Large pretrained language models (LMs) like BERT have improved performance in many disparate natural language processing (NLP) tasks. However, fine tuning such models requires a large number of training examples for each target task. Simultaneously, many realistic NLP problems are "few shot", without a sufficiently large training set. In this work, we propose a novel conditional neural process-based approach for few-shot text classification that learns to transfer from other diverse tasks with rich annotation. Our key idea is to represent each task using gradient information from a base model and to train an adaptation network that modulates a text classifier conditioned on the task representation. While previous task-aware few-shot learners represent tasks by input encoding, our novel task representation is more powerful, as the gradient captures input-output relationships of a task. Experimental results show that our approach outperforms traditional fine-tuning, sequential transfer learning, and state-of-the-art meta learning approaches on a collection of diverse few-shot tasks. We further conducted analysis and ablations to justify our design choices.

Domain adaptation seeks to leverage the abundant label information in a source domain to improve classification performance in a target domain with limited labels. While the field has seen extensive methodological development, its theoretical foundations remain relatively underexplored. Most existing theoretical analyses focus on simplified settings where the source and target domains share the same input space and relate target-domain performance to measures of domain discrepancy. Although insightful, these analyses may not fully capture the behavior of modern approaches that align domains into a shared space via feature transformations. In this paper, we present a comprehensive theoretical study of domain adaptation algorithms based on domain alignment. We consider the joint learning of domain-aligning feature transformations and a shared classifier in a semi-supervised setting. We first derive generalization bounds in a broad setting, in terms of covering numbers of the relevant function classes. We then extend our analysis to characterize the sample complexity of domain-adaptive neural networks employing maximum mean discrepancy (MMD) or adversarial objectives. Our results rely on a rigorous analysis of the covering numbers of these architectures. We show that, for both MMD-based and adversarial models, the sample complexity admits an upper bound that scales quadratically with network depth and width. Furthermore, our analysis suggests that in semi-supervised settings, robustness to limited labeled target data can be achieved by scaling the target loss proportionally to the square root of the number of labeled target samples. Experimental evaluation in both shallow and deep settings lends support to our theoretical findings.

According to the study of brain connectomics [29] and the aforementioned statement above, the topological connection Spurred on by the advent of advanced non-invasive techniques of the human brain takes place on three separate levels with such as electroencephalogram (EEG), explorations of different scales, inextricably linked with the geometry of the brain networks have entered a new era [40]. The proposed multi-scale spatial data distribution as a remarkable organ, exhibits a high level of time-varying differences can thus be concluded as three categories under complexity attributed to the intricate nature of the structural different brain scales: connections among its constituent units [4]. To the best of the authors' knowledge, The deep domain adaptation (DDA) method combines the no previous work has integrated the multi-scale spatial data superiority of deep learning and transfer learning, becoming distribution problem with the deep domain adaptation network one of the most efficient tools to address the data distribution (DDAN), neither on the design of the CNN structure nor difference problem in cross-subject EEG classification tasks the establishment of the adaptation domain. More and more researchers utilize this powerful integrate the principles of multi-scale brain topological structures tool to solve cross-subject motor imagery (MI) classification in order to solve the multi-scale spatial data distribution problems [35], [37], [38], aiming to improve the model generalization difference problem [29], a novel multi-scale spatial domain and the classification performance by transferring adaptation network (MSSDAN) consists of both multi-scale knowledge from source domain subject. The existing three types of crosssubject A. Overview of MSSDAN MI classification (MTM: multi-source to multi-target, MTS: multi-source to single-target, and STS) DDA methods In this paper, we propose MSSDAN, a new domain adaptation focus more on the global [15], [39], [41], class [14], [20], and method for the brain-computer interface, which consists of temporal domain adaptations [2], [5].

Deformation detection is vital for enabling accurate assessment and prediction of structural changes in materials, ensuring timely and effective interventions to maintain safety and integrity. Automating deformation detection through computer vision is crucial for efficient monitoring, but it faces significant challenges in creating a comprehensive dataset of both deformed and non-deformed objects, which can be difficult to obtain in many scenarios. In this paper, we introduce a novel framework for generating controlled synthetic data that simulates deformed objects. This approach allows for the realistic modeling of object deformations under various conditions. Our framework integrates an intelligent adapter network that facilitates sim-to-real domain adaptation, enhancing classification results without requiring real data from deformed objects. We conduct experiments on domain adaptation and classification tasks and demonstrate that our framework improves sim-to-real classification results compared to simulation baseline.