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2,295 papers
Graph representation learning has achieved significant success in various domains, including social network analysis, recommendation systems, and knowledge graph reasoning. However, a critical limitation often exists when dealing with temporal data embedded within graphs – accurately capturing the evolving relationship...
The simulation of quantum systems on classical computers faces significant challenges due to the exponential scaling of computational resources required to accurately represent quantum states and their evolution. This work proposes a novel approach to quantum computing simulation based on graph representation and graph...
Graph representation learning has emerged as a powerful technique for analyzing complex relationships within graph-structured data. However, existing methods often struggle to effectively capture the temporal dynamics inherent in many real-world graphs, where relationships evolve over time. This research introduces a n...
This paper introduces a novel approach to graph theory based on neural rings – a dynamic neural network representation of graph structure. The core innovation lies in employing a neural ring that learns and adjusts its parameters to optimize the graph's inherent properties, leading to enhanced information propagation e...
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This paper investigates a novel dynamic approach to adaptive neural network weight adjustment, specifically focusing on the utilization of graph neural networks (GNNs) for dynamic node weighting. We introduce a method that intelligently adjusts node weights during training based on the network's internal state and data...
This paper presents a novel approach to symbolic manipulation learning using self-supervised learning techniques. We leverage Recurrent Neural Networks (RNNs) to learn manipulation rules directly from data, combined with Graph Representation Learning (GRL) to capture the structural complexities inherent in symbolic dom...
This paper presents a novel approach to visual event detection that leverages multi-scale information fusion. The core idea is to enhance detection accuracy and robustness by integrating visual information at different scales, such as low-level textures and high-level semantic cues. We propose a framework utilizing mul...
Predicting drug-target interactions (DTIs) is a crucial task in drug discovery, aiming to identify potential therapeutic candidates and minimize adverse effects. Traditional methods often rely on high-throughput screening or homology modeling, which are time-consuming and expensive. This paper proposes a novel approach...
This paper presents a novel approach to neural symbolic reasoning by integrating distributed neural networks with knowledge graphs. The core claim is that this hybrid architecture can achieve more efficient and reliable symbolic reasoning, ultimately boosting the generalization capabilities of neural reasoning systems....
This paper introduces Neural Symbolic Time Table Reasoning (NSTI), a novel framework for understanding complex time series data by integrating the strengths of neural networks and symbolic reasoning. The core claim is that combining neural networks with symbolic time table inference provides a more interpretable and ro...
Predicting drug-target interactions (DTIs) is a crucial task in drug discovery, aiming to identify potential therapeutic candidates and minimize adverse effects. Traditional methods often rely on high-throughput screening or homology modeling, which are time-consuming and expensive. This paper proposes a novel approach...
Predicting protein structure from its amino acid sequence remains a central challenge in computational biology. Traditional methods often rely on homology modeling and ab initio approaches, which can be computationally expensive and limited in their accuracy. This work explores the potential of graph embeddings to addr...
A cross-section of the MIT community convened discussions on topics ranging from data centers and super pollutants to agriculture and education.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
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