图形神经网络（GNN）由于其独特的能力扩展了机器学习（ML）方法，因此引起了极大的关注，该应用程序广泛定义为具有非结构化数据，尤其是图形。与其他机器学习（ML）方式相比，由于源自图类型的不规则性和异质性，图形神经网络（GNN）的加速度更具挑战性。但是，现有的努力主要集中在处理图形的不规则性上，并且没有研究其异质性。为此，我们提出了H-GCN，PL（可编程逻辑）和AIE（AI引擎）的混合加速器，以利用Xilinx Versal自适应计算加速度平台（ACAPS）的新兴异质性（ACAPS）来实现高表现GNN的确定。特别是，H-GCN根据其固有的异质性将每个图分为三个子图，并分别使用PL和AIE处理它们。为了进一步提高性能，我们探索了AIE的稀疏支持，并开发了一种有效的密度感知方法，以自动将稀疏矩阵矩阵乘法（SPMM）的瓷砖自动映射到收缩张量数阵列上。与最先进的GCN加速器相比，H-GCN平均达到1.1〜2.3倍的速度。

近年来，抑郁症的发病率在全世界迅速上升，但大规模的抑郁症筛查仍然具有挑战性。步态分析提供了抑郁症的非接触，低成本和高效的早期筛查方法。然而，基于步态分析的抑郁症的早期筛查缺乏足够的有效样本数据。在本文中，我们提出了一种用于评估抑郁症风险的骨架数据增强方法。首先，我们提出了五种技术来增加骨架数据并将其应用于抑郁和情感数据集。然后，我们将增强方法分为两种类型（非噪声增强和噪声增强），基于互信息和分类准确性。最后，我们探索了哪些增强策略可以更有效地捕捉人骨架数据的特征。实验结果表明，保留了更多原始骨架数据属性的增强训练数据集确定了检测模型的性能。具体而言，旋转增强和通道掩码增强使抑郁检测精度分别达到92.15％和91.34％。

培训广泛和深度神经网络（DNN）需要大量的存储资源，例如内存，因为在转发传播期间必须在存储器中保存中间激活数据，然后恢复以便向后传播。然而，由于硬件设计约束，诸如GPU之类的最先进的加速器（例如GPU）仅配备了非常有限的存储容量，这显着限制了在训练大规模DNN时的最大批量大小和性能加速。传统的记忆保存技术均受性能开销或受限互连带宽或特定互连技术的约束。在本文中，我们提出了一种新颖的记忆高效的CNN训练框架（称为Comet），利用错误界限的损耗压缩来显着降低训练的内存要求，以允许培训更大的模型或加速培训。不同于采用基于图像的有损压缩机（例如JPEG）的最先进的解决方案来压缩激活数据，我们的框架故意采用严格的错误控制机制来采用错误界限的损耗压缩。具体而言，我们对从改变的激活数据传播到梯度的压缩误差传播的理论分析，并经验探讨改变梯度对训练过程的影响。基于这些分析，我们优化了误报的损耗压缩，并提出了一种用于激活数据压缩的自适应误差控制方案。我们评估我们对最先进的解决方案的设计，其中包含五个广泛采用的CNN和Imagenet DataSet。实验表明，我们所提出的框架可以在基线训练中显着降低13.5倍，并分别在另一个最先进的基于压缩框架上的1.8倍，几乎没有准确性损失。

Reinforcement learning often suffer from the sparse reward issue in real-world robotics problems. Learning from demonstration (LfD) is an effective way to eliminate this problem, which leverages collected expert data to aid online learning. Prior works often assume that the learning agent and the expert aim to accomplish the same task, which requires collecting new data for every new task. In this paper, we consider the case where the target task is mismatched from but similar with that of the expert. Such setting can be challenging and we found existing LfD methods can not effectively guide learning in mismatched new tasks with sparse rewards. We propose conservative reward shaping from demonstration (CRSfD), which shapes the sparse rewards using estimated expert value function. To accelerate learning processes, CRSfD guides the agent to conservatively explore around demonstrations. Experimental results of robot manipulation tasks show that our approach outperforms baseline LfD methods when transferring demonstrations collected in a single task to other different but similar tasks.

Positive-Unlabeled (PU) learning aims to learn a model with rare positive samples and abundant unlabeled samples. Compared with classical binary classification, the task of PU learning is much more challenging due to the existence of many incompletely-annotated data instances. Since only part of the most confident positive samples are available and evidence is not enough to categorize the rest samples, many of these unlabeled data may also be the positive samples. Research on this topic is particularly useful and essential to many real-world tasks which demand very expensive labelling cost. For example, the recognition tasks in disease diagnosis, recommendation system and satellite image recognition may only have few positive samples that can be annotated by the experts. These methods mainly omit the intrinsic hardness of some unlabeled data, which can result in sub-optimal performance as a consequence of fitting the easy noisy data and not sufficiently utilizing the hard data. In this paper, we focus on improving the commonly-used nnPU with a novel training pipeline. We highlight the intrinsic difference of hardness of samples in the dataset and the proper learning strategies for easy and hard data. By considering this fact, we propose first splitting the unlabeled dataset with an early-stop strategy. The samples that have inconsistent predictions between the temporary and base model are considered as hard samples. Then the model utilizes a noise-tolerant Jensen-Shannon divergence loss for easy data; and a dual-source consistency regularization for hard data which includes a cross-consistency between student and base model for low-level features and self-consistency for high-level features and predictions, respectively.

The task of Few-shot learning (FSL) aims to transfer the knowledge learned from base categories with sufficient labelled data to novel categories with scarce known information. It is currently an important research question and has great practical values in the real-world applications. Despite extensive previous efforts are made on few-shot learning tasks, we emphasize that most existing methods did not take into account the distributional shift caused by sample selection bias in the FSL scenario. Such a selection bias can induce spurious correlation between the semantic causal features, that are causally and semantically related to the class label, and the other non-causal features. Critically, the former ones should be invariant across changes in distributions, highly related to the classes of interest, and thus well generalizable to novel classes, while the latter ones are not stable to changes in the distribution. To resolve this problem, we propose a novel data augmentation strategy dubbed as PatchMix that can break this spurious dependency by replacing the patch-level information and supervision of the query images with random gallery images from different classes from the query ones. We theoretically show that such an augmentation mechanism, different from existing ones, is able to identify the causal features. To further make these features to be discriminative enough for classification, we propose Correlation-guided Reconstruction (CGR) and Hardness-Aware module for instance discrimination and easier discrimination between similar classes. Moreover, such a framework can be adapted to the unsupervised FSL scenario.

CNN-based surrogates have become prevalent in scientific applications to replace conventional time-consuming physical approaches. Although these surrogates can yield satisfactory results with significantly lower computation costs over small training datasets, our benchmarking results show that data-loading overhead becomes the major performance bottleneck when training surrogates with large datasets. In practice, surrogates are usually trained with high-resolution scientific data, which can easily reach the terabyte scale. Several state-of-the-art data loaders are proposed to improve the loading throughput in general CNN training; however, they are sub-optimal when applied to the surrogate training. In this work, we propose SOLAR, a surrogate data loader, that can ultimately increase loading throughput during the training. It leverages our three key observations during the benchmarking and contains three novel designs. Specifically, SOLAR first generates a pre-determined shuffled index list and accordingly optimizes the global access order and the buffer eviction scheme to maximize the data reuse and the buffer hit rate. It then proposes a tradeoff between lightweight computational imbalance and heavyweight loading workload imbalance to speed up the overall training. It finally optimizes its data access pattern with HDF5 to achieve a better parallel I/O throughput. Our evaluation with three scientific surrogates and 32 GPUs illustrates that SOLAR can achieve up to 24.4X speedup over PyTorch Data Loader and 3.52X speedup over state-of-the-art data loaders.

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.

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.