model optimization
Model optimization involves tuning the parameters and architecture of a machine learning model to achieve the best performance on a given task, often balancing between accuracy and computational efficiency.
- Learning Gradient Boosted Decision Trees with Algorithmic Recourse
- Learning to Insert for Constructive Neural Vehicle Routing Solver
- MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
- QuestBench: Can LLMs ask the right question to acquire information in reasoning tasks?
- RidgeLoRA: Matrix Ridge Enhanced Low-Rank Adaptation of Large Language Models
- RoomEditor: High-Fidelity Furniture Synthesis with Parameter-Sharing U-Net