The image captioning task is typically realized by an auto-regressive method that decodes the text tokens one by one. We present a diffusion-based captioning model, dubbed the name DDCap, to allow more decoding flexibility. Unlike image generation, where the output is continuous and redundant with a fixed length, texts in image captions are categorical and short with varied lengths. Therefore, naively applying the discrete diffusion model to text decoding does not work well, as shown in our experiments. To address the performance gap, we propose several key techniques including best-first inference, concentrated attention mask, text length prediction, and image-free training. On COCO without additional caption pre-training, it achieves a CIDEr score of 117.8, which is +5.0 higher than the auto-regressive baseline with the same architecture in the controlled setting. It also performs +26.8 higher CIDEr score than the auto-regressive baseline (230.3 v.s.203.5) on a caption infilling task. With 4M vision-language pre-training images and the base-sized model, we reach a CIDEr score of 125.1 on COCO, which is competitive to the best well-developed auto-regressive frameworks. The code is available at https://github.com/buxiangzhiren/DDCap.

由于其在多个工业应用领域的竞争性能，深度学习在我们的日常生活中起着越来越重要的作用。作为基于DL的系统的核心，深度神经网络会自动从精心收集和有组织的培训数据中学习知识，以获得预测看不见数据的标签的能力。与需要全面测试的传统软件系统类似，还需要仔细评估DNN，以确保受过训练的模型的质量满足需求。实际上，评估行业中DNN质量的事实上的标准是检查其在收集的标记测试数据集中的性能（准确性）。但是，准备这样的标记数据通常不容易部分，部分原因是标签工作巨大，即数据标记是劳动密集型的，尤其是每天有大量新的新传入的未标记数据。最近的研究表明，DNN的测试选择是一个有希望的方向，可以通过选择最小的代表性数据来标记并使用这些数据来评估模型来解决此问题。但是，它仍然需要人类的努力，不能自动。在本文中，我们提出了一种名为Aries的新技术，可以使用原始测试数据获得的信息估算新未标记数据的DNN的性能。我们技术背后的关键见解是，该模型在与决策边界具有相似距离的数据上应具有相似的预测准确性。我们对13种数据转换方法的技术进行了大规模评估。结果表明，我们技术的有用性是，白羊座的估计准确性仅为0.03％-2.60％（平均0.61％），从真实的准确性中差。此外，在大多数（128个）情况下，白羊座还优于最先进的选择标记方法。

在过去的几年中，深度学习（DL）一直在不断扩大其应用程序，并成为大型法规时代大规模源代码分析的推动力。由于意外的准确性降解，测试集与训练集不同的分布与训练集不同的分布与训练集不同。尽管最近在计算机视觉和自然语言过程等领域取得了分配转移基准测试的最新进展。对于源代码任务的分配转移分析和基准测试，进展有限，由于其数量和支持几乎所有工业部门的基础，都有很大的需求。为了填补这一空白，本文启动了提出代码，即用于源代码学习的分销基准数据集。具体而言，代码支持2种编程语言（即Java和Python）和5种代码分发偏移（即任务，程序员，时间戳记，代币和CST）。据我们所知，我们是第一个定义基于代码表示的分布变化的人。在实验中，我们首先评估现有分布探测器的有效性以及分配移位定义的合理性，然后测量流行代码学习模型（例如Codebert）对分类任务的模型概括。结果表明，1）仅基于SoftMax得分的OOD检测器在代码上表现良好，2）分配转移会导致所有代码分类模型中的准确性降解，3）基于表示的分布转移对模型的影响比其他模型具有更高的影响，并且4）预训练的模型对分布变化更具抵抗力。我们公开提供代码，从而实现了有关代码学习模型质量评估的后续研究。

处理聚类问题在数据统计数据统计，模式识别和图像处理中很重要。平均换档算法是一种公共无监督算法，广泛用于解决聚类问题。然而，平均移位算法受其巨额计算资源成本的限制。在以前的研究[10]中，我们提出了一种新型GPU加速的更快的平均移位算法，这大大加快了余弦嵌入的聚类问题。在本研究中，我们扩展并改进了以前的算法来处理欧几里德距离度量。不同于传统的基于GPU的平均移位算法，我们的算法采用新颖的种子选择和早期停止方法，这大大提高了计算速度并降低了GPU存储器消耗。在仿真测试中，在处理200k点聚类问题时，与基于最先进的GPU的平均换档算法相比，我们的算法达到了3次加速度，具有优化的GPU存储器消耗。此外，在本研究中，我们实现了一种用于更快的平均移位算法的即插即用模型，可以轻松地部署。 （即插即用型号可用：https://github.com/masqm/faster-mean-shift-euc）

积极学习是一种降低标签成本以构建高质量机器学习模型的既定技术。主动学习的核心组件是确定应选择哪些数据来注释的采集功能。最先进的采集功能 - 更重要的是主动学习技术 - 已经旨在最大限度地提高清洁性能（例如，准确性）并忽视了鲁棒性，这是一种受到越来越受关注的重要品质。因此，主动学习产生准确但不强大的模型。在本文中，我们提出了一种积极的学习过程，集成了对抗性培训的积极学习过程 - 最熟悉的制作强大模型的方法。通过对11个采集函数的实证研究，4个数据集，6个DNN架构和15105培训的DNN，我们表明，强大的主动学习可以产生具有鲁棒性的模型（对抗性示例的准确性），范围从2.35 \％到63.85 \％，而标准主动学习系统地实现了可忽略不计的鲁棒性（小于0.20 \％）。然而，我们的研究还揭示了在稳健性方面，在准确性上表现良好的采集功能比随机抽样更糟糕。因此，我们检查了它背后的原因，并设计了一个新的采购功能，这些功能既可定位清洁的性能和鲁棒性。我们的采集功能 - 基于熵（DRE）的基于密度的鲁棒采样 - 优于鲁棒性的其他采集功能（包括随机），最高可达24.40 \％（特别是3.84 \％），同时仍然存在竞争力准确性。此外，我们证明了DRE适用于测试选择度量，用于模型再培训，并从所有比较功能中脱颖而出，高达8.21％的鲁棒性。

磁共振成像（MRI）是重要的医学成像模型，而需要长时间的采集时间。为了减少采集​​时间，已经提出了各种方法。然而，这些方法未能以明确的结构重建图像，以两种主要原因。首先，在MR图像中广泛存在的类似补丁，而最先前的基于深度学习的方法忽略此属性，并且仅采用CNN学习本地信息。其次，现有方法仅使用清晰的图像来限制解决方案空间的上限，而下限不会受约束，从而无法获得网络的更好参数。为了解决这些问题，我们向本地和全球学习MRI重建网络（CLGNET）提出了对比的学习。具体地，根据傅立叶理论，傅里叶域中的每个值由空间域中的所有值计算。因此，我们提出了一种空间和傅里叶层（SFL），以同时学习空间和傅立叶域中的本地和全局信息。此外，与自我关注和变压器相比，SFL具有更强的学习能力，可以在更短的时间内实现更好的性能。基于SFL，我们设计了一个空间和傅里叶的剩余块作为模型的主要组成部分。同时，要限制解决方案空间的下限和上限，我们引入了对比度学习，这可以将结果拉到清晰图像上，并将结果推到远离下采样的图像。不同数据集和加速率的广泛实验结果表明，所提出的CLGNET实现了新的最先进的结果。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

Non-linear state-space models, also known as general hidden Markov models, are ubiquitous in statistical machine learning, being the most classical generative models for serial data and sequences in general. The particle-based, rapid incremental smoother PaRIS is a sequential Monte Carlo (SMC) technique allowing for efficient online approximation of expectations of additive functionals under the smoothing distribution in these models. Such expectations appear naturally in several learning contexts, such as likelihood estimation (MLE) and Markov score climbing (MSC). PARIS has linear computational complexity, limited memory requirements and comes with non-asymptotic bounds, convergence results and stability guarantees. Still, being based on self-normalised importance sampling, the PaRIS estimator is biased. Our first contribution is to design a novel additive smoothing algorithm, the Parisian particle Gibbs PPG sampler, which can be viewed as a PaRIS algorithm driven by conditional SMC moves, resulting in bias-reduced estimates of the targeted quantities. We substantiate the PPG algorithm with theoretical results, including new bounds on bias and variance as well as deviation inequalities. Our second contribution is to apply PPG in a learning framework, covering MLE and MSC as special examples. In this context, we establish, under standard assumptions, non-asymptotic bounds highlighting the value of bias reduction and the implicit Rao--Blackwellization of PPG. These are the first non-asymptotic results of this kind in this setting. We illustrate our theoretical results with numerical experiments supporting our claims.

Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro-Reader is one of the studies that has solved this problem effectively. However, the encoders of most traditional machine reading comprehension models in general and Retro-Reader, in particular, have not been able to exploit the contextual semantic information of the context completely. Inspired by SemBERT, we use semantic role labels from the SRL task to add semantics to pre-trained language models such as mBERT, XLM-R, PhoBERT. This experiment was conducted to compare the influence of semantics on the classification of answerability for the Vietnamese machine reading comprehension. Additionally, we hope this experiment will enhance the encoder for the Retro-Reader model's Sketchy Reading Module. The improved Retro-Reader model's encoder with semantics was first applied to the Vietnamese Machine Reading Comprehension task and obtained positive results.

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this work, we leverage interactive machine learning and introduce a joint learning framework, termed DRG-Net, to effectively learn both disease grading and multi-lesion segmentation. Our DRG-Net consists of two modules: (i) DRG-AI-System to classify DR Grading, localize lesion areas, and provide visual explanations; (ii) DRG-Expert-Interaction to receive feedback from user-expert and improve the DRG-AI-System. To deal with sparse data, we utilize transfer learning mechanisms to extract invariant feature representations by using Wasserstein distance and adversarial learning-based entropy minimization. Besides, we propose a novel attention strategy at both low- and high-level features to automatically select the most significant lesion information and provide explainable properties. In terms of human interaction, we further develop DRG-Net as a tool that enables expert users to correct the system's predictions, which may then be used to update the system as a whole. Moreover, thanks to the attention mechanism and loss functions constraint between lesion features and classification features, our approach can be robust given a certain level of noise in the feedback of users. We have benchmarked DRG-Net on the two largest DR datasets, i.e., IDRID and FGADR, and compared it to various state-of-the-art deep learning networks. In addition to outperforming other SOTA approaches, DRG-Net is effectively updated using user feedback, even in a weakly-supervised manner.