本文有助于将读者介绍到多功能增强学习（MARL）领域及其与因果关系研究的方法的交叉。我们突出了Marl中的关键挑战，并在因因果方法如何协助解决它们的情况下讨论这些问题。我们促进了对Marl的'因果首先'的透视。具体而言，我们认为因果关系可以提高安全性，可解释性和稳健性，同时还为紧急行为提供了强烈的理论保障。我们讨论潜在的挑战解决方案，并使用这种背景激励未来的研究方向。

A long-standing goal of machine-learning-based protein engineering is to accelerate the discovery of novel mutations that improve the function of a known protein. We introduce a sampling framework for evolving proteins in silico that supports mixing and matching a variety of unsupervised models, such as protein language models, and supervised models that predict protein function from sequence. By composing these models, we aim to improve our ability to evaluate unseen mutations and constrain search to regions of sequence space likely to contain functional proteins. Our framework achieves this without any model fine-tuning or re-training by constructing a product of experts distribution directly in discrete protein space. Instead of resorting to brute force search or random sampling, which is typical of classic directed evolution, we introduce a fast MCMC sampler that uses gradients to propose promising mutations. We conduct in silico directed evolution experiments on wide fitness landscapes and across a range of different pre-trained unsupervised models, including a 650M parameter protein language model. Our results demonstrate an ability to efficiently discover variants with high evolutionary likelihood as well as estimated activity multiple mutations away from a wild type protein, suggesting our sampler provides a practical and effective new paradigm for machine-learning-based protein engineering.

Gaussian process training decomposes into inference of the (approximate) posterior and learning of the hyperparameters. For non-Gaussian (non-conjugate) likelihoods, two common choices for approximate inference are Expectation Propagation (EP) and Variational Inference (VI), which have complementary strengths and weaknesses. While VI's lower bound to the marginal likelihood is a suitable objective for inferring the approximate posterior, it does not automatically imply it is a good learning objective for hyperparameter optimization. We design a hybrid training procedure where the inference leverages conjugate-computation VI and the learning uses an EP-like marginal likelihood approximation. We empirically demonstrate on binary classification that this provides a good learning objective and generalizes better.

决策者需要在采用新的治疗政策之前预测结果的发展，该政策定义了何时以及如何连续地影响结果的治疗序列。通常，预测介入的未来结果轨迹的算法将未来治疗的固定顺序作为输入。这要么忽略了未来治疗对结果之前的结果的依赖性，要么隐含地假设已知治疗政策，因此排除了该政策未知或需要反事实分析的情况。为了应对这些局限性，我们开发了一种用于治疗和结果的联合模型，该模型允许估计处理策略和顺序治疗（OUT COMECTION数据）的影响。它可以回答有关治疗政策干预措施的介入和反事实查询，因为我们使用有关血糖进展的现实数据显示，并在此基础上进行了模拟研究。

3D多对象跟踪（MOT）是自动驾驶汽车的关键问题，需要在动态环境中执行信息良好的运动计划。特别是对于密集的占领场景，将现有曲目与新检测相关联仍然具有挑战性，因为现有系统倾向于省略关键的上下文信息。我们提出的解决方案InterTrack引入了3D MOT的相互作用变压器，以生成数据关联的区分对象表示。我们为每个轨道和检测提取状态和形状特征，并通过注意力有效地汇总全局信息。然后，我们对每个轨道/检测功能对进行学习的回归以估计亲和力，并使用强大的两阶段数据关联和轨道管理方法来生成最终轨道。我们在Nuscenes 3D MOT基准上验证了我们的方法，在那里我们观察到了显着的改进，尤其是在物理大小和聚类对象的类别上。从提交开始时，InterTrack在使用CenterPoint检测的方法中排名第1位AMOTA。

最近，注意机制已成功应用于基于神经网络的说话者验证系统。将挤压和兴奋的块纳入卷积神经网络中的表现出色。但是，它使用全球平均池（GAP）简单地沿时间和频率维度平均功能，这无法在功能地图中保留足够的扬声器信息。在这项研究中，我们表明GAP是时间频域在数学上仅使用频率分解中最低频率分量的特殊情况。为了增强扬声器信息提取能力，我们建议利用多频信息，并设计两个新颖的有效注意模块，称为单频率单通道（SFSC）注意模块和多频单通道（MFSC）注意模块。提出的注意模块可以根据DCT有效地从多个频率组件中捕获更多扬声器信息。我们在Voxceleb数据集上进行了全面的实验，并对第148个UTD法医语料库进行了探测评估。实验结果表明，我们提出的SFSC和MFSC注意模块可以有效地产生更具歧视性的扬声器表示，并且优于RESNET34-SE和ECAPA-TDNN系统，而EER降低了20.9％和20.2％，而无需添加额外的网络参数。

高斯过程（GPS）提供了对图表的推理和学习的原则和直接的方法。然而，缺乏用于时空建模的正义的图形内核已经备份了在图形问题中的使用。我们在图形上利用随机偏微分方程（SPDES）和GPS之间的显式链接，并导出捕获空间和时间交互的不可分离的时空图形内核。我们制定了随机热方程和波动方程的图形核。我们展示通过为图形提供新颖的时空GP建模的新型工具，我们在特征扩散，振荡和其他复杂交互中的实际应用中优先于现有的图形内核。

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.