127 papers indexed here
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This paper introduces Dynamic Topology Dependency Neural Networks (TDNN), a novel neural network architecture designed to address the limitations of traditional static neural networks when processing dynamic and complex data streams. The core concept of TDNN revolves around a dynamically adaptable neural network topolo...
This paper explores the application of topology-based generative algorithms to neural networks, aiming to design novel and complex architectures through a self-organizing learning paradigm. We propose a graph-based approach where the network's structure is explicitly defined by the topology of the input data, offering...
This paper explores the application of Graph Neural Networks (GNNs) to the problem of program abstraction and optimization. Traditional approaches to program abstraction often rely on manually crafted rules or limited pattern matching, which can be brittle and difficult to scale. This work proposes a novel framework th...
This paper proposes a novel approach to compiler optimization by leveraging the power of Graph Neural Networks (GNNs). Traditional compiler optimization techniques rely heavily on hand-crafted heuristics and static analysis, often struggling to capture complex code relationships and achieve optimal performance. We intr...
Graph Neural Networks (GNNs) have demonstrated remarkable success in various domains, including social network analysis, drug discovery, and recommendation systems. However, their inherent complexity often leads to a "black box" problem, where it is difficult to understand the reasoning behind their predictions. This p...
This paper introduces a novel approach to graph embedding that leverages the temporal dynamics of graph relationships. Traditional graph embedding techniques often treat graphs as static structures, failing to capture the evolving nature of connections between nodes. Our method, Temporal Graph Embedding via Relational...
This paper proposes a novel approach to program error diagnosis and repair leveraging Graph Neural Networks (GNNs). Traditional methods for software defect detection and correction often rely on manual inspection or rule-based systems, which can be time-consuming, expensive, and prone to human error. Our approach trans...
Biological regulatory networks (BRNs) govern cellular processes with inherent stochasticity, reflecting the probabilistic nature of gene expression and protein interactions. Traditional Graph Neural Networks (GNNs) often treat these networks as deterministic, leading to inaccurate representations and predictions. This...
This paper presents a novel approach to dependency discovery within software systems, leveraging topological information derived from program code. The core idea is to automatically uncover complex dependencies between programs by analyzing control flow graphs, data dependencies, and abstract syntax trees (ASTs). A gra...
This paper explores the development of an emergent semantic network system designed to accelerate and enhance mathematical proof verification. Traditional methods rely heavily on manual verification, which is inherently slow and susceptible to human error. We propose a novel approach leveraging graph neural networks to...
This paper introduces a novel approach to graph embedding that explicitly models the temporal dynamics of relationships within a graph. Current graph embedding techniques often treat graphs as static structures, neglecting the crucial aspect of how connections evolve over time. This work addresses this limitation by le...
The optimization of computational graphs is a critical step in deploying machine learning models, particularly deep neural networks. Traditional manual optimization methods are labor-intensive, require deep domain expertise, and are prone to human error. This paper proposes a novel approach leveraging program synthesis...
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