通常用于从时间序列数据学习模型的在线高斯流程（GPS）比离线GPS更灵活，更健壮。 GPS的本地和稀疏近似都可以在线有效地学习复杂的模型。但是，这些方法假定所有信号都是相对准确的，并且所有数据都可以学习而无需误导数据。此外，在实践中，GP的在线学习能力受到高维问题和长期任务的限制。本文提出了一个稀疏的在线GP（SOGP），其遗忘机制以特定速度忘记了遥远的模型信息。所提出的方法结合了SOGP基础向量集的两个常规数据删除方案：基于位置信息的方案和最古老的基于点的方案。我们采用我们的方法来学习在任务切换的两部分轨迹跟踪问题下具有7度自由度的协作机器人的逆动力学。模拟和实验都表明，与两种常规数据删除方案相比，所提出的方法可实现更好的跟踪准确性和预测平滑度。

回声状态网络（ESN）是一种经常性神经网络，由固定的储层组成，其中神经元随机连接和递归连接，仅通过训练输出连接权重才能获得所需的输出。一阶减少和控制误差（力）学习是一种在线监督培训方法，可以将ESN的混乱活动变成指定的活动模式。本文提出了一种基于递归最小二乘的复合力学习方法，以训练初始活动自发性混乱的ESN，其中采用动态回归器扩展和内存数据开发的复合学习技术来增强参数收敛。提出的方法应用于基准问题，以预测Mackey-Glass系统产生的混沌时间序列，而数值结果表明，与现有方法相比，它显着改善了学习和预测性能。

自适应控制可以应用于具有参数不确定性的机器人系统，但是提高其性能通常很困难，尤其是在不连续的摩擦下。受到人类运动学习控制机制的启发，针对具有不连续摩擦的广泛机器人系统提出了一种自适应学习控制方法，其中采用了利用数据记忆来增强参数估计的复合误差学习技术。与经典的反馈误差学习控制相比，所提出的方法可以实现出色的瞬态和稳态跟踪，而无需高增益反馈和持续的激发，而持续的激发则以额外的计算负担和记忆使用费用。基于Denso工业机器人的实验验证了所提出方法的性能改善。

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

In this paper, we study the problem of knowledge-intensive text-to-SQL, in which domain knowledge is necessary to parse expert questions into SQL queries over domain-specific tables. We formalize this scenario by building a new Chinese benchmark KnowSQL consisting of domain-specific questions covering various domains. We then address this problem by presenting formulaic knowledge, rather than by annotating additional data examples. More concretely, we construct a formulaic knowledge bank as a domain knowledge base and propose a framework (ReGrouP) to leverage this formulaic knowledge during parsing. Experiments using ReGrouP demonstrate a significant 28.2% improvement overall on KnowSQL.

Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

As an important variant of entity alignment (EA), multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) with multiple modalities like images. However, current MMEA algorithms all adopt KG-level modality fusion strategies but ignore modality differences among individual entities, hurting the robustness to potential noise involved in modalities (e.g., unidentifiable images and relations). In this paper we present MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, to dynamically predict the mutual correlation coefficients among modalities for instance-level feature fusion. A modal-aware hard entity replay strategy is also proposed for addressing vague entity details. Extensive experimental results show that our model not only achieves SOTA performance on multiple training scenarios including supervised, unsupervised, iterative, and low resource, but also has limited parameters, optimistic speed, and good interpretability. Our code will be available soon.

Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (<50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.

Pure transformers have shown great potential for vision tasks recently. However, their accuracy in small or medium datasets is not satisfactory. Although some existing methods introduce a CNN as a teacher to guide the training process by distillation, the gap between teacher and student networks would lead to sub-optimal performance. In this work, we propose a new One-shot Vision transformer search framework with Online distillation, namely OVO. OVO samples sub-nets for both teacher and student networks for better distillation results. Benefiting from the online distillation, thousands of subnets in the supernet are well-trained without extra finetuning or retraining. In experiments, OVO-Ti achieves 73.32% top-1 accuracy on ImageNet and 75.2% on CIFAR-100, respectively.