We study a multi-agent reinforcement learning (MARL) problem where the agents interact over a given network. The goal of the agents is to cooperatively maximize the average of their entropy-regularized long-term rewards. To overcome the curse of dimensionality and to reduce communication, we propose a Localized Policy Iteration (LPI) algorithm that provably learns a near-globally-optimal policy using only local information. In particular, we show that, despite restricting each agent's attention to only its $\kappa$-hop neighborhood, the agents are able to learn a policy with an optimality gap that decays polynomially in $\kappa$. In addition, we show the finite-sample convergence of LPI to the global optimal policy, which explicitly captures the trade-off between optimality and computational complexity in choosing $\kappa$. Numerical simulations demonstrate the effectiveness of LPI.

重建3D对象是重要的计算机视觉任务，在AR/VR中具有广泛的应用。为此任务开发的深度学习算法通常依赖于不切实际的合成数据集，例如shapenet和things3d。另一方面，现有的以对象为中心的数据集通常没有足够的注释来实现监督培训或可靠的评估。在此技术报告中，我们提出了一个以照片为中心的对象数据集HM3D-ABO。它是通过构成现实的室内场景和现实对象来构建的。对于每种配置，我们提供多视图RGB观测值，这是对象，地面真实深度图和对象掩码的水密网格模型。所提出的数据集也可用于诸如摄像头估计和新颖视图合成之类的任务。数据集生成代码在https://github.com/zhenpeiyang/hm3d-abo上发布。

开发深度神经网络以生成3D场景是神经综合的基本问题，其立即应用于架构CAD，计算机图形，以及生成虚拟机器人训练环境。这项任务是具有挑战性的，因为3D场景呈现不同的模式，从连续的模式等等，例如对象尺寸和成对对之间的相对姿势，以离散模式，例如具有对称关系的对象的发生和共发生。本文介绍了一种新型神经场景综合方法，可以捕获3D场景的不同特征模式。我们的方法结合了神经网络和传统场景合成方法的强度。我们使用从训练数据中学到的参数上的分布，这提供了对象属性和相对属性的不确定性，以规范前馈神经模型的输出。此外，我们的方法不仅仅是预测场景布局，而不是预测场景布局。该方法允许我们利用预测属性之间的底层一致性约束来修剪不可行的预测。实验结果表明，我们的方法显着优于现有方法。生成的3D场景在保留连续和离散特征模式的同时忠实地插入训练数据。

在这项工作中，我们研究了解决强化学习问题的基于政策的方法，其中采用了非政策性采样和线性函数近似进行政策评估，以及包括自然政策梯度（NPG）在内的各种政策更新规则，用于政策更新。为了在致命三合会的存在下解决政策评估子问题，我们提出了一个通用算法的多步型TD学习框架，具有广义的重要性抽样比率，其中包括两个特定的算法：$ \ lambda $ Q Q $ Q Q $ - 跟踪和双面$ Q $ - 跟踪。通用算法是单个时间尺度，具有可证明的有限样本保证，并克服了非政策学习中的高方差问题。至于策略更新，我们仅使用Bellman操作员的收缩属性和单调性属性提供通用分析，以在各种策略更新规则下建立几何融合。重要的是，通过将NPG视为实施政策迭代的近似方法，我们在不引入正则化的情况下建立了NPG的几何融合，并且不使用现有文献中的镜像下降类型的分析类型。将策略更新的几何融合与策略评估的有限样本分析相结合，我们首次建立了整​​体$ \ Mathcal {o}（\ Epsilon^{ - 2}）$样本复杂性以找到最佳策略（最多达到函数近似误差）使用基于策略的方法和线性函数近似下的基于策略的方法。

随机近似（SA）和随机梯度下降（SGD）算法是现代机器学习算法的工作马。由于快速收敛行为，它们在实践中优选它们的持续步骤变体。然而，恒定的步骤随机迭代算法不与最佳解决方案渐近地收敛，而是具有静止分布，这通常不能被分析表征。在这项工作中，我们研究了适当缩放的静止分布的渐近行为，在恒定步骤零的限制中。具体而言，我们考虑以下三种设置：（1）SGD算法，具有平滑且强的凸面物镜，（2）涉及Hurwitz矩阵的线性SA算法，和（3）涉及收缩算子的非线性SA算法。当迭代以$ 1 / \ sqrt {\ alpha} $缩放时，其中$ \ alpha $是常量的步骤，我们表明限制缩放静止分布是整体方程的解决方案。在该等式上的唯一性假设（可以在某些设置中除去），我们进一步表征了作为高斯分布的限制分布，其协方差矩阵是合适的Lyapunov方程的独特解决方案。对于超出这些情况的SA算法，我们的数值实验表明，与中央极限定理类型结果不同：（1）缩放因子不需要为$ 1 / \ sqrt {\ alpha} $，并且（2）限制分布不需要高斯。基于数值研究，我们提出了一种确定右缩放因子的公式，并与近似随机微分方程的欧拉 - 玛赖山离散化方案进行富有洞察力的连接。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.