Search Results for author:
Found 23 papers, 7 papers with code
TILP: Differentiable Learning of Temporal Logical Rules on Knowledge Graphs
Compared with static knowledge graphs, temporal knowledge graphs (tKG), which can capture the evolution and change of information over time, are more realistic and general.
TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning
We first convert TKGs into a temporal event knowledge graph (TEKG) which has a more explicit representation of time in term of nodes of the graph.
A Cognitively-Inspired Neural Architecture for Visual Abstract Reasoning Using Contrastive Perceptual and Conceptual Processing
We introduce a new neural architecture for solving visual abstract reasoning tasks inspired by human cognition, specifically by observations that human abstract reasoning often interleaves perceptual and conceptual processing as part of a flexible, iterative, and dynamic cognitive process.
FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design
By providing a neural network function approximation of this common kernel using graph attention networks, we develop a unified learning-based framework, FERN, for scalable Failure Evaluation and Robust Network design.
A Computational Account Of Self-Supervised Visual Learning From Egocentric Object Play
Research in child development has shown that embodied experience handling physical objects contributes to many cognitive abilities, including visual learning.
Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation
Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal logic literature.
A Neurodiversity-Inspired Solver for the Abstraction \& Reasoning Corpus (ARC) Using Visual Imagery and Program Synthesis
Core knowledge about physical objects -- e. g., their permanency, spatial transformations, and interactions -- is one of the most fundamental building blocks of biological intelligence across humans and non-human animals.
Computational Models of Solving Raven's Progressive Matrices: A Comprehensive Introduction
As being widely used to measure human intelligence, Raven's Progressive Matrices (RPM) tests also pose a great challenge for AI systems.
Visual-Imagery-Based Analogical Construction in Geometric Matrix Reasoning Task
Raven's Progressive Matrices is a family of classical intelligence tests that have been widely used in both research and clinical settings.
Temporal Inductive Logic Reasoning
Inductive logic reasoning is one of the fundamental tasks on graphs, which seeks to generalize patterns from the data.
Neural MoCon: Neural Motion Control for Physically Plausible Human Motion Capture
Our key-idea is to use real physical supervisions to train a target pose distribution prior for sampling-based motion control to capture physically plausible human motion.
Automatic Item Generation of Figural Analogy Problems: A Review and Outlook
Figural analogy problems have long been a widely used format in human intelligence tests.
Learning Time Series from Scale Information
Based on that, we focus on a specific but important type of scale information, the resolution/sampling rate of time series data.
Multi-Phase Locking Value: A Generalized Method for Determining Instantaneous Multi-frequency Phase Coupling
Methods such as $n:m$ phase locking value and bi-phase locking value have previously been proposed to quantify phase coupling between two resonant frequencies (e. g. $f$, $2f/3$) and across three frequencies (e. g. $f_1$, $f_2$, $f_1+f_2$), respectively.
From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
The behaviors and skills of models in many geosciences (e. g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration.
Efficient Probabilistic Logic Reasoning with Graph Neural Networks
In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN.
Learn to Explain Efficiently via Neural Logic Inductive Learning
The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems.
Can Graph Neural Networks Help Logic Reasoning?
Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training data, while the latter provides a principled framework for dealing with noisy data.
Learning a Meta-Solver for Syntax-Guided Program Synthesis
Our framework consists of three components: 1) an encoder, which embeds both the logical specification and grammar at the same time using a graph neural network; 2) a grammar adaptive policy network which enables learning a transferable policy; and 3) a reinforcement learning algorithm that jointly trains the specification and grammar embedding and adaptive policy.
One Model for the Learning of Language
Here, we describe a learning system that is maximally unconstrained, operating over the space of all computations, and is able to acquire several of the key structures present natural language from positive evidence alone.
CMU LiveMedQA at TREC 2017 LiveQA: A Consumer Health Question Answering System
In this paper, we present LiveMedQA, a question answering system that is optimized for consumer health question.
Predicting Discharge Medications at Admission Time Based on Deep Learning
Predicting discharge medications right after a patient being admitted is an important clinical decision, which provides physicians with guidance on what type of medication regimen to plan for and what possible changes on initial medication may occur during an inpatient stay.
Positive Definite Estimation of Large Covariance Matrix Using Generalized Nonconvex Penalties
Further, the statistical properties of the new estimators have been analyzed for generalized nonconvex penalties.
