近年来，在移动设备上部署深度学习（DL）一直是一个显着的趋势。为了支持对开发DL的快速推断，DL库作为算法和硬件扮演着至关重要的角色。不幸的是，先前的工作从未深入现代DL Libs的生态系统，并为其性能提供定量结果。在本文中，我们首先建立了一个全面的基准，其中包括6个代表性DL LIB和15种多元化的DL模型。然后，我们在10个移动设备上进行了广泛的实验，这有助于揭示当前移动DL LIBS生态系统的完整景观。例如，我们发现表现最佳的DL LIB在不同的模型和硬件中严重碎片，这些DL Libs之间的差距可能相当巨大。实际上，DL LIB的影响会淹没算法或硬件的优化，例如模型量化和基于GPU/DSP的异质计算。最后，在观察结果上，我们总结了对DL Lib生态系统中不同角色的实际意义。

最近的智能故障诊断（IFD）的进展大大依赖于深度代表学习和大量标记数据。然而，机器通常以各种工作条件操作，或者目标任务具有不同的分布，其中包含用于训练的收集数据（域移位问题）。此外，目标域中的新收集的测试数据通常是未标记的，导致基于无监督的深度转移学习（基于UDTL为基础的）IFD问题。虽然它已经实现了巨大的发展，但标准和开放的源代码框架以及基于UDTL的IFD的比较研究尚未建立。在本文中，我们根据不同的任务，构建新的分类系统并对基于UDTL的IFD进行全面审查。对一些典型方法和数据集的比较分析显示了基于UDTL的IFD中的一些开放和基本问题，这很少研究，包括特征，骨干，负转移，物理前导等的可转移性，强调UDTL的重要性和再现性 - 基于IFD，整个测试框架将发布给研究界以促进未来的研究。总之，发布的框架和比较研究可以作为扩展界面和基本结果，以便对基于UDTL的IFD进行新的研究。代码框架可用于\ url {https:/github.com/zhaozhibin/udtl}。

Reinforcement learning (RL) problems can be challenging without well-shaped rewards. Prior work on provably efficient RL methods generally proposes to address this issue with dedicated exploration strategies. However, another way to tackle this challenge is to reformulate it as a multi-task RL problem, where the task space contains not only the challenging task of interest but also easier tasks that implicitly function as a curriculum. Such a reformulation opens up the possibility of running existing multi-task RL methods as a more efficient alternative to solving a single challenging task from scratch. In this work, we provide a theoretical framework that reformulates a single-task RL problem as a multi-task RL problem defined by a curriculum. Under mild regularity conditions on the curriculum, we show that sequentially solving each task in the multi-task RL problem is more computationally efficient than solving the original single-task problem, without any explicit exploration bonuses or other exploration strategies. We also show that our theoretical insights can be translated into an effective practical learning algorithm that can accelerate curriculum learning on simulated robotic tasks.

自回归生成模型可以估计复杂的连续数据分布，例如在RL环境，图像强度和音频中的轨迹推出。大多数最先进的模型将连续数据离散为几个箱，并在箱上使用分类分布来近似连续数据分布。优点是，分类分布可以轻松地表达多种模式，并且可以简单地进行优化。但是，如果没有明显的垃圾箱，这种近似就无法表达密度的急剧变化，从而使其参数效率低下。我们提出了一种称为自适应分类离散化（ADACAT）的有效，表现力的多模式参数化。 AdaCat自适应地自适应地自动回归模型的每个维度，这使该模型能够分配密度为感兴趣的细胞间隔，从而提高了参数效率。 Adacat概括了分类和基于分位数的回归。 ADACAT是任何基于离散化的分布估计器的简单附加组件。在实验中，Adacat改善了现实世界表数据，图像，音频和轨迹的密度估计，并改善了基于模型的离线RL计划。

强化学习（RL）通常涉及估计静止政策或单步模型，利用马尔可夫属性来解决问题。但是，我们也可以将RL视为通用序列建模问题，目标是产生一系列导致一系列高奖励的动作。通过这种方式观看，考虑在其他域中运用良好的高容量序列预测模型，例如自然语言处理，也可以为RL问题提供有效的解决方案。为此，我们探索如何使用变压器架构与序列建模的工具来解决RL，以将分布在轨迹上和将光束搜索作为规划算法进行重新定位。框架RL作为序列建模问题简化了一系列设计决策，允许我们分配在离线RL算法中常见的许多组件。我们展示了这种方法跨越长地平动态预测，仿制学习，目标条件的RL和离线RL的灵活性。此外，我们表明这种方法可以与现有的无模型算法结合起来，以在稀疏奖励，长地平线任务中产生最先进的策划仪。

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.