在任何翻译工作流程中，从源到目标的域知识保存至关重要。在翻译行业中，接收高度专业化的项目是很常见的，那里几乎没有任何平行的内域数据。在这种情况下，没有足够的内域数据来微调机器翻译（MT）模型，生成与相关上下文一致的翻译很具有挑战性。在这项工作中，我们提出了一种新颖的方法，用于域适应性，以利用最新的审计语言模型（LMS）来用于特定于域的MT的域数据增强，并模拟（a）的（a）小型双语数据集的域特征，或（b）要翻译的单语源文本。将这个想法与反翻译相结合，我们可以为两种用例生成大量的合成双语内域数据。为了进行调查，我们使用最先进的变压器体系结构。我们采用混合的微调来训练模型，从而显着改善了内域文本的翻译。更具体地说，在这两种情况下，我们提出的方法分别在阿拉伯语到英语对阿拉伯语言对上分别提高了大约5-6个BLEU和2-3 BLEU。此外，人类评估的结果证实了自动评估结果。

深度强化学习中的一个开放研究问题是如何将稀疏领域中关键决策的政策学习重点。本文强调将“隐藏的马尔可夫模型”和“强化学习”的优势结合在一起，以朝着可解释的维护决策中。我们提出了一种新型的层次建模方法，该方法在高水平上检测并解释了失败的根本原因以及涡轮扇叶引擎的健康降解，而在低水平上，它提供了最佳的替换政策。它的表现优于直接应用于原始数据或使用隐藏的马尔可夫模型而没有这样的专业层次结构时，深入强化学习方法的基线性能。但是，它还提供了与先前的工作相当的绩效，并具有可解释性的额外好处。

我们研究了对知识图中链路预测任务的知识图形嵌入（KGE）模型产生数据中毒攻击的问题。为了毒害KGE模型，我们建议利用他们通过知识图中的对称性，反演和构图等关系模式捕获的归纳能力。具体而言，为了降低模型对目标事实的预测信心，建议改善模型对一系列诱饵事实的预测信心。因此，我们通过不同的推理模式来制作对逆势的添加能够改善模型对诱饵事实上的预测信心。我们的实验表明，拟议的中毒攻击在四个KGE模型上倾斜的最先进的基座，用于两个公共数据集。我们还发现基于对称模式的攻击遍历了所有模型 - 数据集合，指示KGE模型对此模式的灵敏度。

尽管使用知识图形嵌入式（KGE），但对于可能会扰乱其预期行为的安全漏洞很少。我们研究了对KGE模型进行链路预测的数据中毒攻击。这些攻击在训练时间进行工艺对抗性添加或删除，以在测试时间造型失败。要选择对抗性删除，我们建议使用来自可解释的机器学习的模型 - 无人实例归因方法，该模型 - 无可争议的机器学习，该模型算法识别对神经模型对测试实例的预测最大的培训实例。我们使用这些有影响力的三元组作为对抗性缺失。我们进一步提出了一种启发式方法，以取代各种有影响力的三倍的两个实体中的一个以产生对抗性添加。我们的实验表明，该拟议的策略优于KGE模型的最先进的数据中毒攻击，并通过基线的攻击达到62％，提高MRR降级。

We demonstrate a proof-of-concept of a large language model conducting corporate lobbying related activities. We use an autoregressive large language model (OpenAI's text-davinci-003) to determine if proposed U.S. Congressional bills are relevant to specific public companies and provide explanations and confidence levels. For the bills the model deems as relevant, the model drafts a letter to the sponsor of the bill in an attempt to persuade the congressperson to make changes to the proposed legislation. We use hundreds of ground-truth labels of the relevance of a bill to a company to benchmark the performance of the model, which outperforms the baseline of predicting the most common outcome of irrelevance. However, we test the ability to determine the relevance of a bill with the previous OpenAI GPT-3 model (text-davinci-002), which was state-of-the-art on many language tasks until text-davinci-003 was released on November 28, 2022. The performance of text-davinci-002 is worse than simply always predicting that a bill is irrelevant to a company. These results suggest that, as large language models continue to improve core natural language understanding capabilities, performance on corporate lobbying related tasks will continue to improve. We then discuss why this could be problematic for societal-AI alignment.

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short). This class of systems is widely used in control engineering and econometrics, in particular, they represent a special case of recurrent neural networks. In this paper we 1) formalize the learning problem for stochastic LTI systems with inputs, 2) derive a PAC-Bayesian-Like error bound for such systems, 3) discuss various consequences of this error bound.

We demonstrate how efficient autonomous drone swarms can be in detecting and tracking occluded targets in densely forested areas, such as lost people during search and rescue missions. Exploration and optimization of local viewing conditions, such as occlusion density and target view obliqueness, provide much faster and much more reliable results than previous, blind sampling strategies that are based on pre-defined waypoints. An adapted real-time particle swarm optimization and a new objective function are presented that are able to deal with dynamic and highly random through-foliage conditions. Synthetic aperture sensing is our fundamental sampling principle, and drone swarms are employed to approximate the optical signals of extremely wide and adaptable airborne lenses.

Generative AI has matured to a point where large-scale models can generate text that seems indistinguishable from human-written text and remarkably photorealistic images. Automatically measuring how close the distribution of generated data is to the target real data distribution is a key step in diagnosing existing models and developing better models. We present MAUVE, a family of comparison measures between pairs of distributions such as those encountered in the generative modeling of text or images. These scores are statistical summaries of divergence frontiers capturing two types of errors in generative modeling. We explore four approaches to statistically estimate these scores: vector quantization, non-parametric estimation, classifier-based estimation, and parametric Gaussian approximations. We provide statistical bounds for the vector quantization approach. Empirically, we find that the proposed scores paired with a range of $f$-divergences and statistical estimation methods can quantify the gaps between the distributions of human-written text and those of modern neural language models by correlating with human judgments and identifying known properties of the generated texts. We conclude the paper by demonstrating its applications to other AI domains and discussing practical recommendations.