiniPost, where we start from a regular pretrained model and build a mini-model by extracting and freezing a few layers and learning a small number of parameters on top. Experiments on XNLI, MLQA and PAWS-X show that mini-model adaptation matches the performance of the standard approach using up to 2.4x less compute.