正如最近的作品中观察到的那样，通信图神经网络（GNN）中信号传播的质量强烈影响其表现力。特别是，对于依靠远程相互作用的预测任务，节点特征的递归聚合可能导致不希望的现象称为“过句”。我们提出了一个基于信息收缩的分析过度句子的框架。我们的分析以可靠计算的模型为指导，该模型由于冯·诺伊曼（Von Neumann），该模型在嘈杂的计算图中提供了新的洞察力作为信号淬灭的新见解。在此基础上，我们提出了一个旨在减轻过度量化的算法的图形。我们的算法采用了由扩展器图构造动机的随机局部边缘翻转原始的。我们将算法的光谱膨胀特性与现有基于曲率的非本地重新布线策略的光谱膨胀属性进行了比较。合成实验表明，尽管我们的算法通常具有较慢的膨胀速率，但总体计算更便宜，可以准确地保留节点度，并且永远不会断开图表的连接。

我们在假设目标函数的先前和EIGENExpansion系数的假定下，我们将高斯进程回归（GPR）的幂律渐近学习曲线的幂律渐近学呈现出高斯过程回归（GPR）。在类似的假设下，我们利用GPR和内核RIDGE回归（KRR）之间的等价性来显示KRR的泛化误差。无限宽的神经网络可以与GPR相对于神经网络GP内核和神经切线内核有关，其中已知在几个情况下具有幂律谱。因此，我们的方法可以应用于研究无限宽神经网络的泛化误差。我们提出了展示理论的玩具实验。

本研究旨在为印地语开发半自动标记的韵律数据库，用于增强ASR和TTS系统中的语调组件，这也有助于向语音机翻译系统构建语音。虽然印地语中没有单一的韵律标签标准，但过去的研究人员在文献中使用了感知和统计方法，以利用印地语中韵律模式的行为的推论。基于此类现有研究并在很大程度上商定了印地语中的语调理论，这项研究试图首先开发印地语语音数据的手动注释的韵律语料库，然后用于培训用于产生自动韵律标签的预测模型。已经标记了总数为5,000句话（23,500字）的声明和疑问类型。训练有素的型号的音高型号，中级短语和呼吸界界限分别为73.40％，93.20％和43％。

Machine Translation (MT) system generally aims at automatic representation of source language into target language retaining the originality of context using various Natural Language Processing (NLP) techniques. Among various NLP methods, Statistical Machine Translation(SMT). SMT uses probabilistic and statistical techniques to analyze information and conversion. This paper canvasses about the development of bilingual SMT models for translating English to fifteen low-resource Indian Languages (ILs) and vice versa. At the outset, all 15 languages are briefed with a short description related to our experimental need. Further, a detailed analysis of Samanantar and OPUS dataset for model building, along with standard benchmark dataset (Flores-200) for fine-tuning and testing, is done as a part of our experiment. Different preprocessing approaches are proposed in this paper to handle the noise of the dataset. To create the system, MOSES open-source SMT toolkit is explored. Distance reordering is utilized with the aim to understand the rules of grammar and context-dependent adjustments through a phrase reordering categorization framework. In our experiment, the quality of the translation is evaluated using standard metrics such as BLEU, METEOR, and RIBES

When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of black-box learned components in the modern robot autonomy stack. Therefore, coping with OOD data is an important challenge on the path towards trustworthy learning-enabled open-world autonomy. In this paper, we aim to demystify the topic of OOD data and its associated challenges in the context of data-driven robotic systems, drawing connections to emerging paradigms in the ML community that study the effect of OOD data on learned models in isolation. We argue that as roboticists, we should reason about the overall system-level competence of a robot as it performs tasks in OOD conditions. We highlight key research questions around this system-level view of OOD problems to guide future research toward safe and reliable learning-enabled autonomy.

In multi-agent systems with large number of agents, typically the contribution of each agent to the value of other agents is minimal (e.g., aggregation systems such as Uber, Deliveroo). In this paper, we consider such multi-agent systems where each agent is self-interested and takes a sequence of decisions and represent them as a Stochastic Non-atomic Congestion Game (SNCG). We derive key properties for equilibrium solutions in SNCG model with non-atomic and also nearly non-atomic agents. With those key equilibrium properties, we provide a novel Multi-Agent Reinforcement Learning (MARL) mechanism that minimizes variance across values of agents in the same state. To demonstrate the utility of this new mechanism, we provide detailed results on a real-world taxi dataset and also a generic simulator for aggregation systems. We show that our approach reduces the variance in revenues earned by taxi drivers, while still providing higher joint revenues than leading approaches.

Migraine is a high-prevalence and disabling neurological disorder. However, information migraine management in real-world settings could be limited to traditional health information sources. In this paper, we (i) verify that there is substantial migraine-related chatter available on social media (Twitter and Reddit), self-reported by migraine sufferers; (ii) develop a platform-independent text classification system for automatically detecting self-reported migraine-related posts, and (iii) conduct analyses of the self-reported posts to assess the utility of social media for studying this problem. We manually annotated 5750 Twitter posts and 302 Reddit posts. Our system achieved an F1 score of 0.90 on Twitter and 0.93 on Reddit. Analysis of information posted by our 'migraine cohort' revealed the presence of a plethora of relevant information about migraine therapies and patient sentiments associated with them. Our study forms the foundation for conducting an in-depth analysis of migraine-related information using social media data.

Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery. Unlike image and video compression algorithms that limit errors to primary data, scientists require compression techniques that accurately preserve derived quantities of interest (QoIs). This paper presents a physics-informed compression technique implemented as an end-to-end, scalable, GPU-based pipeline for data compression that addresses this requirement. Our hybrid compression technique combines machine learning techniques and standard compression methods. Specifically, we combine an autoencoder, an error-bounded lossy compressor to provide guarantees on raw data error, and a constraint satisfaction post-processing step to preserve the QoIs within a minimal error (generally less than floating point error). The effectiveness of the data compression pipeline is demonstrated by compressing nuclear fusion simulation data generated by a large-scale fusion code, XGC, which produces hundreds of terabytes of data in a single day. Our approach works within the ADIOS framework and results in compression by a factor of more than 150 while requiring only a few percent of the computational resources necessary for generating the data, making the overall approach highly effective for practical scenarios.

Concept bottleneck models (CBMs) (Koh et al. 2020) are interpretable neural networks that first predict labels for human-interpretable concepts relevant to the prediction task, and then predict the final label based on the concept label predictions.We extend CBMs to interactive prediction settings where the model can query a human collaborator for the label to some concepts. We develop an interaction policy that, at prediction time, chooses which concepts to request a label for so as to maximally improve the final prediction. We demonstrate thata simple policy combining concept prediction uncertainty and influence of the concept on the final prediction achieves strong performance and outperforms a static approach proposed in Koh et al. (2020) as well as active feature acquisition methods proposed in the literature. We show that the interactiveCBM can achieve accuracy gains of 5-10% with only 5 interactions over competitive baselines on the Caltech-UCSDBirds, CheXpert and OAI datasets.

Selective classification involves identifying the subset of test samples that a model can classify with high accuracy, and is important for applications such as automated medical diagnosis. We argue that this capability of identifying uncertain samples is valuable for training classifiers as well, with the aim of building more accurate classifiers. We unify these dual roles by training a single auxiliary meta-network to output an importance weight as a function of the instance. This measure is used at train time to reweight training data, and at test-time to rank test instances for selective classification. A second, key component of our proposal is the meta-objective of minimizing dropout variance (the variance of classifier output when subjected to random weight dropout) for training the metanetwork. We train the classifier together with its metanetwork using a nested objective of minimizing classifier loss on training data and meta-loss on a separate meta-training dataset. We outperform current state-of-the-art on selective classification by substantial margins--for instance, upto 1.9% AUC and 2% accuracy on a real-world diabetic retinopathy dataset. Finally, our meta-learning framework extends naturally to unsupervised domain adaptation, given our unsupervised variance minimization meta-objective. We show cumulative absolute gains of 3.4% / 3.3% accuracy and AUC over the other baselines in domain shift settings on the Retinopathy dataset using unsupervised domain adaptation.