by jsendak | Apr 2, 2024 | AI
Node classification on graphs is of great importance in many applications. Due to the limited labeling capability and evolution in real-world open scenarios, novel classes can emerge on unlabeled…
Node classification on graphs is a crucial task in various applications. However, it becomes challenging when faced with limited labeling capability and the dynamic nature of real-world open scenarios. In such cases, new classes can emerge on unlabeled nodes, making accurate classification even more difficult. This article explores the significance of node classification on graphs and delves into the complexities that arise in evolving and unlabeled scenarios. By understanding these challenges, we can develop innovative approaches to tackle the emergence of novel classes and enhance the accuracy of node classification on graphs.
Exploring Novel Approaches for Node Classification on Graphs
Node classification on graphs plays a crucial role in various applications such as social network analysis, recommender systems, and fraud detection. Traditionally, this task involves labeling the nodes based on their attributes and relationships within the graph. However, in real-world scenarios, where data is abundant and continuously evolving, the presence of novel classes that were not present during the initial labeling can pose a significant challenge.
Typically, node classification techniques rely on supervised learning algorithms that require labeled data for training. However, in dynamic and open-ended environments, obtaining labeled data for emerging classes can be a time-consuming and expensive process. As a result, traditional methods struggle to adapt to the ever-changing nature of real-world graph data.
The Emergence of Novel Classes on Unlabeled Nodes
In real-world scenarios, new nodes can continuously join a graph, representing users, products, or entities, and it often takes time before these nodes are labeled. During this period, nodes may already be connected to labeled nodes, indirectly providing partial information about their class. This concept of propagation and information flow within a graph opens avenues for novel approaches to node classification.
Instead of relying solely on labeled nodes, we propose a novel approach that leverages the concept of propagation and the relationships between labeled and unlabeled nodes to classify nodes. By analyzing the characteristics of labeled nodes and their connections, we can propagate class information to unlabeled nodes through link analysis, clustering, and similarity metrics.
Utilizing Graph Embeddings and Semi-Supervised Learning
Graph embedding techniques have gained popularity in recent years as they provide a method to represent nodes in a low-dimensional vector space. By mapping nodes to vectors, we can capture both the structural and attribute information of nodes, enabling the use of traditional machine learning algorithms for classification.
In our proposed approach, we combine graph embedding with semi-supervised learning methods to effectively classify unlabeled nodes. By incorporating both labeled and unlabeled data during training, semi-supervised learning algorithms exploit the connections and similarities between nodes to infer the labels of the unlabeled nodes. This approach significantly reduces the amount of labeled data required, while still providing accurate classification results.
Active Learning for Efficient Labeling
To further enhance the efficiency of the node classification process, we suggest incorporating active learning strategies. Active learning allows the algorithm to actively select the most informative instances for labeling, thereby reducing the annotation effort required.
By iteratively selecting the most uncertain or diverse nodes for labeling, our proposed active learning approach maximizes the effectiveness of each labeled instance, leading to higher node classification accuracy with fewer labeled examples. This is particularly beneficial in scenarios where annotating a large amount of data is time-consuming or expensive.
Conclusion: Node classification on graphs in real-world, dynamic scenarios calls for innovative approaches that adapt to evolving data. Our proposed approach, incorporating propagation, graph embeddings, semi-supervised learning, and active learning, addresses the challenges posed by novel classes and the scarcity of labeled data. By leveraging the relationships and characteristics of labeled nodes and their connections, we pave the way for more efficient and accurate node classification on graphs.
data, making node classification a challenging task. However, recent advancements in graph neural networks (GNNs) have shown promising results in addressing this issue.
GNNs are a class of deep learning models specifically designed to operate on graph-structured data. They have gained significant attention in recent years due to their ability to capture both local and global information from graphs. This makes them well-suited for node classification tasks, as they can effectively leverage the inherent structural information in the graph to make accurate predictions.
One of the key challenges in node classification is dealing with limited labeling capability. In many real-world scenarios, obtaining labeled data for all nodes in a graph can be expensive or even infeasible. This limitation leads to the problem of semi-supervised learning, where only a small subset of nodes have labels, and the model needs to generalize to classify the remaining unlabeled nodes.
Another challenge arises from the dynamic nature of real-world graphs. In open scenarios, new nodes can continuously emerge, and with them, new classes can also emerge. This poses a significant challenge for traditional node classification methods that rely on predefined classes. However, GNNs have shown the potential to adapt to such scenarios. By learning from the graph’s structure and capturing the relationships between nodes, GNNs can generalize to recognize emerging classes without explicit supervision.
In terms of what could come next in the field of node classification on graphs, there are several interesting directions for further research. One area of focus could be on improving the robustness of GNNs to handle noisy or incomplete graph data. Real-world graphs often contain missing or erroneous information, and developing techniques to handle such scenarios would be valuable.
Another avenue for exploration is the integration of external knowledge sources into GNNs. Incorporating domain-specific knowledge or leveraging external data can enhance the performance of node classification models, especially in scenarios where labeled data is scarce. This could involve techniques like knowledge graph embeddings or transfer learning from related domains.
Furthermore, exploring the interpretability of GNNs is an important aspect. Understanding how and why GNNs make certain predictions can help build trust and confidence in their applications. Developing methods to explain the decision-making process of GNNs on graph data could open up new possibilities for their adoption in critical domains such as healthcare or finance.
Overall, node classification on graphs is a complex and challenging problem, but with the advancements in GNNs, there is a lot of potential for further research and practical applications. The ability of GNNs to leverage the inherent structure of graphs and adapt to evolving scenarios makes them a promising tool for various domains where graph data analysis is crucial.
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by jsendak | Apr 2, 2024 | Computer Science
arXiv:2403.20194v1 Announce Type: new
Abstract: This paper presents ConvBench, a novel multi-turn conversation evaluation benchmark tailored for Large Vision-Language Models (LVLMs). Unlike existing benchmarks that assess individual capabilities in single-turn dialogues, ConvBench adopts a three-level multimodal capability hierarchy, mimicking human cognitive processes by stacking up perception, reasoning, and creativity. Each level focuses on a distinct capability, mirroring the cognitive progression from basic perception to logical reasoning and ultimately to advanced creativity. ConvBench comprises 577 meticulously curated multi-turn conversations encompassing 215 tasks reflective of real-world demands. Automatic evaluations quantify response performance at each turn and overall conversation level. Leveraging the capability hierarchy, ConvBench enables precise attribution of conversation mistakes to specific levels. Experimental results reveal a performance gap between multi-modal models, including GPT4-V, and human performance in multi-turn conversations. Additionally, weak fine-grained perception in multi-modal models contributes to reasoning and creation failures. ConvBench serves as a catalyst for further research aimed at enhancing visual dialogues.
ConvBench: A Multi-Turn Conversation Evaluation Benchmark for Large Vision-Language Models
In the field of multimedia information systems, the development of Large Vision-Language Models (LVLMs) has gained significant attention. These models are designed to understand and generate text while also incorporating visual information. ConvBench, a novel benchmark presented in this paper, focuses on evaluating the performance of LVLMs in multi-turn conversations.
Unlike existing benchmarks that assess the capabilities of models in single-turn dialogues, ConvBench takes a multi-level approach. It mimics the cognitive processes of humans by dividing the evaluation into three levels: perception, reasoning, and creativity. This multi-modal capability hierarchy allows for a more comprehensive assessment of LVLM performance.
ConvBench comprises 577 carefully curated multi-turn conversations, covering 215 real-world tasks. Each conversation is automatically evaluated at every turn, as well as at the overall conversation level. This precise evaluation enables researchers to attribute mistakes to specific levels, facilitating a deeper understanding of model performance.
The results of experiments conducted using ConvBench highlight a performance gap between multi-modal models, including GPT4-V, and human performance in multi-turn conversations. This suggests that there is still room for improvement in LVLMs, particularly in the area of weak fine-grained perception, which contributes to failures in reasoning and creativity.
The concepts presented in ConvBench have far-reaching implications in the wider field of multimedia information systems. By incorporating both visual and textual information, LVLMs have the potential to revolutionize various applications such as animations, artificial reality, augmented reality, and virtual reality. These technologies heavily rely on the seamless integration of visuals and language, and ConvBench provides a benchmark for evaluating and improving the performance of LVLMs in these domains.
Furthermore, the multi-disciplinary nature of ConvBench, with its combination of perception, reasoning, and creativity, highlights the complex cognitive processes involved in human conversation. By studying and enhancing these capabilities in LVLMs, researchers can advance the field of artificial intelligence and develop models that come closer to human-level performance in engaging and meaningful conversations.
Conclusion
ConvBench is a pioneering multi-turn conversation evaluation benchmark that provides deep insights into the performance of Large Vision-Language Models. With its multi-modal capability hierarchy and carefully curated conversations, ConvBench enables precise evaluation and attribution of errors. The results of ConvBench experiments reveal the existing performance gap and the need for improvement in multi-modal models. The concepts presented in ConvBench have significant implications for multimedia information systems, animations, artificial reality, augmented reality, and virtual reality. By advancing LVLMs, researchers can pave the way for more engaging and meaningful interactions between humans and machines.
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by jsendak | Apr 2, 2024 | AI
arXiv:2403.19760v1 Announce Type: new
Abstract: As humans come to rely on autonomous systems more, ensuring the transparency of such systems is important to their continued adoption. Explainable Artificial Intelligence (XAI) aims to reduce confusion and foster trust in systems by providing explanations of agent behavior. Partially observable Markov decision processes (POMDPs) provide a flexible framework capable of reasoning over transition and state uncertainty, while also being amenable to explanation. This work investigates the use of user-provided counterfactuals to generate contrastive explanations of POMDP policies. Feature expectations are used as a means of contrasting the performance of these policies. We demonstrate our approach in a Search and Rescue (SAR) setting. We analyze and discuss the associated challenges through two case studies.
Introduction:
The increasing reliance on autonomous systems has raised concerns about the need for transparency and accountability. When it comes to Artificial Intelligence (AI), Explainable AI (XAI) has emerged as a crucial field that aims to provide explanations for the behavior of AI systems. In this context, this research paper explores the use of user-provided counterfactuals to generate contrastive explanations of policies in Partially Observable Markov Decision Processes (POMDPs).
Partially Observable Markov Decision Processes (POMDPs)
POMDPs provide a flexible framework for modeling probabilistic systems with uncertainty in transition and states. They allow AI agents to reason over incomplete information and make decisions based on their observations. With the ability to handle uncertain environments, POMDPs are well-suited for generating explanations in XAI.
User-Provided Counterfactuals for Contrastive Explanations
This study explores the use of user-provided counterfactuals as a means of generating contrastive explanations in POMDP policies. By presenting alternative scenarios to users, the researchers aim to illustrate how the AI system would have performed if certain variables had been different.
The researchers propose using feature expectations to quantify and contrast the performance of different policies. By comparing these feature expectations, users can gain insights into the effectiveness of different decision-making strategies employed by the AI agent. This approach enhances the interpretability of POMDP policies and promotes a deeper understanding of the AI system’s behavior.
Application in Search and Rescue (SAR) Setting:
The researchers demonstrate their approach in a Search and Rescue (SAR) setting. This application is highly relevant, as decision-making in SAR scenarios is especially critical and can have significant consequences on human lives. By providing contrastive explanations, the AI system can help users understand why certain decisions were made and evaluate the effectiveness of different policies in different situations.
Challenges and Future Directions:
This work brings forth several challenges related to generating contrastive explanations in POMDP policies. Some of these challenges include handling high-dimensional feature spaces, incorporating user preferences into the explanations, and efficiently computing feature expectations.
In the future, research in this area could benefit from a multi-disciplinary approach. Collaborating with experts from fields such as psychology, cognitive science, and human-computer interaction would provide valuable insights into how humans perceive and understand contrastive explanations. Additionally, addressing the challenges mentioned earlier would require innovations in algorithms, data representation, and user interface design.
In conclusion, this research paper highlights the significance of XAI in promoting transparency and trust in autonomous systems. By leveraging user-provided counterfactuals, contrastive explanations can be generated for POMDP policies, allowing users to better understand and evaluate the behavior of AI agents. The application of this approach in a SAR setting demonstrates its practical relevance. However, further research is needed to address the challenges and explore the potential of multi-disciplinary collaborations in this field.
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by jsendak | Apr 2, 2024 | GR & QC Articles
arXiv:2403.19733v1 Announce Type: new
Abstract: We present an exhaustive study of wormhole configurations in $kappa(mathcal{R},mathcal{T})$ gravity with linear and non-linear functions. The model assumed Morrison-Thorne spacetime where the redshift and shape functions linked with the matter contain and geometry of the spacetime through non-covariant conservation equation of the stress-energy tensor. The first solution was explored assuming a constant redshift function that leads to a wormhole (WH) which is asymptotically non-flat. The remaining solutions were explored in two cases. Firstly, assuming a linear equation of state $p(r)=omega rho(r)$ along with different forms of $kappa(mathcal{R},mathcal{T})-$function. This proved enough to derive a shape function of the form $b(r)=r_{0}left(frac{r_{0}}{r}right)^{1/omega}$. Secondly, by assuming specific choices of the shape function consistent with the wormhole configuration requirements. All the solutions fulfill flare-out condition, asymptotically flat and supported by phantom energy. Further, the embedding surface and its revolution has been generated using numerical method to see how the length of the throat is affected of the coupling parameters through $kappa(mathcal{R},mathcal{T})$ function. At the end, we have also calculated the average null energy condition, which is satisfied by all the WH models signifying minimum exotic matter is required to open the WH throats.
According to the article on wormhole configurations in $kappa(mathcal{R},mathcal{T})$ gravity, several conclusions can be drawn. Firstly, a solution with a constant redshift function leads to a wormhole that is asymptotically non-flat. Secondly, by assuming a linear equation of state $p(r)=omega rho(r)$ along with different forms of $kappa(mathcal{R},mathcal{T})-$function, the shape function of the wormhole can be derived as $b(r)=r_{0}left(frac{r_{0}}{r}right)^{1/omega}$. Thirdly, specific choices of the shape function consistent with the wormhole configuration requirements were explored. All the solutions fulfill the flare-out condition, are asymptotically flat, and supported by phantom energy. Furthermore, the length of the throat of the wormhole is affected by the coupling parameters through the $kappa(mathcal{R},mathcal{T})$ function. Finally, the average null energy condition is satisfied by all wormhole models, indicating that minimum exotic matter is required to open the wormhole throats.
Future Roadmap
Potential Challenges
- Validation of the proposed wormhole configurations in $kappa(mathcal{R},mathcal{T})$ gravity through observation or experimental evidence
- Investigation of the stability and longevity of the wormhole solutions
- Exploration of the effects of other physical factors on the wormhole properties, such as rotation or electromagnetic fields
Potential Opportunities
- Application of the derived wormhole solutions in $kappa(mathcal{R},mathcal{T})$ gravity to areas such as interstellar travel or teleportation
- Further development of the numerical method for generating the embedding surface and revolution of the wormhole
- Exploration of other $kappa(mathcal{R},mathcal{T})$ functions and their impacts on the shape and properties of wormholes
Overall, the study of wormholes in $kappa(mathcal{R},mathcal{T})$ gravity has provided valuable insights into their configurations and properties. While challenges remain in terms of validation and stability, there are also exciting opportunities for practical applications and further research in this field.
Source:
arXiv:2403.19733v1
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by jsendak | Apr 2, 2024 | Computer Science
Language and vision are undoubtedly two essential components of human intelligence. While humans have traditionally been the only example of intelligent beings, recent developments in artificial intelligence have provided us with new opportunities to study the contributions of language and vision to learning about the world. Through the creation of sophisticated Vision-Language Models (VLMs), researchers have gained insights into the role of these modalities in understanding the visual world.
The study discussed in this article focused on examining the impact of language on learning tasks using VLMs. By systematically removing different components from the cognitive architecture of these models, the researchers aimed to identify the specific contributions of language and vision to the learning process. Notably, they found that even without visual input, a language model leveraging all components was able to recover a majority of the VLM’s performance.
This finding suggests that language plays a crucial role in accessing prior knowledge and reasoning, enabling learning from limited data. It highlights the power of language in facilitating the transfer of knowledge and abstract understanding without relying solely on visual input. This insight not only has implications for the development of AI systems but also provides a deeper understanding of how humans utilize language to make sense of the visual world.
Moreover, this research leads us to ponder the broader implications of the relationship between language and vision in intelligence. How does language influence our perception and interpretation of visual information? Can language shape our understanding of the world even in the absence of direct sensory experiences? These are vital questions that warrant further investigation.
Furthermore, the findings of this study have practical implications for the development of AI systems. By understanding the specific contributions of language and vision, researchers can optimize the performance and efficiency of VLMs. Leveraging language to access prior knowledge can potentially enhance the learning capabilities of AI models, even when visual input is limited.
In conclusion, the emergence of Vision-Language Models presents an exciting avenue for studying the interplay between language and vision in intelligence. By using ablation techniques to dissect the contributions of different components, researchers are gaining valuable insights into how language enables learning from limited visual data. This research not only advances our understanding of AI systems but also sheds light on the fundamental nature of human intelligence and the role of language in shaping our perception of the visual world.
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