Graph Neural Networks (GNNs) have demonstrated remarkable ability in semi-supervised node classification. However, most existing GNNs rely heavily on a large amount of labeled data for training, which is labor-intensive and requires extensive domain knowledge. In this paper, we first analyze the restrictions of GNNs generalization from the perspective of supervision signals in the context of few-shot semi-supervised node classification. To address these challenges, we propose a novel algorithm named NormProp, which utilizes the homophily assumption of unlabeled nodes to generate additional supervision signals, thereby enhancing the generalization against label scarcity. The key idea is to efficiently capture both the class information and the consistency of aggregation during message passing, via decoupling the direction and Euclidean norm of node representations. Moreover, we conduct a theoretical analysis to determine the upper bound of Euclidean norm, and then propose homophilous regularization to constraint the consistency of unlabeled nodes. Extensive experiments demonstrate that NormProp achieve state-of-the-art performance under low-label rate scenarios with low computational complexity.

Recent advancements in Large Language Models (LLMs) have catalyzed the exploration of Chain of Thought (CoT) approaches, particularly in extending their application to multimodal tasks to enhance reasoning capabilities. However, current studies often fail to fully leverage the inferential capabilities of these models, as they primarily focus on selecting similar questions or images rather than exploring identical inferences, thereby limiting the potential for analogical learning. In this paper, we propose an inference retrieval-augmented method that incorporates two strategies to identify training set examples with inferential processes similar to those of the target problem. Initially, we fine-tune a vanilla model to generate the pseudo-inference relevant to the question, which is then utilized to retrieve analogous examples. Next, we hypothesize that questions sharing similar contexts may share inferences, thus retrieving examples based on similarity in contextual questions. Ultimately, we construct multiple reasoning pathways from the retrieved examples and employ a voting mechanism to determine the most frequent answer. Our method surpasses all few-shot approaches and most supervised methods on the ScienceQA dataset, achieving an accuracy of 87.37% with ChatGPT and exceeding the human benchmark in several categories.

Experience Replay (ER) allows Deep Reinforcement Learning (RL) agent to reuse past experience, as though recall the same Experience repeatedly. ER enables RL algorithm to be trained by reusing previous states, so that RL agent can obtain more accurate value estimations and action selections. Current policy algorithms either have a rule-based replay policy or uniformly replay past experiences, which may be suboptimal. The agent is updated based on the replay data to maximize the cumulative reward, and the replay policy is updated to provide the more valuable experience for the agent. In this work, we propose a novel experience replay algorithm Regret Minimization Experience Replay (RMER), to improve the immediate reward via sampling and ensure certain exploration capability of agent. Finally, we prove the ascendency of RMER with different off- policy algorithms on the suite of Open AI gym continuous control tasks.

In the realm of visual recognition, data augmentation stands out as a pivotal technique to amplify model robustness. Yet, a considerable number of existing methodologies lean heavily on heuristic foundations, rendering their intrinsic mechanisms ambiguous. This manuscript takes both a theoretical and empirical approach to understanding the phenomenon. Theoretically, we frame the discourse around data augmentation within game theory’s constructs. Venturing deeper, our empirical evaluations dissect the intricate mechanisms of emblematic data augmentation strategies, illuminating that these techniques primarily stimulate mid- and high-order game interactions. Beyond the foundational exploration, our experiments span multiple datasets and diverse augmentation techniques, underscoring the universal applicability of our findings. Recognizing the vast array of robustness metrics with intricate correlations, we unveil a streamlined proxy. This proxy not only simplifies robustness assessment but also offers invaluable insights, shedding light on the inherent dynamics of model game interactions and their relation to overarching system robustness. These insights provide a novel lens through which we can re-evaluate model safety and robustness in visual recognition tasks.

Visual dialog involves answering questions by analyzing both images and dialogue history. While current multimodal research has effectively modeled the interactions among images, dialogue history, and questions, it incurs significant computational overhead and complexity. To address these challenges, this paper introduces a MultiModal Star Transformer (MMST) that effectively models the interactions between visual and textual modalities, as well as within each modality, with linear computational overhead. MMST utilizes a relay token for each modality, allowing each satellite token to interact with its two adjacent tokens, its previous state, and the two relay tokens. The introduction of relay tokens ensures that every two non-adjacent satellite tokens are two-hop neighbors, thus enabling MMST to support both intramodal long-range connections and intermodal interactions efficiently. Experimental results on the Visdial v0.9 and v1.0 datasets demonstrate that MMST performs comparably to full-attention models.

Objective. The data processing of medical test report has always been one of the important contents in biological information domain, especially the process of extracting the effective information from the report so as to assist doctors with the correct medical plan. Usual methods neglect the implicit relationship between features. More features are generally not a better choice because more noise is generated between feature combinations. We propose a practical feature selection strategy RMFS, which aims to select the optimal combination of features. Materials and Methods. Based on the above situation, in this paper, 64 features are extracted from a real medical test report dataset for stroke and feature selection is defined as a reinforcement learning problem to optimize the feature combination by minimizing regret. We select three current mainstream feature selection methods and conduct comparative experiments. Results. We processed and completed a dataset derived from real medical test reports of stroke. We redefine the feature selection problem as a reinforcement learning problem and propose an optimization strategy based on regret minimization and train weight parameters in a DQN network. Experimental results demonstrate that our method can identify feature combinations with higher prediction accuracy. Discussion. RMFS shows a strong robustness to the randomness of the environment and has high computational efficiency and accuracy. Compared with the previous feature selection methods, our method yields superior results. Conclusion. The experimental results demonstrate that our method can obtain a more accurate prediction rate under the same feature scale and we can achieve baseline performance with fewer features.