Search Results for author:
Found 8 papers, 4 papers with code
Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation
Recent advances in Large Language Models (LLMs) have demonstrated significant potential in the field of Recommendation Systems (RSs).
PAIL: Performance based Adversarial Imitation Learning Engine for Carbon Neutral Optimization
In addition, PAIL offers meaningful interpretability for the optimization in carbon neutrality.
Learning-driven Physically-aware Large-scale Circuit Gate Sizing
Gate sizing plays an important role in timing optimization after physical design.
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
To evaluate the editing impact on the behaviours of LLMs, we propose two model editing studies for medical domain: (1) editing factual knowledge for medical specialization and (2) editing the explanatory ability for complex knowledge.
Cognitive Evolutionary Search to Select Feature Interactions for Click-Through Rate Prediction
Inspired by natural evolution, we propose a general Cognitive EvoLutionary Search (CELS) framework, where cognitive ability refers to the malleability of organisms to orientate to the environment.
Ranked #3 on
Click-Through Rate Prediction
on Avazu
XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction
Motivated by this, we propose a novel Extreme Cross Network, abbreviated XCrossNet, which aims at learning dense and sparse feature interactions in an explicit manner.
Ranked #24 on
Click-Through Rate Prediction
on Criteo
Long-term Joint Scheduling for Urban Traffic
Recently, the traffic congestion in modern cities has become a growing worry for the residents.
