Code generation models have achieved impressive performance. However, they tend to be brittle as slight edits to a prompt could lead to very different generations; these robustness properties, critical for user experience when deployed in real-life applications, are not well understood. Most existing works on robustness in text or code tasks have focused on classification, while robustness in generation tasks is an uncharted area and to date there is no comprehensive benchmark for robustness in code generation. In this paper, we propose ReCode, a comprehensive robustness evaluation benchmark for code generation models. We customize over 30 transformations specifically for code on docstrings, function and variable names, code syntax, and code format. They are carefully designed to be natural in real-life coding practice, preserve the original semantic meaning, and thus provide multifaceted assessments of a model's robustness performance. With human annotators, we verified that over 90% of the perturbed prompts do not alter the semantic meaning of the original prompt. In addition, we define robustness metrics for code generation models considering the worst-case behavior under each type of perturbation, taking advantage of the fact that executing the generated code can serve as objective evaluation. We demonstrate ReCode on SOTA models using HumanEval, MBPP, as well as function completion tasks derived from them. Interesting observations include: better robustness for CodeGen over InCoder and GPT-J; models are most sensitive to syntax perturbations; more challenging robustness evaluation on MBPP over HumanEval.

Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging task in computer vision. Mainstream approaches follow a multi-stage framework and suffer from high training costs. In this paper, we explore the potential of Contrastive Language-Image Pre-training models (CLIP) to localize different categories with only image-level labels and without any further training. To efficiently generate high-quality segmentation masks from CLIP, we propose a novel framework called CLIP-ES for WSSS. Our framework improves all three stages of WSSS with special designs for CLIP: 1) We introduce the softmax function into GradCAM and exploit the zero-shot ability of CLIP to suppress the confusion caused by non-target classes and backgrounds. Meanwhile, to take full advantage of CLIP, we re-explore text inputs under the WSSS setting and customize two text-driven strategies: sharpness-based prompt selection and synonym fusion. 2) To simplify the stage of CAM refinement, we propose a real-time class-aware attention-based affinity (CAA) module based on the inherent multi-head self-attention (MHSA) in CLIP-ViTs. 3) When training the final segmentation model with the masks generated by CLIP, we introduced a confidence-guided loss (CGL) to mitigate noise and focus on confident regions. Our proposed framework dramatically reduces the cost of training for WSSS and shows the capability of localizing objects in CLIP. Our CLIP-ES achieves SOTA performance on Pascal VOC 2012 and MS COCO 2014 while only taking 10% time of previous methods for the pseudo mask generation. Code is available at https://github.com/linyq2117/CLIP-ES.

In recent years, using a self-supervised learning framework to learn the general characteristics of graphs has been considered a promising paradigm for graph representation learning. The core of self-supervised learning strategies for graph neural networks lies in constructing suitable positive sample selection strategies. However, existing GNNs typically aggregate information from neighboring nodes to update node representations, leading to an over-reliance on neighboring positive samples, i.e., homophilous samples; while ignoring long-range positive samples, i.e., positive samples that are far apart on the graph but structurally equivalent samples, a problem we call "neighbor bias." This neighbor bias can reduce the generalization performance of GNNs. In this paper, we argue that the generalization properties of GNNs should be determined by combining homogeneous samples and structurally equivalent samples, which we call the "GC combination hypothesis." Therefore, we propose a topological signal-driven self-supervised method. It uses a topological information-guided structural equivalence sampling strategy. First, we extract multiscale topological features using persistent homology. Then we compute the structural equivalence of node pairs based on their topological features. In particular, we design a topological loss function to pull in non-neighboring node pairs with high structural equivalence in the representation space to alleviate neighbor bias. Finally, we use the joint training mechanism to adjust the effect of structural equivalence on the model to fit datasets with different characteristics. We conducted experiments on the node classification task across seven graph datasets. The results show that the model performance can be effectively improved using a strategy of topological signal enhancement.

Video-and-language pre-training has shown promising results for learning generalizable representations. Most existing approaches usually model video and text in an implicit manner, without considering explicit structural representations of the multi-modal content. We denote such form of representations as structural knowledge, which express rich semantics of multiple granularities. There are related works that propose object-aware approaches to inject similar knowledge as inputs. However, the existing methods usually fail to effectively utilize such knowledge as regularizations to shape a superior cross-modal representation space. To this end, we propose a Cross-modaL knOwledge-enhanced Pre-training (CLOP) method with Knowledge Regularizations. There are two key designs of ours: 1) a simple yet effective Structural Knowledge Prediction (SKP) task to pull together the latent representations of similar videos; and 2) a novel Knowledge-guided sampling approach for Contrastive Learning (KCL) to push apart cross-modal hard negative samples. We evaluate our method on four text-video retrieval tasks and one multi-choice QA task. The experiments show clear improvements, outperforming prior works by a substantial margin. Besides, we provide ablations and insights of how our methods affect the latent representation space, demonstrating the value of incorporating knowledge regularizations into video-and-language pre-training.

视觉变压器（VIT）最近在一系列计算机视觉任务中占据了主导地位，但训练数据效率低下，局部语义表示能力较低，而没有适当的电感偏差。卷积神经网络（CNNS）固有地捕获了区域感知语义，激发了研究人员将CNN引入VIT的架构中，以为VIT提供理想的诱导偏见。但是，嵌入在VIT中的微型CNN实现的位置是否足够好？在本文中，我们通过深入探讨混合CNNS/VIT的宏观结构如何增强层次VIT的性能。特别是，我们研究了令牌嵌入层，别名卷积嵌入（CE）的作用，并系统地揭示了CE如何在VIT中注入理想的感应偏置。此外，我们将最佳CE配置应用于最近发布的4个最先进的Vits，从而有效地增强了相应的性能。最后，释放了一个有效的混合CNN/VIT家族，称为CETNET，可以用作通用的视觉骨架。具体而言，CETNET在Imagenet-1K上获得了84.9％的TOP-1准确性（从头开始训练），可可基准上的48.6％的盒子地图和ADE20K上的51.6％MIOU，从而显着提高了相应的最新态度的性能。艺术基线。

进化游戏理论一直是将经典游戏理论与多动力系统中的学习动力描述相结合的成功工具。前提是一些相互作用玩家的对称结构，许多研究一直集中在使用简化的启发式收益表作为分析相互作用动态的输入。然而，即使对于最先进的方法，也有两个限制。首先，分析简化的收益表时存在不准确性。其次，没有现有的工作能够处理2种人口多人游戏不对称游戏。在本文中，我们填补了启发式收益表和动态分析之间的空白，而无需任何不准确。此外，我们为$ m $ $ n $ n $ 2人群的多人游戏提出了一个通用框架。然后，我们将方法与一些经典游戏中的最新方法进行了比较。最后，为了说明我们的方法，我们对Wolfpack和Starcraft II进行了经验游戏理论分析，这两者都涉及复杂的多基因相互作用。

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％。

用于图像文本生成任务的传统方法主要是分别解决自然双向生成任务，专注于设计任务特定的框架以提高所生成的样本的质量和保真度。最近，Vision-Language预训练模型大大提高了图像到文本生成任务的性能，但仍未开发出用于文本到图像综合任务的大规模预训练模型。在本文中，我们提出了一个具有变压器模型的双向图像文本生成的统一生成的预训练框架的Ernie-Vi​​lg。基于图像量化模型，我们将图像生成和文本生成标准为在文本/图像输入上调节的自回归生成任务。双向图像文本生成建模简化了视觉和语言的语义对齐。对于文本到图像生成过程，我们进一步提出了端到端的训练方法，共同学习视觉序列发生器和图像重建。为了探讨双向文本图像生成的大规模预培训景观，我们在大规模数据集中培训了100亿参数的Ernie-Vi​​lg模型，以145百万（中文）图像 - 文本对实现了达到的状态 - 文本到图像和图像到文本任务的最佳性能，以便在MS-Coco上获取7.9的FID，用于文本到图像合成以及用于图像标题的Coco-CN和AIC-ICC的最佳结果。

预先接受的语言模型实现了最先进的导致各种自然语言处理（NLP）任务。 GPT-3表明，缩放预先训练的语言模型可以进一步利用它们的巨大潜力。最近提出了一个名为Ernie 3.0的统一框架，以预先培训大型知识增强型号，并培训了具有10亿参数的模型。 Ernie 3.0在各种NLP任务上表现出最先进的模型。为了探讨缩放的表现，我们培养了百卢比的3.0泰坦参数型号，在PaddlePaddle平台上有高达260亿参数的泰坦。此外，我们设计了一种自我监督的对抗性损失和可控语言建模损失，以使ERNIE 3.0 TITAN产生可信和可控的文本。为了减少计算开销和碳排放，我们向Ernie 3.0泰坦提出了一个在线蒸馏框架，教师模型将同时教授学生和培训。埃塞尼3.0泰坦是迄今为止最大的中国密集预训练模型。经验结果表明，Ernie 3.0泰坦在68个NLP数据集中优于最先进的模型。

离线强化学习利用静态数据集来学习最佳策略，无需访问环境。由于代理商在线交互的展示和培训期间的样本数量，这种技术对于多代理学习任务是可取的。然而，在多代理强化学习（Marl）中，从未研究过在线微调的离线预训练的范式从未研究过，可以使用离线MARL研究的数据集或基准。在本文中，我们试图回答违规在Marl中的离线培训是否能够学习一般的政策表现，这些问题可以帮助提高多个下游任务的性能。我们首先引入基于Starcraftia环境的不同质量水平的第一个离线Marl数据集，然后提出了用于有效的离线学习的多代理决策变压器（MADT）的新颖体系结构。 MADT利用变换器的时间表示的建模能力，并将其与离线和在线MARL任务集成。 Madt的一个至关重要的好处是，它学会了可以在不同任务场景下不同类型的代理之间转移的可稳定性政策。当在脱机目的Datline数据上进行评估时，Madt展示了比最先进的离线RL基线的性能卓越。当应用于在线任务时，预先训练的MADT显着提高了样品效率，即使在零射击案件中也享有强大的性能。为了我们的最佳知识，这是第一个研究并展示了在Marl中的样本效率和最常性增强方面的离线预训练模型的有效性。