现实世界的对抗例（通常以补丁形式）对安全关键计算机视觉任务中的深度学习模型（如在自动驾驶中的视觉感知）中使用深度学习模型构成严重威胁。本文涉及用不同类型的对抗性斑块攻击时，对语义分割模型的稳健性进行了广泛的评价，包括数字，模拟和物理。提出了一种新的损失功能，提高攻击者在诱导像素错误分类方面的能力。此外，提出了一种新的攻击策略，提高了在场景中放置补丁的转换方法的期望。最后，首先扩展用于检测对抗性补丁的最先进的方法以应对语义分割模型，然后改进以获得实时性能，并最终在现实世界场景中进行评估。实验结果表明，尽管具有数字和真实攻击的对抗效果，其影响通常在空间上限制在补丁周围的图像区域。这将打开关于实时语义分段模型的空间稳健性的进一步疑问。

Deep learning techniques with neural networks have been used effectively in computational fluid dynamics (CFD) to obtain solutions to nonlinear differential equations. This paper presents a physics-informed neural network (PINN) approach to solve the Blasius function. This method eliminates the process of changing the non-linear differential equation to an initial value problem. Also, it tackles the convergence issue arising in the conventional series solution. It is seen that this method produces results that are at par with the numerical and conventional methods. The solution is extended to the negative axis to show that PINNs capture the singularity of the function at $\eta=-5.69$

Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether abstractive dialogue summarization can also be used as a means to boost an NLP system's performance on other important dialogue comprehension tasks. In this paper, we propose a novel type of dialogue summarization task - STRUctured DiaLoguE Summarization - that can help pre-trained language models to better understand dialogues and improve their performance on important dialogue comprehension tasks. We further collect human annotations of STRUDEL summaries over 400 dialogues and introduce a new STRUDEL dialogue comprehension modeling framework that integrates STRUDEL into a graph-neural-network-based dialogue reasoning module over transformer encoder language models to improve their dialogue comprehension abilities. In our empirical experiments on two important downstream dialogue comprehension tasks - dialogue question answering and dialogue response prediction - we show that our STRUDEL dialogue comprehension model can significantly improve the dialogue comprehension performance of transformer encoder language models.

A popular approach to creating a zero-shot cross-language retrieval model is to substitute a monolingual pretrained language model in the retrieval model with a multilingual pretrained language model such as Multilingual BERT. This multilingual model is fined-tuned to the retrieval task with monolingual data such as English MS MARCO using the same training recipe as the monolingual retrieval model used. However, such transferred models suffer from mismatches in the languages of the input text during training and inference. In this work, we propose transferring monolingual retrieval models using adapters, a parameter-efficient component for a transformer network. By adding adapters pretrained on language tasks for a specific language with task-specific adapters, prior work has shown that the adapter-enhanced models perform better than fine-tuning the entire model when transferring across languages in various NLP tasks. By constructing dense retrieval models with adapters, we show that models trained with monolingual data are more effective than fine-tuning the entire model when transferring to a Cross Language Information Retrieval (CLIR) setting. However, we found that the prior suggestion of replacing the language adapters to match the target language at inference time is suboptimal for dense retrieval models. We provide an in-depth analysis of this discrepancy between other cross-language NLP tasks and CLIR.

Conditional diffusion probabilistic models can model the distribution of natural images and can generate diverse and realistic samples based on given conditions. However, oftentimes their results can be unrealistic with observable color shifts and textures. We believe that this issue results from the divergence between the probabilistic distribution learned by the model and the distribution of natural images. The delicate conditions gradually enlarge the divergence during each sampling timestep. To address this issue, we introduce a new method that brings the predicted samples to the training data manifold using a pretrained unconditional diffusion model. The unconditional model acts as a regularizer and reduces the divergence introduced by the conditional model at each sampling step. We perform comprehensive experiments to demonstrate the effectiveness of our approach on super-resolution, colorization, turbulence removal, and image-deraining tasks. The improvements obtained by our method suggest that the priors can be incorporated as a general plugin for improving conditional diffusion models.

Generating photos satisfying multiple constraints find broad utility in the content creation industry. A key hurdle to accomplishing this task is the need for paired data consisting of all modalities (i.e., constraints) and their corresponding output. Moreover, existing methods need retraining using paired data across all modalities to introduce a new condition. This paper proposes a solution to this problem based on denoising diffusion probabilistic models (DDPMs). Our motivation for choosing diffusion models over other generative models comes from the flexible internal structure of diffusion models. Since each sampling step in the DDPM follows a Gaussian distribution, we show that there exists a closed-form solution for generating an image given various constraints. Our method can unite multiple diffusion models trained on multiple sub-tasks and conquer the combined task through our proposed sampling strategy. We also introduce a novel reliability parameter that allows using different off-the-shelf diffusion models trained across various datasets during sampling time alone to guide it to the desired outcome satisfying multiple constraints. We perform experiments on various standard multimodal tasks to demonstrate the effectiveness of our approach. More details can be found in https://nithin-gk.github.io/projectpages/Multidiff/index.html

Autonomous driving requires efficient reasoning about the location and appearance of the different agents in the scene, which aids in downstream tasks such as object detection, object tracking, and path planning. The past few years have witnessed a surge in approaches that combine the different taskbased modules of the classic self-driving stack into an End-toEnd(E2E) trainable learning system. These approaches replace perception, prediction, and sensor fusion modules with a single contiguous module with shared latent space embedding, from which one extracts a human-interpretable representation of the scene. One of the most popular representations is the Birds-eye View (BEV), which expresses the location of different traffic participants in the ego vehicle frame from a top-down view. However, a BEV does not capture the chromatic appearance information of the participants. To overcome this limitation, we propose a novel representation that captures various traffic participants appearance and occupancy information from an array of monocular cameras covering 360 deg field of view (FOV). We use a learned image embedding of all camera images to generate a BEV of the scene at any instant that captures both appearance and occupancy of the scene, which can aid in downstream tasks such as object tracking and executing language-based commands. We test the efficacy of our approach on synthetic dataset generated from CARLA. The code, data set, and results can be found at https://rebrand.ly/APP OCC-results.

近年来，基于神经网络的深度恢复方法已实现了最先进的方法，从而导致了各种图像过度的任务。但是，基于深度学习的Deblurring网络的一个主要缺点是，训练需要大量模糊清洁图像对才能实现良好的性能。此外，当测试过程中的模糊图像和模糊内核与训练过程中使用的图像和模糊内核时，深层网络通常无法表现良好。这主要是因为网络参数在培训数据上过度拟合。在这项工作中，我们提出了一种解决这些问题的方法。我们将非盲图像脱毛问题视为一个脱氧问题。为此，我们在一对模糊图像上使用相应的模糊内核进行Wiener过滤。这导致一对具有彩色噪声的图像。因此，造成造成的问题被转化为一个降解问题。然后，我们在不使用明确的清洁目标图像的情况下解决了降解问题。进行了广泛的实验，以表明我们的方法取得了与最先进的非盲人脱毛作品相提并论的结果。

现代监视系统使用基于深度学习的面部验证网络执行人员认可。大多数最先进的面部验证系统都是使用可见光谱图像训练的。但是，在弱光和夜间条件的情况下，在可见光谱中获取图像是不切实际的，并且通常在诸如热红外域之类的替代域中捕获图像。在检索相应的可见域图像后，通常在热图像中进行面部验证。这是一个公认的问题，通常称为热能（T2V）图像翻译。在本文中，我们建议针对面部图像的T2V翻译基于Denoising扩散概率模型（DDPM）解决方案。在训练过程中，该模型通过扩散过程了解了它们相应的热图像，可见面部图像的条件分布。在推断过程中，可见的域图像是通过从高斯噪声开始并反复执行的。 DDPM的现有推理过程是随机且耗时的。因此，我们提出了一种新颖的推理策略，以加快DDPM的推理时间，特别是用于T2V图像翻译问题。我们在多个数据集上实现了最新结果。代码和验证的模型可在http://github.com/nithin-gk/t2v-ddpm上公开获得

我们提出了一种数据驱动的算法，广告商可以用来自动在线出版商的数字广告广告。该算法使广告客户能够跨越可用的目标受众和AD-Media搜索通过在线实验找到其广告系列的最佳组合。找到最佳受众ad AD组合的问题使许多独特的挑战变得复杂，包括（a）需要积极探索以解决先前的不确定性并加快搜索有利可图的组合，（b）许多组合可供选择，产生高维搜索公式，以及（c）成功概率非常低，通常只有百分之一。我们的算法（指定的LRDL，logistic回归与Debiased Lasso的首字母缩写）通过结合四个元素来解决这些挑战：一个用于主动探索的多层匪徒框架；套索惩罚功能以处理高维度；一个内置的偏见核，可处理套索引起的正则化偏差；以及一个半参数回归模型，用于促进跨武器交叉学习的结果。该算法是作为汤普森采样器实施的，据我们所知，这是第一个实际上可以解决以上所有挑战的方法。具有真实和合成数据的模拟表明该方法是有效的，并记录了其在最近的高维匪徒文献中的几个基准测试中的出色性能。