7 papers
CaliSplat: Sparse Gaussian Fitting and Calibrated Rate Allocation for Image Compression
Zhenbang Zhang, Zhengquan Luo, Kai Zhao +1
2D Gaussian representations provide an explicit and lightweight alternative for image representation, enabling fast deterministic decoding without a heavy neural synthesis network.…
What Are LLM Agents Actually Learning on Low-Budget Molecular Optimization Benchmarks?
Jiayu Qin, Zhengquan Luo, Changyou Chen +1
Recent work reports large gains for LLM agents on low-budget molecular optimization. We measure the contributions of online optimization, retrievable task information, and permissi…
Path-Guided Flow Matching for Dataset Distillation
Xuhui Li, Zhengquan Luo, Xiwei Liu +2
Dataset distillation compresses large datasets into compact synthetic sets with comparable performance in training models. Despite recent progress on diffusion-based distillation,…
KGOT: Unified Knowledge Graph and Optimal Transport Pseudo-Labeling for Molecule-Protein Interaction Prediction
Jiayu Qin, Zhengquan Luo, Guy Tadmor +3
Predicting molecule-protein interactions (MPIs) is a fundamental task in computational biology, with crucial applications in drug discovery and molecular function annotation. Howev…
GeoDM: Geometry-aware Distribution Matching for Dataset Distillation
Xuhui Li, Zhengquan Luo, Zihui Cui +1
Dataset distillation aims to synthesize a compact subset of the original data, enabling models trained on it to achieve performance comparable to those trained on the original larg…
Local-Curvature-Aware Knowledge Graph Embedding: An Extended Ricci Flow Approach
Zhengquan Luo, Guy Tadmor, Or Amar +2
Knowledge graph embedding (KGE) relies on the geometry of the embedding space to encode semantic and structural relations. Existing methods place all entities on one homogeneous ma…
Utility Boundary of Dataset Distillation: Scaling and Configuration-Coverage Laws
Zhengquan Luo, Zhiqiang Xu
Dataset distillation (DD) aims to construct compact synthetic datasets that allow models to achieve comparable performance to full-data training while substantially reducing storag…