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Found 11 papers, 6 papers with code
A Survey on Deep Learning for Theorem Proving
Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning in mathematical language to rigorous derivations in formal systems.
Learning with Logical Constraints but without Shortcut Satisfaction
In this paper, we present a new framework for learning with logical constraints.
Softened Symbol Grounding for Neuro-symbolic Systems
Neuro-symbolic learning generally consists of two separated worlds, i. e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in AI.
Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey
Transformer-based Large Language Models (LLMs) have been applied in diverse areas such as knowledge bases, human interfaces, and dynamic agents, and marking a stride towards achieving Artificial General Intelligence (AGI).
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments
Offline Reinforcement Learning (RL) has emerged as a promising framework for learning policies without active interactions, making it especially appealing for autonomous driving tasks.
Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills
Learning-based vehicle planning is receiving increasing attention with the emergence of diverse driving simulators and large-scale driving datasets.
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification
In this paper, we introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes, as an important building block for a pioneering Transformer-style network for node classification on large graphs, dubbed as \textsc{NodeFormer}.
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs
Solving combinatorial optimization (CO) on graphs is among the fundamental tasks for upper-stream applications in data mining, machine learning and operations research.
Joint Distribution across Representation Space for Out-of-Distribution Detection
Specifically, We construct a generative model, called Latent Sequential Gaussian Mixture (LSGM), to depict how the in-distribution latent features are generated in terms of the trace of DNN inference across representation spaces.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Operational Calibration: Debugging Confidence Errors for DNNs in the Field
Trained DNN models are increasingly adopted as integral parts of software systems, but they often perform deficiently in the field.
Boosting Operational DNN Testing Efficiency through Conditioning
With the increasing adoption of Deep Neural Network (DNN) models as integral parts of software systems, efficient operational testing of DNNs is much in demand to ensure these models' actual performance in field conditions.
