In deep neural networks (DNNs), there are a huge number of weights and multiply-and-accumulate (MAC) operations. Accordingly, it is challenging to apply DNNs on resource-constrained platforms, e.g., mobile phones. Quantization is a method to reduce the size and the computational complexity of DNNs. Existing quantization methods either require hardware overhead to achieve a non-uniform quantization or focus on model-wise and layer-wise uniform quantization, which are not as fine-grained as filter-wise quantization. In this paper, we propose a class-based quantization method to determine the minimum number of quantization bits for each filter or neuron in DNNs individually. In the proposed method, the importance score of each filter or neuron with respect to the number of classes in the dataset is first evaluated. The larger the score is, the more important the filter or neuron is and thus the larger the number of quantization bits should be. Afterwards, a search algorithm is adopted to exploit the different importance of filters and neurons to determine the number of quantization bits of each filter or neuron. Experimental results demonstrate that the proposed method can maintain the inference accuracy with low bit-width quantization. Given the same number of quantization bits, the proposed method can also achieve a better inference accuracy than the existing methods.

Deep neural networks (DNNs) have successfully been applied in many fields in the past decades. However, the increasing number of multiply-and-accumulate (MAC) operations in DNNs prevents their application in resource-constrained and resource-varying platforms, e.g., mobile phones and autonomous vehicles. In such platforms, neural networks need to provide acceptable results quickly and the accuracy of the results should be able to be enhanced dynamically according to the computational resources available in the computing system. To address these challenges, we propose a design framework called SteppingNet. SteppingNet constructs a series of subnets whose accuracy is incrementally enhanced as more MAC operations become available. Therefore, this design allows a trade-off between accuracy and latency. In addition, the larger subnets in SteppingNet are built upon smaller subnets, so that the results of the latter can directly be reused in the former without recomputation. This property allows SteppingNet to decide on-the-fly whether to enhance the inference accuracy by executing further MAC operations. Experimental results demonstrate that SteppingNet provides an effective incremental accuracy improvement and its inference accuracy consistently outperforms the state-of-the-art work under the same limit of computational resources.

The last decade has witnessed the breakthrough of deep neural networks (DNNs) in many fields. With the increasing depth of DNNs, hundreds of millions of multiply-and-accumulate (MAC) operations need to be executed. To accelerate such operations efficiently, analog in-memory computing platforms based on emerging devices, e.g., resistive RAM (RRAM), have been introduced. These acceleration platforms rely on analog properties of the devices and thus suffer from process variations and noise. Consequently, weights in neural networks configured into these platforms can deviate from the expected values, which may lead to feature errors and a significant degradation of inference accuracy. To address this issue, in this paper, we propose a framework to enhance the robustness of neural networks under variations and noise. First, a modified Lipschitz constant regularization is proposed during neural network training to suppress the amplification of errors propagated through network layers. Afterwards, error compensation is introduced at necessary locations determined by reinforcement learning to rescue the feature maps with remaining errors. Experimental results demonstrate that inference accuracy of neural networks can be recovered from as low as 1.69% under variations and noise back to more than 95% of their original accuracy, while the training and hardware cost are negligible.

本文提出了一种新颖的方法，该方法支持自然语言语音说明，以指导训练自动驾驶汽车时进行深度强化学习（DRL）算法。DRL方法是自动驾驶汽车（AV）代理的流行方法。但是，大多数现有的方法都是样本和时间的，并且缺乏与人类专家的自然通信渠道。在本文中，新的人类驾驶员如何从人类教练那里学习，激励我们研究人类在循环学习的新方法，并为代理商学习更自然和平易近人的培训界面。我们建议将自然语言语音说明（NLI）纳入基于模型的深度强化学习以训练自动驾驶汽车。我们与Carla模拟器中的一些最先进的DRL方法一起评估了所提出的方法。结果表明，NLI可以帮助缓解训练过程，并大大提高代理商的学习速度。

我们开发了一种新的持续元学习方法，以解决连续多任务学习中的挑战。在此设置中，代理商的目标是快速通过任何任务序列实现高奖励。先前的Meta-Creenifiltive学习算法已经表现出有希望加速收购新任务的结果。但是，他们需要在培训期间访问所有任务。除了简单地将过去的经验转移到新任务，我们的目标是设计学习学习的持续加强学习算法，使用他们以前任务的经验更快地学习新任务。我们介绍了一种新的方法，连续的元策略搜索（Comps），通过以增量方式，在序列中的每个任务上，通过序列的每个任务来消除此限制，而无需重新访问先前的任务。 Comps持续重复两个子程序：使用RL学习新任务，并使用RL的经验完全离线Meta学习，为后续任务学习做好准备。我们发现，在若干挑战性连续控制任务的旧序列上，Comps优于持续的持续学习和非政策元增强方法。

用于控制多助手群的动态系统模型对弹性，分散的导航算法进行了展示的进步。我们之前介绍了神经沃尔斯控制器，其中基于代理的相互作用是通过类复制网络交互来建模的，包括吸引子动力学和相位同步，这些相互作用和相同步在导航啮齿动物的海马地区电路内进行了理论上。这种复杂性妨碍了通常用于研究常规群模型的稳定性，可控性和性能的线性分析。此外，由于目标的复杂性，模型参数的复杂性和基于模拟的采样的计算成本，调谐动态控制器通常是不充分的。在这里，我们提出了一种基于贝叶斯优化（Bayesopt）的自主多智能体系动态控制器模型的框架。我们的方法利用了任务依赖性目标函数来培训高斯过程（GPS）作为代理模型，以实现对动态控制器模型的参数空间的自适应和有效探索。我们通过研究对在时间压力下协作定位和捕获空间分布的奖励的神经沃尔斯行为选择的目标函数来证明这种方法。我们通过在不同几何形状中组合模拟的分数来推广跨环境的任务性能。为了验证搜索性能，我们通过在均匀歧管近似和投影（UMAP）嵌入中的样本轨迹中比较高VS的高维聚类。我们的研究结果表明，适应性，样本有效地评估复杂系统的自组织行为能力，包括动态群体控制器，可以加速神经科学理论的翻译，以应用域。

通过从EMG信号中自动学习肌肉激活模式，在基于肌电图（EMG）的假体控制中使用深神经网络为手工制作的特征提供了一种有希望的替代方法。同时，将RAW EMG信号用作卷积神经网络（CNN）的输入提供了一种简单，快速且理想的方案，以有效控制假体。因此，这项研究研究了窗口长度和重叠之间的关系，这可能会影响用于在CNN中应用的强大原始EMG 2维（2D）信号的产生。以及这些参数正确组合可以保证最佳网络性能的经验法则。此外，我们研究了CNN接受窗口大小与原始EMG信号大小之间的关系。实验结果表明，CNN的性能随着生成的信号内的重叠的增加而增加，当重叠率为窗口长度的75％时，确定的精度最高9.49％，F1得分23.33％。同样，网络性能随接收窗口（内核）大小的增加而增加。这项研究的结果表明，2D EMG信号中75％重叠的组合和更广泛的网络内核可以为基于EMG-CNN的适当假体控制方案提供理想的运动意图分类。

时间序列形状是最近发现对时间序列聚类有效（TSC）有效的歧视子序列。形状方便地解释簇。因此，TSC的主要挑战是发现高质量的可变长度形状以区分不同的簇。在本文中，我们提出了一种新型的自动编码器窗帘方法（AutoShape），这是第一次利用自动编码器和塑形器以不受欢迎的方式确定形状的研究。自动编码器的专门设计用于学习高质量的形状。更具体地说，为了指导潜在的表示学习，我们采用了最新的自我监督损失来学习不同变量的可变长度塑形塑形（时间序列子序列）的统一嵌入，并提出多样性损失，以选择歧视嵌入的嵌入方式统一空间。我们介绍了重建损失，以在原始时间序列空间中恢复形状，以进行聚类。最后，我们采用Davies Bouldin指数（DBI），将学习过程中的聚类性能告知AutoShape。我们介绍了有关自动赛的广泛实验。为了评估单变量时间序列（UTS）的聚类性能，我们将AutoShape与使用UCR存档数据集的15种代表性方法进行比较。为了研究多元时间序列（MTS）的性能，我们使用5种竞争方法评估了30个UEA档案数据集的AutoShape。结果证明了AutoShape是所有比较的方法中最好的。我们用形状来解释簇，并可以在三个UTS案例研究和一个MTS案例研究中获得有关簇的有趣直觉。

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.