室外（OOD）检测是面向任务的对话框系统中的关键组件，旨在确定查询是否不在预定义的支持的意图集之外。事实证明，先前基于软磁性的检测算法对OOD样品被过度自信。在本文中，我们分析了过度自信的OOD来自由于训练和测试分布之间的不匹配而导致的分布不确定性，这使得该模型无法自信地做出预测，因此可能导致异常软磁得分。我们提出了一个贝叶斯OOD检测框架，以使用Monte-Carlo辍学来校准分布不确定性。我们的方法是灵活的，并且可以轻松地插入现有的基于软磁性的基线和增益33.33 \％OOD F1改进，而与MSP相比仅增加了0.41 \％的推理时间。进一步的分析表明，贝叶斯学习对OOD检测的有效性。

传统意图分类模型基于预定义的意图集，仅识别有限的内域（IND）意图类别。但是用户可以在实用的对话系统中输入室外（OOD）查询。这样的OOD查询可以提供未来改进的方向。在本文中，我们定义了一项新任务，广义意图发现（GID），旨在将IND意图分类器扩展到包括IND和OOD意图在内的开放世界意图集。我们希望在发现和识别新的未标记的OOD类型的同时，同时对一组标记的IND意图类进行分类。我们为不同的应用程序方案构建了三个公共数据集，并提出了两种框架，即基于管道的框架和端到端，以实现未来的工作。此外，我们进行详尽的实验和定性分析，以理解关键挑战，并为未来的GID研究提供新的指导。

This paper presents a novel framework for planning in unknown and occluded urban spaces. We specifically focus on turns and intersections where occlusions significantly impact navigability. Our approach uses an inpainting model to fill in a sparse, occluded, semantic lidar point cloud and plans dynamically feasible paths for a vehicle to traverse through the open and inpainted spaces. We demonstrate our approach using a car's lidar data with real-time occlusions, and show that by inpainting occluded areas, we can plan longer paths, with more turn options compared to without inpainting; in addition, our approach more closely follows paths derived from a planner with no occlusions (called the ground truth) compared to other state of the art approaches.

Semi-supervised learning (SSL) has made significant strides in the field of remote sensing. Finding a large number of labeled datasets for SSL methods is uncommon, and manually labeling datasets is expensive and time-consuming. Furthermore, accurately identifying remote sensing satellite images is more complicated than it is for conventional images. Class-imbalanced datasets are another prevalent phenomenon, and models trained on these become biased towards the majority classes. This becomes a critical issue with an SSL model's subpar performance. We aim to address the issue of labeling unlabeled data and also solve the model bias problem due to imbalanced datasets while achieving better accuracy. To accomplish this, we create "artificial" labels and train a model to have reasonable accuracy. We iteratively redistribute the classes through resampling using a distribution alignment technique. We use a variety of class imbalanced satellite image datasets: EuroSAT, UCM, and WHU-RS19. On UCM balanced dataset, our method outperforms previous methods MSMatch and FixMatch by 1.21% and 0.6%, respectively. For imbalanced EuroSAT, our method outperforms MSMatch and FixMatch by 1.08% and 1%, respectively. Our approach significantly lessens the requirement for labeled data, consistently outperforms alternative approaches, and resolves the issue of model bias caused by class imbalance in datasets.

Large pretrained language models have shown surprising In-Context Learning (ICL) ability. With a few demonstration input-label pairs, they can predict the label for an unseen input without additional parameter updates. Despite the great success in performance, the working mechanism of ICL still remains an open problem. In order to better understand how ICL works, this paper explains language models as meta-optimizers and understands ICL as a kind of implicit finetuning. Theoretically, we figure out that the Transformer attention has a dual form of gradient descent based optimization. On top of it, we understand ICL as follows: GPT first produces meta-gradients according to the demonstration examples, and then these meta-gradients are applied to the original GPT to build an ICL model. Experimentally, we comprehensively compare the behavior of ICL and explicit finetuning based on real tasks to provide empirical evidence that supports our understanding. The results prove that ICL behaves similarly to explicit finetuning at the prediction level, the representation level, and the attention behavior level. Further, inspired by our understanding of meta-optimization, we design a momentum-based attention by analogy with the momentum-based gradient descent algorithm. Its consistently better performance over vanilla attention supports our understanding again from another aspect, and more importantly, it shows the potential to utilize our understanding for future model designing.

Position modeling plays a critical role in Transformers. In this paper, we focus on length extrapolation, i.e., training on short texts while evaluating longer sequences. We define attention resolution as an indicator of extrapolation. Then we propose two designs to improve the above metric of Transformers. Specifically, we introduce a relative position embedding to explicitly maximize attention resolution. Moreover, we use blockwise causal attention during inference for better resolution. We evaluate different Transformer variants with language modeling. Experimental results show that our model achieves strong performance in both interpolation and extrapolation settings. The code will be available at https://aka.ms/LeX-Transformer.

Large language models have exhibited intriguing in-context learning capability, achieving promising zero- and few-shot performance without updating the parameters. However, conventional in-context learning is usually restricted by length constraints, rendering it ineffective to absorb supervision from a large number of examples. In order to go beyond few shots, we introduce structured prompting that breaks the length limit and scales in-context learning to thousands of examples. Specifically, demonstration examples are separately encoded with well-designed position embeddings, and then they are jointly attended by the test example using a rescaled attention mechanism. So we can scale the number of exemplars with linear complexity instead of quadratic complexity with respect to length. Experimental results on a diverse set of tasks show that our approach improves end-task performance and reduces evaluation variance over conventional in-context learning as the number of demonstration examples increases. Code has been released at https://aka.ms/structured-prompting.

Using a Bayesian network to analyze the causal relationship between nodes is a hot spot. The existing network learning algorithms are mainly constraint-based and score-based network generation methods. The constraint-based method is mainly the application of conditional independence (CI) tests, but the inaccuracy of CI tests in the case of high dimensionality and small samples has always been a problem for the constraint-based method. The score-based method uses the scoring function and search strategy to find the optimal candidate network structure, but the search space increases too much with the increase of the number of nodes, and the learning efficiency is very low. This paper presents a new hybrid algorithm, MCME (multiple compound memory erasing). This method retains the advantages of the first two methods, solves the shortcomings of the above CI tests, and makes innovations in the scoring function in the direction discrimination stage. A large number of experiments show that MCME has better or similar performance than some existing algorithms.

It is crucial to choose the appropriate scale in order to build an effective and informational representation of a complex system. Scientists carefully choose the scales for their experiments to extract the variables that describe the causalities in the system. They found that the coarse scale(macro) is sometimes more causal and informative than the numerous-parameter observations(micro). The phenomenon that the causality emerges by coarse-graining is called Causal Emergence(CE). Based on information theory, a number of recent works quantitatively showed that CE indeed happens while coarse-graining a micro model to the macro. However, the existing works have not discussed the question of why and when the CE happens. We quantitatively analyze the redistribution of uncertainties for coarse-graining and suggest that the redistribution of uncertainties is the cause of causal emergence. We further analyze the thresholds that determine if CE happens or not. From the regularity of the transition probability matrix(TPM) of discrete systems, the mathematical expressions of the model properties are derived. The values of thresholds for different operations are computed. The results provide the critical and specific conditions of CE as helpful suggestions for choosing the proper coarse-graining operation. The results also provided a new way to better understand the nature of causality and causal emergence.

This work focuses on 3D Radar imaging inverse problems. Current methods obtain undifferentiated results that suffer task-depended information retrieval loss and thus don't meet the task's specific demands well. For example, biased scattering energy may be acceptable for screen imaging but not for scattering diagnosis. To address this issue, we propose a new task-oriented imaging framework. The imaging principle is task-oriented through an analysis phase to obtain task's demands. The imaging model is multi-cognition regularized to embed and fulfill demands. The imaging method is designed to be general-ized, where couplings between cognitions are decoupled and solved individually with approximation and variable-splitting techniques. Tasks include scattering diagnosis, person screen imaging, and parcel screening imaging are given as examples. Experiments on data from two systems indicate that the pro-posed framework outperforms the current ones in task-depended information retrieval.