联合学习（FL）为培训机器学习模型打开了新的观点，同时将个人数据保存在用户场所上。具体而言，在FL中，在用户设备上训练了模型，并且仅将模型更新（即梯度）发送到中央服务器以进行聚合目的。但是，近年来发表的一系列推理攻击泄漏了私人数据，这强调了需要设计有效的保护机制来激励FL的大规模采用。尽管存在缓解服务器端的这些攻击的解决方案，但几乎没有采取任何措施来保护用户免受客户端执行的攻击。在这种情况下，在客户端使用受信任的执行环境（TEE）是最建议的解决方案之一。但是，现有的框架（例如，Darknetz）需要静态地将机器学习模型的很大一部分放入T恤中，以有效防止复杂的攻击或攻击组合。我们提出了GradSec，该解决方案允许在静态或动态上仅在机器学习模型的TEE上进行保护，因此将TCB的大小和整体训练时间降低了30％和56％，相比之下 - 艺术竞争者。

推荐系统被证明是提取与用户相关的内容帮助用户进行日常活动的宝贵工具（例如，找到相关的访问地点，要消费的内容，要购买的商品）。但是，为了有效，这些系统需要收集和分析大量个人数据（例如，位置检查，电影评分，点击率等），这使用户面临许多隐私威胁。在这种情况下，基于联合学习（FL）的推荐系统似乎是一个有前途的解决方案，可以在计算准确的建议的同时将个人数据保存在用户设备上时，是一个有前途的解决方案。但是，FL，因此基于FL的推荐系统，依靠中央服务器，除了容易受到攻击外，还可以遇到可伸缩性问题。为了解决这个问题，我们提出了基于八卦学习原理的分散推荐系统Pepper。在胡椒中，用户八卦模型更新并不同步。 Pepper的核心位于两个关键组成部分：一个个性化的同行采样协议，该协议保存在每个节点附近，这是与前者具有相似兴趣的节点的一部分，以及一个简单而有效的模型汇总功能，该功能构建了一个模型更适合每个用户。通过在三个实施两个用例的实验实验中进行实验：位置入住建议和电影推荐，我们证明我们的解决方案比其他分散的解决方案快42％收敛于42％与分散的竞争对手相比，长时间性能的命中率和高达21％的速度提高了21％。

We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language. We discuss the dominant datasets within each theme, evaluation metrics for the challenges, and the performance of state-of-the-art models. We highlight commonalities between top approaches to the challenges and identify potential future directions for Embodied AI research.

量化是在嵌入式系统或手机上部署训练有素的DNN模型时，是最应用的深神经网络（DNN）压缩策略之一。这是由于其对广泛的应用和情况的简单性和适应性，而不是特定的人工智能（AI）加速器和编译器，这些加速器和编译器通常仅用于某些特定的硬件（例如Google Coral Edge TPU）。随着对量化的需求不断增长，确保该策略的可靠性成为一个关键挑战。传统的测试方法收集越来越多的真实数据以进行更好的评估，通常是不切实际的，因为输入空间的尺寸很大，并且原始DNN及其量化的对应物之间的相似性很高。结果，高级评估策略已变得至关重要。在本文中，我们提出了Diverget，这是一个基于搜索的测试框架，用于量化评估。 Diverget定义了变质关系的空间，该空间模拟了输入上的自然扭曲。然后，它最佳地探索了这些关系，以揭示不同算术精度的DNN之间的分歧。我们评估了应用于高光谱遥感图像的最先进的DNN上的Diverget的性能。我们选择了遥感DNN，因为它们越来越多地部署在诸如气候变化研究和天文学之类的关键领域中的边缘（例如，高级无人机）。我们的结果表明，Diverget成功地挑战了已建立的量化技术的鲁棒性，以防止自然变化的数据，并胜过其最新的并发，Diffchaser，其成功率（平均）是四倍。

Extracting complex structures from grid-based data is a common key step in automated medical image analysis. The conventional solution to recovering tree-structured geometries typically involves computing the minimal cost path through intermediate representations derived from segmentation masks. However, this methodology has significant limitations in the context of projective imaging of tree-structured 3D anatomical data such as coronary arteries, since there are often overlapping branches in the 2D projection. In this work, we propose a novel approach to predicting tree connectivity structure which reformulates the task as an optimization problem over individual steps of a recursive process. We design and train a two-stage model which leverages the UNet and Transformer architectures and introduces an image-based prompting technique. Our proposed method achieves compelling results on a pair of synthetic datasets, and outperforms a shortest-path baseline.

Curriculum learning and self-paced learning are the training strategies that gradually feed the samples from easy to more complex. They have captivated increasing attention due to their excellent performance in robotic vision. Most recent works focus on designing curricula based on difficulty levels in input samples or smoothing the feature maps. However, smoothing labels to control the learning utility in a curriculum manner is still unexplored. In this work, we design a paced curriculum by label smoothing (P-CBLS) using paced learning with uniform label smoothing (ULS) for classification tasks and fuse uniform and spatially varying label smoothing (SVLS) for semantic segmentation tasks in a curriculum manner. In ULS and SVLS, a bigger smoothing factor value enforces a heavy smoothing penalty in the true label and limits learning less information. Therefore, we design the curriculum by label smoothing (CBLS). We set a bigger smoothing value at the beginning of training and gradually decreased it to zero to control the model learning utility from lower to higher. We also designed a confidence-aware pacing function and combined it with our CBLS to investigate the benefits of various curricula. The proposed techniques are validated on four robotic surgery datasets of multi-class, multi-label classification, captioning, and segmentation tasks. We also investigate the robustness of our method by corrupting validation data into different severity levels. Our extensive analysis shows that the proposed method improves prediction accuracy and robustness.

Temporal reasoning is the task of predicting temporal relations of event pairs with corresponding contexts. While some temporal reasoning models perform reasonably well on in-domain benchmarks, we have little idea of the systems' generalizability due to existing datasets' limitations. In this work, we introduce a novel task named TODAY that bridges this gap with temporal differential analysis, which as the name suggests, evaluates if systems can correctly understand the effect of incremental changes. Specifically, TODAY makes slight context changes for given event pairs, and systems need to tell how this subtle contextual change will affect temporal relation distributions. To facilitate learning, TODAY also annotates human explanations. We show that existing models, including GPT-3, drop to random guessing on TODAY, suggesting that they heavily rely on spurious information rather than proper reasoning for temporal predictions. On the other hand, we show that TODAY's supervision style and explanation annotations can be used in joint learning and encourage models to use more appropriate signals during training and outperform across several benchmarks. TODAY can also be used to train models to solicit incidental supervision from noisy sources such as GPT-3 and moves farther towards generic temporal reasoning systems.

State-of-the-art 3D semantic segmentation models are trained on the off-the-shelf public benchmarks, but they often face the major challenge when these well-trained models are deployed to a new domain. In this paper, we propose an Active-and-Adaptive Segmentation (ADAS) baseline to enhance the weak cross-domain generalization ability of a well-trained 3D segmentation model, and bridge the point distribution gap between domains. Specifically, before the cross-domain adaptation stage begins, ADAS performs an active sampling operation to select a maximally-informative subset from both source and target domains for effective adaptation, reducing the adaptation difficulty under 3D scenarios. Benefiting from the rise of multi-modal 2D-3D datasets, ADAS utilizes a cross-modal attention-based feature fusion module that can extract a representative pair of image features and point features to achieve a bi-directional image-point feature interaction for better safe adaptation. Experimentally, ADAS is verified to be effective in many cross-domain settings including: 1) Unsupervised Domain Adaptation (UDA), which means that all samples from target domain are unlabeled; 2) Unsupervised Few-shot Domain Adaptation (UFDA) which means that only a few unlabeled samples are available in the unlabeled target domain; 3) Active Domain Adaptation (ADA) which means that the selected target samples by ADAS are manually annotated. Their results demonstrate that ADAS achieves a significant accuracy gain by easily coupling ADAS with self-training methods or off-the-shelf UDA works.

As language models (LMs) scale, they develop many novel behaviors, good and bad, exacerbating the need to evaluate how they behave. Prior work creates evaluations with crowdwork (which is time-consuming and expensive) or existing data sources (which are not always available). Here, we automatically generate evaluations with LMs. We explore approaches with varying amounts of human effort, from instructing LMs to write yes/no questions to making complex Winogender schemas with multiple stages of LM-based generation and filtering. Crowdworkers rate the examples as highly relevant and agree with 90-100% of labels, sometimes more so than corresponding human-written datasets. We generate 154 datasets and discover new cases of inverse scaling where LMs get worse with size. Larger LMs repeat back a dialog user's preferred answer ("sycophancy") and express greater desire to pursue concerning goals like resource acquisition and goal preservation. We also find some of the first examples of inverse scaling in RL from Human Feedback (RLHF), where more RLHF makes LMs worse. For example, RLHF makes LMs express stronger political views (on gun rights and immigration) and a greater desire to avoid shut down. Overall, LM-written evaluations are high-quality and let us quickly discover many novel LM behaviors.

In this paper, we discuss an imitation learning based method for reducing the calibration error for a mixed reality system consisting of a vision sensor and a projector. Unlike a head mounted display, in this setup, augmented information is available to a human subject via the projection of a scene into the real world. Inherently, the camera and projector need to be calibrated as a stereo setup to project accurate information in 3D space. Previous calibration processes require multiple recording and parameter tuning steps to achieve the desired calibration, which is usually time consuming process. In order to avoid such tedious calibration, we train a CNN model to iteratively correct the extrinsic offset given a QR code and a projected pattern. We discuss the overall system setup, data collection for training, and results of the auto-correction model.