当许多松散相关的未标记数据可用并且稀缺标记的数据时，机器智能的范式从纯粹的监督学习转变为更实用的情况。大多数现有算法都假定基础任务分布是固定的。在这里，我们考虑了随着时间的推移，该任务分布中的一个更现实和具有挑战性的环境会不断发展。我们将这个问题称为半监督的元学习，并具有不断发展的任务分布，缩写为集合。在这种更现实的环境中出现了两个关键挑战：（i）在存在大量未标记的分发（OOD）数据的情况下，如何使用未标记的数据； （ii）如何防止由于任务分配转移而导致先前学习的任务分布的灾难性遗忘。我们提出了一种强大的知识和知识保留的半监督元学习方法（秩序），以应对这两个主要挑战。具体而言，我们的订单引入了一种新型的共同信息正则化，以使用未标记的OOD数据鲁棒化模型，并采用最佳的运输正规化来记住以前在特征空间中学习的知识。此外，我们在一个非常具有挑战性的数据集上测试我们的方法：大规模非平稳的半监督任务分布的集合，该任务分布由（至少）72K任务组成。通过广泛的实验，我们证明了拟议的订单减轻了忘记不断发展的任务分布，并且对OOD数据比相关的强基础更强大。

无任务持续学习（CL）旨在学习非平稳数据流，而无需明确的任务定义，不要忘记以前的知识。广泛采用的内存重播方法可能会逐渐对长数据流有效，因为该模型可能会记住存储的示例并过度拟合内存缓冲区。其次，现有方法忽略了内存数据分布的高不确定性，因为内存数据分布与所有先前数据示例的分布之间存在很大差距。为了解决这些问题，我们首次提出了一个原则的内存演进框架，以使内存缓冲区逐渐难以通过分布强大的优化（DRO）来动态发展内存数据分布。然后，我们得出了一个方法家族，以通过Wasserstein梯度流（WGF）在连续概率中进化内存缓冲区数据。所提出的DRO是W.R.T最糟糕的记忆数据分布，因此保证了模型性能，并且比现有基于内存重新播放的方法更加可靠的功能。对现有基准测试的广泛实验证明了拟议方法减轻遗忘的有效性。作为拟议框架的副产品，与现有的无任务CL方法相比，我们的方法对对抗性示例更强大。

基于深度神经网络的EEG解码系统已广泛用于大脑计算机接口（BCI）的决策制作。然而，在EEG信号中的显着方差和噪声，它们的预测可能是不可靠的。以前的eEG分析工作主要关注源信号中噪声模式的探索，而解码过程中的不确定性主要是未开发的。自动检测和量化这种解码不确定性对于诸如机器人手臂控制等的BCI电机图像等很重要。在这项工作中，我们提出了一个不确定性估计模型（UE-EEG），以探讨EEG解码过程中的不确定性，这考虑了输入信号中的不确定性和模型中的不确定性。采用模型不确定性估计的模型面向模型的模型方法，采用贝叶斯神经网络来建立输入数据的不确定性。该模型可以集成到电流广泛使用的深度学习分类器中，而无需改变架构。我们对两个公共电机图像数据集进行了对主题内部EEG解码和交叉对象eEG解码的不确定性估计进行了广泛的实验，其中拟议的模型实现了对估计不确定性的质量的显着改善，并演示了所提出的UE-EEG是一种有用的BCI应用程序的工具。

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is of high possibility to be degraded due to noises and distortions. In this paper, we propose two novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (i.e., signal and object)-domain curvature regularization model. Fast numerical optimization algorithms are developed relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, which are further accelerated by GPU implementation. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.

In this paper, we target at the problem of learning a generalizable dynamic radiance field from monocular videos. Different from most existing NeRF methods that are based on multiple views, monocular videos only contain one view at each timestamp, thereby suffering from ambiguity along the view direction in estimating point features and scene flows. Previous studies such as DynNeRF disambiguate point features by positional encoding, which is not transferable and severely limits the generalization ability. As a result, these methods have to train one independent model for each scene and suffer from heavy computational costs when applying to increasing monocular videos in real-world applications. To address this, We propose MonoNeRF to simultaneously learn point features and scene flows with point trajectory and feature correspondence constraints across frames. More specifically, we learn an implicit velocity field to estimate point trajectory from temporal features with Neural ODE, which is followed by a flow-based feature aggregation module to obtain spatial features along the point trajectory. We jointly optimize temporal and spatial features by training the network in an end-to-end manner. Experiments show that our MonoNeRF is able to learn from multiple scenes and support new applications such as scene editing, unseen frame synthesis, and fast novel scene adaptation.

In this paper, we propose a large-scale language pre-training for text GENeration using dIffusion modEl, which is named GENIE. GENIE is a pre-training sequence-to-sequence text generation model which combines Transformer and diffusion. The diffusion model accepts the latent information from the encoder, which is used to guide the denoising of the current time step. After multiple such denoise iterations, the diffusion model can restore the Gaussian noise to the diverse output text which is controlled by the input text. Moreover, such architecture design also allows us to adopt large scale pre-training on the GENIE. We propose a novel pre-training method named continuous paragraph denoise based on the characteristics of the diffusion model. Extensive experiments on the XSum, CNN/DailyMail, and Gigaword benchmarks shows that GENIE can achieves comparable performance with various strong baselines, especially after pre-training, the generation quality of GENIE is greatly improved. We have also conduct a lot of experiments on the generation diversity and parameter impact of GENIE. The code for GENIE will be made publicly available.

Structured tabular data exist across nearly all fields. Reasoning task over these data aims to answer questions or determine the truthiness of hypothesis sentences by understanding the semantic meaning of a table. While previous works have devoted significant efforts to the tabular reasoning task, they always assume there are sufficient labeled data. However, constructing reasoning samples over tables (and related text) is labor-intensive, especially when the reasoning process is complex. When labeled data is insufficient, the performance of models will suffer an unendurable decline. In this paper, we propose a unified framework for unsupervised complex tabular reasoning (UCTR), which generates sufficient and diverse synthetic data with complex logic for tabular reasoning tasks, assuming no human-annotated data at all. We first utilize a random sampling strategy to collect diverse programs of different types and execute them on tables based on a "Program-Executor" module. To bridge the gap between the programs and natural language sentences, we design a powerful "NL-Generator" module to generate natural language sentences with complex logic from these programs. Since a table often occurs with its surrounding texts, we further propose novel "Table-to-Text" and "Text-to-Table" operators to handle joint table-text reasoning scenarios. This way, we can adequately exploit the unlabeled table resources to obtain a well-performed reasoning model under an unsupervised setting. Our experiments cover different tasks (question answering and fact verification) and different domains (general and specific), showing that our unsupervised methods can achieve at most 93% performance compared to supervised models. We also find that it can substantially boost the supervised performance in low-resourced domains as a data augmentation technique. Our code is available at https://github.com/leezythu/UCTR.

Making sense of multiple modalities can yield a more comprehensive description of real-world phenomena. However, learning the co-representation of diverse modalities is still a long-standing endeavor in emerging machine learning applications and research. Previous generative approaches for multimodal input approximate a joint-modality posterior by uni-modality posteriors as product-of-experts (PoE) or mixture-of-experts (MoE). We argue that these approximations lead to a defective bound for the optimization process and loss of semantic connection among modalities. This paper presents a novel variational method on sets called the Set Multimodal VAE (SMVAE) for learning a multimodal latent space while handling the missing modality problem. By modeling the joint-modality posterior distribution directly, the proposed SMVAE learns to exchange information between multiple modalities and compensate for the drawbacks caused by factorization. In public datasets of various domains, the experimental results demonstrate that the proposed method is applicable to order-agnostic cross-modal generation while achieving outstanding performance compared to the state-of-the-art multimodal methods. The source code for our method is available online https://anonymous.4open.science/r/SMVAE-9B3C/.

The dual-encoder has become the de facto architecture for dense retrieval. Typically, it computes the latent representations of the query and document independently, thus failing to fully capture the interactions between the query and document. To alleviate this, recent work expects to get query-informed representations of documents. During training, it expands the document with a real query, while replacing the real query with a generated pseudo query at inference. This discrepancy between training and inference makes the dense retrieval model pay more attention to the query information but ignore the document when computing the document representation. As a result, it even performs worse than the vanilla dense retrieval model, since its performance depends heavily on the relevance between the generated queries and the real query. In this paper, we propose a curriculum sampling strategy, which also resorts to the pseudo query at training and gradually increases the relevance of the generated query to the real query. In this way, the retrieval model can learn to extend its attention from the document only to both the document and query, hence getting high-quality query-informed document representations. Experimental results on several passage retrieval datasets show that our approach outperforms the previous dense retrieval methods1.

In this work, we study the black-box targeted attack problem from the model discrepancy perspective. On the theoretical side, we present a generalization error bound for black-box targeted attacks, which gives a rigorous theoretical analysis for guaranteeing the success of the attack. We reveal that the attack error on a target model mainly depends on empirical attack error on the substitute model and the maximum model discrepancy among substitute models. On the algorithmic side, we derive a new algorithm for black-box targeted attacks based on our theoretical analysis, in which we additionally minimize the maximum model discrepancy(M3D) of the substitute models when training the generator to generate adversarial examples. In this way, our model is capable of crafting highly transferable adversarial examples that are robust to the model variation, thus improving the success rate for attacking the black-box model. We conduct extensive experiments on the ImageNet dataset with different classification models, and our proposed approach outperforms existing state-of-the-art methods by a significant margin. Our codes will be released.