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.

现代深度学习框架提供嵌入在Python中的必要的急切执行编程接口，以提供生产的开发体验。但是，深度学习从业者有时需要捕获和转换程序结构以进行性能优化，可视化，分析和硬件集成。我们研究了深度学习中使用的程序捕获和转型的不同设计。通过设计典型的深度学习用例而不是长尾部，可以为程序捕获和转换创建更简单的框架。我们在Torch.fx中应用了这一原理，是一个完全在Python写入的Pytorch的程序捕获和转换库，并通过ML从业者进行高开发人员生产力优化。我们存在案例研究，展示了Torch.fx如何实现先前在Pytorch生态系统中无法访问的工作流程。

In this work, we explore a useful but often neglected methodology for robustness analysis of text generation evaluation metrics: stress tests with synthetic data. Basically, we design and synthesize a wide range of potential errors and check whether they result in a commensurate drop in the metric scores. We examine a range of recently proposed evaluation metrics based on pretrained language models, for the tasks of open-ended generation, translation, and summarization. Our experiments reveal interesting insensitivities, biases, or even loopholes in existing metrics. For example, we find that BERTScore ignores truncation errors in summarization, and MAUVE (built on top of GPT-2) is insensitive to errors at the beginning of generations. Further, we investigate the reasons behind these blind spots and suggest practical workarounds for a more reliable evaluation of text generation.

Language Models appear to perform poorly on quantification. We ask how badly. 'Few'-type quantifiers, as in 'few children like vegetables' might pose a particular challenge for Language Models, since the sentence components without the quantifier are likely to co-occur, and because 'few'-type quantifiers are rare. We present 960 sentences stimuli from two human neurolinguistic experiments to 22 autoregressive transformer models of differing sizes. Not only do the models perform poorly on 'few'-type quantifiers, but overall the larger the model, the worse its performance. We interpret this inverse scaling as suggesting that larger models increasingly reflect online rather than offline human processing, and argue that decreasing performance of larger models may challenge uses of Language Models as the basis for Natural Language Systems.

Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging problem due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg, based on image synthesis. A background generator delivers image backgrounds that closely match real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.02%, 1.4%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.

Are the predictions of humans and language models affected by similar things? Research suggests that while comprehending language, humans make predictions about upcoming words, with more predictable words being processed more easily. However, evidence also shows that humans display a similar processing advantage for highly anomalous words when these words are semantically related to the preceding context or to the most probable continuation. Using stimuli from 3 psycholinguistic experiments, we find that this is also almost always also the case for 8 contemporary transformer language models (BERT, ALBERT, RoBERTa, XLM-R, GPT-2, GPT-Neo, GPT-J, and XGLM). We then discuss the implications of this phenomenon for our understanding of both human language comprehension and the predictions made by language models.

Gauge Theory plays a crucial role in many areas in science, including high energy physics, condensed matter physics and quantum information science. In quantum simulations of lattice gauge theory, an important step is to construct a wave function that obeys gauge symmetry. In this paper, we have developed gauge equivariant neural network wave function techniques for simulating continuous-variable quantum lattice gauge theories in the Hamiltonian formulation. We have applied the gauge equivariant neural network approach to find the ground state of 2+1-dimensional lattice gauge theory with U(1) gauge group using variational Monte Carlo. We have benchmarked our approach against the state-of-the-art complex Gaussian wave functions, demonstrating improved performance in the strong coupling regime and comparable results in the weak coupling regime.

我们研究了Levin（1993）所述的动词交替类的程度和句子级预测任务。我们遵循并扩展了Kann等人的实验。（2019年），旨在探测静态嵌入是否编码动词的框架选择性。在单词和句子级别上，我们发现来自PLM的上下文嵌入不仅超过了非上下文嵌入，而且在大多数交替类中的任务上达到了惊人的高精度。此外，我们发现证据表明，PLM的中间层平均比所有探测任务中的较低层都能取得更好的性能。

某些语言允许在某些情况下省略参数。然而，人类语言理解者可靠地推断出这些零代词的预期参考人，部分原因是他们建立了对哪些参考人更有可能的期望。我们询问神经语言模型是否也提取了相同的期望。我们测试了12种当代语言模型是否显示出反映人类行为的期望，这些句子暴露于Carminati（2005）中意大利五个行为实验中的零代词中。我们发现三个模型-XGLM 2.9B，4.5B和7.5B-从所有实验中捕获人类行为，而其他实验则成功地对某些结果进行了建模。该结果表明，人类对核心的期望可以从接触语言中得出，并且还指示了语言模型的特征，使他们能够更好地反映人类的行为。

强化学习中的固有问题是应对不确定要采取的行动（或状态价值）的政策。模型不确定性，更正式地称为认知不确定性，是指超出采样噪声的模型的预期预测误差。在本文中，我们提出了Q值函数中认知不确定性估计的度量，我们将其称为路线上的认知不确定性。我们进一步开发了一种计算其近似上限的方法，我们称之为f值。我们通过实验将后者应用于深Q-Networks（DQN），并表明增强学习中的不确定性估计是学习进步的有用指标。然后，我们提出了一种新的方法，通过从现有（以前学过的或硬编码）的甲骨文政策中学习不确定性的同时，旨在避免在训练过程中避免非生产性的随机操作，从而提高参与者批评算法的样本效率。我们认为这位评论家的信心指导了探索（CCGE）。我们使用我们的F-Value指标在软演奏者（SAC）上实施CCGE，我们将其应用于少数流行的健身环境，并表明它比有限的背景下的香草囊获得了更好的样本效率和全部情节奖励。