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Bridging Neural and Symbolic Representations with Transitional Dictionary Learning
This paper introduces a novel Transitional Dictionary Learning (TDL) framework that can implicitly learn symbolic knowledge, such as …
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SocioDojo: Building Lifelong Analytical Agents with Real-world Text and Time Series
We introduce SocioDojo, an open-ended lifelong learning environment for developing ready-to-deploy autonomous agents capable of …
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Detecting Continuous Gravitational Waves Using Generated Training Data
Detecting continuous gravitational waves using machine learning approaches is an active research topic. With signal strengths between …
Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexes
Graph Neural Networks (GNNs), despite achieving remarkable performance across different tasks, are theoretically bounded by the …
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ANEDA: Adaptable Node Embeddings for Shortest Path Distance Approximation
Abstract—Shortest path distance approximation is a crucial aspect of many graph algorithms, in particular the heuristic- based routing …
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Deep Distance Sensitivity Oracles
One of the most fundamental graph problems is finding a shortest path from a source to a target node. While in its basic forms the …
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Learned Approximate Distance Labels for Graphs
Distance computation is a fundamental problem in algorithmic graph theory with broad applications across various fields. Distance …
Improved And Optimized Drug Repurposing For The SARS-CoV-2 Pandemic
Abstract The active global SARS-CoV-2 pandemic caused more than 426 million cases and 5.8 million deaths worldwide. The development of …
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Semi-supervised adversarial text generation based on seq2seq models
To improve deep learning models’ robustness, adversarial training has been frequently used in computer vision with satisfying results. …
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A Study on Self-Supervised Object Detection Pretraining
In this work, we study different approaches to self-supervised pretraining of object detection models. We first design a general …
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