Contrastive learning has been successfully used for retrieval of semantically aligned sentences, but it often requires large batch sizes or careful engineering to work well. In this paper, we instead propose a generative model for learning multilingual text embeddings which can be used to retrieve or score sentence pairs. Our model operates on parallel data in $N$ languages and, through an approximation we introduce, efficiently encourages source separation in this multilingual setting, separating semantic information that is shared between translations from stylistic or language-specific variation. We show careful large-scale comparisons between contrastive and generation-based approaches for learning multilingual text embeddings, a comparison that has not been done to the best of our knowledge despite the popularity of these approaches. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval -- the last of which we introduce in this paper. Overall, our Variational Multilingual Source-Separation Transformer (VMSST) model outperforms both a strong contrastive and generative baseline on these tasks.

异常检测是确定不符合正常数据分布的样品。由于异常数据的无法获得，培训监督的深神经网络是一项繁琐的任务。因此，无监督的方法是解决此任务的常见方法。深度自动编码器已被广泛用作许多无监督的异常检测方法的基础。但是，深层自动编码器的一个显着缺点是，它们通过概括重建异常值来提供不足的表示异常检测的表示。在这项工作中，我们设计了一个对抗性框架，该框架由两个竞争组件组成，一个对抗性变形者和一个自动编码器。对抗性变形器是一种卷积编码器，学会产生有效的扰动，而自动编码器是一个深层卷积神经网络，旨在重建来自扰动潜在特征空间的图像。这些网络经过相反的目标训练，在这种目标中，对抗性变形者会产生用于编码器潜在特征空间的扰动，以最大化重建误差，并且自动编码器试图中和这些扰动的效果以最大程度地减少它。当应用于异常检测时，该提出的方法会由于对特征空间的扰动应用而学习语义上的富裕表示。所提出的方法在图像和视频数据集上的异常检测中优于现有的最新方法。

Levenberg-Marquardt（LM）优化算法已广泛用于解决机器学习问题。文学评论表明，当网络中的权重数不超过几百个时，LM对中等函数近似问题的LM非常强大而有效。相比之下，在处理模式识别或分类问题时，LM似乎并不表现，并且当网络变大时效率低（例如，超过500重量）。在本文中，我们利用一些现实世界飞机数据集利用LM算法的真正力量。在这些数据集上，大多数其他常用的优化器无法检测到飞机发动机的变化条件引起的异常。数据集的具有挑战性是时间序列数据的突然变化。我们发现LM优化器具有更好的近似突然变化的能力，并检测除其他优化器的异常。我们比较LM和几个其他优化器的这种异常/更改检测问题的性能。我们基于一系列措施评估了相对性能，包括网络复杂性（即权重的数量），拟合精度，拟合，培训时间，GPU和内存要求等的使用等措施。我们还讨论了Matlab中强大的LM实现问题Tensorflow用于推广LM算法的更多流行使用以及LM优化器的潜在使用进行大规模问题。

新奇检测是识别不属于目标类分布的样本的任务。在培训期间，缺乏新颖的课程，防止使用传统分类方法。深度自动化器已被广泛用作许多无监督的新奇检测方法的基础。特别地，上下文自动码器在新颖的检测任务中已经成功了，因为他们通过从随机屏蔽的图像重建原始图像来学习的更有效的陈述。然而，上下文AutoEncoders的显着缺点是随机屏蔽不能一致地涵盖输入图像的重要结构，导致次优表示 - 特别是对于新颖性检测任务。在本文中，为了优化输入掩蔽，我们设计了由两个竞争网络，掩模模块和重建器组成的框架。掩码模块是一个卷积的AutoEncoder，用于生成涵盖最重要的图像的最佳掩码。或者，重建器是卷积编码器解码器，其旨在从屏蔽图像重建未受带的图像。网络训练以侵略的方式训练，其中掩模模块生成应用于给予重构的图像的掩码。以这种方式，掩码模块寻求最大化重建错误的重建错误最小化。当应用于新颖性检测时，与上下文自动置换器相比，所提出的方法学习语义上更丰富的表示，并通过更新的屏蔽增强了在测试时间的新颖性检测。 MNIST和CIFAR-10图像数据集上的新奇检测实验证明了所提出的方法对尖端方法的优越性。在用于新颖性检测的UCSD视频数据集的进一步实验中，所提出的方法实现了最先进的结果。

Adversarial training, in which a network is trained on adversarial examples, is one of the few defenses against adversarial attacks that withstands strong attacks. Unfortunately, the high cost of generating strong adversarial examples makes standard adversarial training impractical on large-scale problems like ImageNet. We present an algorithm that eliminates the overhead cost of generating adversarial examples by recycling the gradient information computed when updating model parameters.Our "free" adversarial training algorithm achieves comparable robustness to PGD adversarial training on the CIFAR-10 and CIFAR-100 datasets at negligible additional cost compared to natural training, and can be 7 to 30 times faster than other strong adversarial training methods. Using a single workstation with 4 P100 GPUs and 2 days of runtime, we can train a robust model for the large-scale ImageNet classification task that maintains 40% accuracy against PGD attacks. The code is available at https://github.com/ashafahi/free_adv_train.

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.