19th Conference on Neurosymbolic Learning and Reasoning (NeSy 2025)
This year, NeSy 2025 will be held in Santa Cruz, California, from September 8th to 10th. We accept full papers, short papers, and extended abstracts of recently published papers on any topic related to neurosymbolic AI. Please see the call for papers for more details.
The NeSy series is the longest standing gathering for the presentation and discussion of cutting-edge research in neurosymbolic AI. NeSy is the annual meeting of the Neurosymbolic Learning and Reasoning Association, which has organised NeSy since 2005, back then as a workshop.
About the NeSy Conference
NeSy studies the integration of deep learning and symbolic AI, by combining statistical machine learning based on neural networks with knowledge representation and reasoning from symbolic AI. Neural networks and statistical Machine Learning have achieved industrial relevance in a number of areas from healthcare to finance and business, obtaining state-of-the-art performance at language modelling, speech and image recognition, sensor data and graph analytics. Symbolic AI is challenged by such unstructured large data, but offers sound and well-understood formal reasoning and explanation via knowledge representation that can be inspected to interpret how decisions follow from data. Neural and symbolic AI approaches also contrast in what problems they excel at: deep learning excels at raw data processing, but fails at planning and rich deductive reasoning.
Neurosymbolic AI aims to build powerful computational AI models, systems and applications by integrating neural and symbolic learning and reasoning. It creates synergies among the strengths of neural and symbolic AI while overcoming their complementary weaknesses. The NeSy conference series is the premier venue for advancing the theory and practice of neurosymbolic computing. Since the first NeSy, as a workshop in 2005, NeSy has fostered an open and collaborative atmosphere, bringing together scientists and practitioners that straddle the line between deep learning and symbolic AI.