Neurosymbolic Programming for Science

Neurosymbolic Programming for Science

Neurosymbolic Programming (NP) techniques have the potential to accelerate scientific discovery across fields. These models combine neural and symbolic components to learn complex patterns and representations from data, using high-level concepts or known constraints. As a result, NP techniques can i...

Full description

Bibliographic Details
Main Authors:	Sun, Jennifer J, Tjandrasuwita, Megan, Sehgal, Atharva, Solar-Lezama, Armando, Chaudhuri, Swarat, Yue, Yisong, Costilla Reyes, Omar
Format:	Article
Language:	en_US
Published:	2022
Subjects:	programming languages deep learning science domain knowledge
Online Access:	https://hdl.handle.net/1721.1/145783

Similar Items

Smooth interpretation
by: Chaudhuri, Swarat, et al.
Published: (2020)

Smoothing a program soundly and robustly
by: Chaudhuri, Swarat, et al.
Published: (2012)

Euler: A System for Numerical Optimization of Programs
by: Chaudhuri, Swarat, et al.
Published: (2014)

Cross-domain retinopathy classification with optical coherence tomography images via a novel deep domain adaptation method
by: Luo, Yuemei, et al.
Published: (2023)

Bridging boolean and quantitative synthesis using smoothed proof search
by: Chaudhuri, Swarat, et al.
Published: (2014)

Contrastive adversarial domain adaptation for machine remaining useful life prediction
by: Mohamed Ragab, et al.
Published: (2022)

Cross-platform privacy-preserving CT image COVID-19 diagnosis based on source-free domain adaptation
by: Feng, Yuanyi, et al.
Published: (2023)

MCI-frcnn: a deep learning method for topological micro-domain boundary detection
by: Tian, Simon Zhongyuan, et al.
Published: (2023)

MobileDA: toward edge-domain adaptation
by: Yang, Jianfei, et al.
Published: (2022)

A domain knowledge-informed design space exploration methodology for mechanical layout design
by: Li, Kangjie, et al.
Published: (2024)

Automated Negotiation from Declarative Contract Descriptions
by: Grosof, Benjamin, et al.
Published: (2002)

Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?
by: Poggio, Tomaso, et al.
Published: (2016)

A Neurosymbolic Approach to Abstraction and Reasoning
by: Alford, Simon
Published: (2022)

Development of a deep learning system for vision based robot control
by: Ahmed, Rashna Analia
Published: (2024)

Deep learning based receiver for downlink NOMA system
by: Lim, Wei Kang
Published: (2024)

Understanding people assembling through hand action recognition
by: Xu, Zihua
Published: (2024)

Self-adaptive deep reinforcement learning for THz beamforming with silicon metasurfaces in 6G communications
by: Tan, Yi Ji, et al.
Published: (2022)

Neurosymbolic Learning for Robust and Reliable Intelligent Systems
by: Inala, Jeevana Priya
Published: (2022)

Vehicle detection and classification by voiceprint recognition based on single acoustic sensor under bridge expansion joint
by: Pan, Yue, et al.
Published: (2024)

Neurosymbolic AI for mining public opinions about wildfires
by: Duong, Cuc, et al.
Published: (2023)

Transformation Optics: A Time- and Frequency-Domain Analysis of Electron-Energy Loss Spectroscopy
by: Kraft, Matthias, et al.
Published: (2017)

Ensemble learning of deep learning-based receiver for 5G communication system implementation
by: Xu, Ziang
Published: (2024)

Dense-TNT: efficient vehicle type classification neural network using satellite imagery
by: Luo, Ruikang, et al.
Published: (2025)

Cross-Modal Deep Variational Hashing
by: Liong, Venice Erin, et al.
Published: (2017)

Fingerprinting deep neural networks - a DeepFool approach
by: Wang, Si, et al.
Published: (2021)

Exploring pre-trained diffusion models in a tuning-free manner
by: Wang, Jinghao
Published: (2025)

Inverse design of topological photonic time crystals via deep learning
by: Long, Yang, et al.
Published: (2024)

CRYOMOVE: cold chain real-time management of vaccine delivery using PCM and deep learning
by: Bhatt, Tapasvi, et al.
Published: (2024)

Knowledge management model practices in business organisation : a theoretical model / Mohd Sazili Shahibi ... [et al.]
by: Shahibi, Mohd Sazili, et al.
Published: (2005)

Prediction for magnetostriction magnetorheological foam using machine learning method
by: Rohim, Muhamad Amirul Sunni, et al.
Published: (2022)

Deep learning approach with image noise reduction to determine planting density and defected paddy seedlings
by: Mohamed Anuar, Mohamed Marzhar
Published: (2022)

Enhancing contextual understanding in NLP: adapting state-of-the-art models for improved sentiment analysis of informal language
by: Sneha Ravisankar
Published: (2024)

From ELIZA to ChatGPT: The Evolution of NLP and Financial Applications
by: Lo, Andrew W, et al.
Published: (2023)

Counterfactual Explanations and Predictive Models to Enhance Clinical Decision-Making in Schizophrenia using Digital Phenotyping
by: Canas, Juan Sebastian, et al.
Published: (2023)

Discovering Abstractions from Language via Neurosymbolic Program Synthesis
by: Grand, Gabriel J.
Published: (2023)

Domain dynamics response to polarization switching in relaxor ferroelectrics
by: Li, Yang, et al.
Published: (2025)

An adaptive deep learning neural network model to enhance machine-learning-based classifiers for intrusion detection in smart grids
by: Li, Xue Jun, et al.
Published: (2023)

Translation and rewriting: importance of domain knowledge and multimodality for science museum translation in Singapore
by: Ooi, Pei An
Published: (2024)

A general domain specific feature transfer framework for hybrid domain adaptation
by: Wei, Pengfei, et al.
Published: (2020)

Multitasking deep neural network models for Arabic dialect sentiment analysis
by: Alali, Muath Mohammad Oqlah
Published: (2022)