我们考虑了一个新颖的表述，即主动射击分类（AFSC）的问题，其目的是对标签预算非常限制的小规定，最初未标记的数据集进行分类。这个问题可以看作是与经典的跨托管少数射击分类（TFSC）的竞争对手范式，因为这两种方法都适用于相似的条件。我们首先提出了一种结合统计推断的方法，以及一种非常适合该框架的原始两级积极学习策略。然后，我们从TFSC领域调整了几个标准视觉基准。我们的实验表明，AFSC的潜在优势可能是很大的，与最先进的TFSC方法相比，对于同一标签预算，平均加权准确性高达10％。我们认为，这种新的范式可能会导致数据筛选学习设置的新发展和标准。

标准化流量（NF）是基于可能性的强大生成模型，能够在表达性和拖延性之间进行折衷，以模拟复杂的密度。现已建立的研究途径利用了最佳运输（OT），并寻找Monge地图，即源和目标分布之间的努力最小的模型。本文介绍了一种基于Brenier的极性分解定理的方法，该方法将任何受过训练的NF转换为更高效率的版本而不改变最终密度。我们通过学习源（高斯）分布的重新排列来最大程度地减少源和最终密度之间的OT成本。由于Euler的方程式，我们进一步限制了导致估计的Monge图的路径，将估计的Monge地图放在量化量的差异方程的空间中。所提出的方法导致几种现有模型的OT成本降低的平滑流动，而不会影响模型性能。

标记分类数据集意味着定义类和相关的粗标签，这可能会近似一个更光滑，更复杂的地面真理。例如，自然图像可能包含多个对象，其中只有一个对象在许多视觉数据集中标记，或者可以是由于回归问题的离散化而导致的。在此类粗标签上使用跨凝结训练分类模型可能会大致介绍特征空间，可能会忽略最有意义的此类功能，特别是在基础细粒任务上失去信息。在本文中，我们对仅在粗粒标签上训练的模型来解决细粒分类或回归的问题感兴趣。我们表明，标准的跨凝结可能导致与粗相关的特征过度拟合。我们引入了基于熵的正则化，以促进训练有素的模型的特征空间中的更多多样性，并从经验上证明了这种方法的功效，以在细粒度问题上提高性能。通过理论发展和经验验证，我们的结果得到了支持。

引入了Wasserstein距离的许多变体，以减轻其原始计算负担。尤其是切成薄片的距离（SW），该距离（SW）利用了一维投影，可以使用封闭式的瓦斯汀距离解决方案。然而，它仅限于生活在欧几里得空间中的数据，而Wasserstein距离已被研究和最近在歧管上使用。我们更具体地专门地关注球体，为此定义了新颖的SW差异，我们称之为球形切片 - 拖鞋，这是朝着定义SW差异的第一步。我们的构造明显基于圆圈上瓦斯汀距离的封闭式解决方案，以及新的球形ra径。除了有效的算法和相应的实现外，我们在几个机器学习用例中说明了它的属性，这些用例中，数据的球形表示受到威胁：在球体上的密度估计，变异推理或超球体自动编码器。

混合是一种数据相关的正则化技术，其包括线性内插输入样本和相关输出。它已被证明在用于培训标准机器学习数据集时提高准确性。然而，作者已经指出，混合可以在增强训练集中产生分配的虚拟样本，甚至是矛盾，可能导致对抗效应。在本文中，我们介绍了当地混合，其中在计算损失时加权远处输入样本。在约束的环境中，我们证明了本地混合可以在偏差和方差之间产生权衡，极端情况降低了香草培训和古典混合。使用标准化的计算机视觉基准测试，我们还表明本地混合可以提高测试精度。

In the era of noisy intermediate scale quantum devices, variational quantum circuits (VQCs) are currently one of the main strategies for building quantum machine learning models. These models are made up of a quantum part and a classical part. The quantum part is given by a parametrization $U$, which, in general, is obtained from the product of different quantum gates. By its turn, the classical part corresponds to an optimizer that updates the parameters of $U$ in order to minimize a cost function $C$. However, despite the many applications of VQCs, there are still questions to be answered, such as for example: What is the best sequence of gates to be used? How to optimize their parameters? Which cost function to use? How the architecture of the quantum chips influences the final results? In this article, we focus on answering the last question. We will show that, in general, the cost function will tend to a typical average value the closer the parameterization used is from a $2$-design. Therefore, the closer this parameterization is to a $2$-design, the less the result of the quantum neural network model will depend on its parametrization. As a consequence, we can use the own architecture of the quantum chips to defined the VQC parametrization, avoiding the use of additional swap gates and thus diminishing the VQC depth and the associated errors.

Dataset scaling, also known as normalization, is an essential preprocessing step in a machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary within the same range. This transformation is known to improve the performance of classification models, but there are several scaling techniques to choose from, and this choice is not generally done carefully. In this paper, we execute a broad experiment comparing the impact of 5 scaling techniques on the performances of 20 classification algorithms among monolithic and ensemble models, applying them to 82 publicly available datasets with varying imbalance ratios. Results show that the choice of scaling technique matters for classification performance, and the performance difference between the best and the worst scaling technique is relevant and statistically significant in most cases. They also indicate that choosing an inadequate technique can be more detrimental to classification performance than not scaling the data at all. We also show how the performance variation of an ensemble model, considering different scaling techniques, tends to be dictated by that of its base model. Finally, we discuss the relationship between a model's sensitivity to the choice of scaling technique and its performance and provide insights into its applicability on different model deployment scenarios. Full results and source code for the experiments in this paper are available in a GitHub repository.\footnote{https://github.com/amorimlb/scaling\_matters}

We describe a Physics-Informed Neural Network (PINN) that simulates the flow induced by the astronomical tide in a synthetic port channel, with dimensions based on the Santos - S\~ao Vicente - Bertioga Estuarine System. PINN models aim to combine the knowledge of physical systems and data-driven machine learning models. This is done by training a neural network to minimize the residuals of the governing equations in sample points. In this work, our flow is governed by the Navier-Stokes equations with some approximations. There are two main novelties in this paper. First, we design our model to assume that the flow is periodic in time, which is not feasible in conventional simulation methods. Second, we evaluate the benefit of resampling the function evaluation points during training, which has a near zero computational cost and has been verified to improve the final model, especially for small batch sizes. Finally, we discuss some limitations of the approximations used in the Navier-Stokes equations regarding the modeling of turbulence and how it interacts with PINNs.

Language modeling, a central task in natural language processing, involves estimating a probability distribution over strings. In most cases, the estimated distribution sums to 1 over all finite strings. However, in some pathological cases, probability mass can ``leak'' onto the set of infinite sequences. In order to characterize the notion of leakage more precisely, this paper offers a measure-theoretic treatment of language modeling. We prove that many popular language model families are in fact tight, meaning that they will not leak in this sense. We also generalize characterizations of tightness proposed in previous works.

As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user's preferred answer ("sycophancy") and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.