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Found 9 papers, 0 papers with code
Knowledge Distillation for Enhancing Walmart E-commerce Search Relevance Using Large Language Models
To leverage the ranking power of LLMs while meeting the low-latency demands of production systems, we propose a novel framework that distills a high performing LLM into a more efficient, low-latency student model.
Global-Decision-Focused Neural ODEs for Proactive Grid Resilience Management
Extreme hazard events such as wildfires and hurricanes increasingly threaten power systems, causing widespread outages and disrupting critical services.
RIS-based Over-the-air Diffractional Channel Coding
Reconfigurable Intelligent Surfaces (RIS) are programmable metasurfaces utilizing sub-wavelength meta-atoms and a controller for precise electromagnetic wave manipulation.
Counterfactual Fairness through Transforming Data Orthogonal to Bias
We propose a novel data pre-processing algorithm, Orthogonal to Bias (OB), which is designed to eliminate the influence of a group of continuous sensitive variables, thus promoting counterfactual fairness in machine learning applications.
RIS-Based On-the-Air Semantic Communications -- a Diffractional Deep Neural Network Approach
Semantic communication has gained significant attention recently due to its advantages in achieving higher transmission efficiency by focusing on semantic information instead of bit-level information.
API-Assisted Code Generation for Question Answering on Varied Table Structures
A persistent challenge to table question answering (TableQA) by generating executable programs has been adapting to varied table structures, typically requiring domain-specific logical forms.
Uncertainty-Aware Robust Learning on Noisy Graphs
Graph neural networks have shown impressive capabilities in solving various graph learning tasks, particularly excelling in node classification.
Approach to Learning Generalized Audio Representation Through Batch Embedding Covariance Regularization and Constant-Q Transforms
Preliminary results show (1) the proposed BECR can incur a more dispersed embedding on the test set, (2) BECR improves the PaSST model without extra computation complexity, and (3) STFT preprocessing outperforms CQT in all tasks we tested.
Non-intrusive Load Monitoring based on Self-supervised Learning
Based on the NILM results in various cases, SSL generally outperforms zero-shot learning in improving load disaggregation performance without any sub-metering data from the target data sets.
