1 citations · 2 across the 8 of their papers we have counts for
9 papers
Continuous-Time Motion Planning and Steering with Global Convergence
Mohsen Amiri, Sima Didari, Davoud Ataee Tarzanagh +2
Motion planning is central to robotics, and it is increasingly used to correct the motions proposed by learned planners and policies. In both uses, constraints must hold at every i…
Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guarantees
Zhuoping Zhou, Davoud Ataee Tarzanagh, Sima Didari +7
Graph-based Retrieval-Augmented Generation (RAG) systems leverage interconnected knowledge structures to capture complex relationships that flat retrieval struggles with, enabling…
PaVeRL-SQL: Text-to-SQL via Partial-Match Rewards and Verbal Reinforcement Learning
Heng Hao, Wenjun Hu, Oxana Verkholyak +6
Text-to-SQL models allow users to interact with a database more easily by generating executable SQL statements from natural-language questions. Despite recent successes on simpler…
ViewPCL: a point cloud based active learning method for multi-view segmentation
Christian Hilaire, Sima Didari
We propose a novel active learning framework for multi-view semantic segmentation. This framework relies on a new score that measures the discrepancy between point cloud distributi…
Bayesian Active Learning for Semantic Segmentation
Sima Didari, Wenjun Hu, Jae Oh Woo +3
Fully supervised training of semantic segmentation models is costly and challenging because each pixel within an image needs to be labeled. Therefore, the sparse pixel-level annota…
Improving Instruction Following in Language Models through Proxy-Based Uncertainty Estimation
JoonHo Lee, Jae Oh Woo, Juree Seok +11
Assessing response quality to instructions in language models is vital but challenging due to the complexity of human language across different contexts. This complexity often resu…
Self-Supervised Contrastive Representation Learning for 3D Mesh Segmentation
Ayaan Haque, Hankyu Moon, Heng Hao +3
3D deep learning is a growing field of interest due to the vast amount of information stored in 3D formats. Triangular meshes are an efficient representation for irregular, non-uni…
PatchNet: Unsupervised Object Discovery based on Patch Embedding
Hankyu Moon, Heng Hao, Sima Didari +2
We demonstrate that frequently appearing objects can be discovered by training randomly sampled patches from a small number of images (100 to 200) by self-supervision. Key to this…
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification
Heng Hao, Hankyu Moon, Sima Didari +2
We propose a highly data-efficient active learning framework for image classification. Our novel framework combines: (1) unsupervised representation learning of a Convolutional Neu…