Search Results for author:
Found 7 papers, 4 papers with code
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.
Securing Reliability: A Brief Overview on Enhancing In-Context Learning for Foundation Models
As foundation models (FMs) continue to shape the landscape of AI, the in-context learning (ICL) paradigm thrives but also encounters issues such as toxicity, hallucination, disparity, adversarial vulnerability, and inconsistency.
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).
Boosting API Recommendation with Implicit Feedback
In this paper, we propose a framework, BRAID (Boosting RecommendAtion with Implicit FeeDback), which leverages learning-to-rank and active learning techniques to boost recommendation performance.
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.
