7 papers
SCOPE: Semantic Coreset with Orthogonal Projection Embeddings for Federated learning
Md Anwar Hossen, Nathan R. Tallent, Luanzheng Guo +1
Scientific discovery increasingly requires learning on federated datasets, fed by streams from high-resolution instruments, that have extreme class imbalance. Current ML approaches…
ProHD: Projection-Based Hausdorff Distance Approximation
Jiuzhou Fu, Luanzheng Guo, Nathan R. Tallent +1
The Hausdorff distance (HD) is a robust measure of set dissimilarity, but computing it exactly on large, high-dimensional datasets is prohibitively expensive. We propose \textbf{Pr…
On The Reproducibility Limitations of RAG Systems
Baiqiang Wang, Dongfang Zhao, Nathan R Tallent +1
Retrieval-Augmented Generation (RAG) is increasingly employed in generative AI-driven scientific workflows to integrate rapidly evolving scientific knowledge bases, yet its reliabi…
Order-Preserving Dimension Reduction for Multimodal Semantic Embedding
Chengyu Gong, Gefei Shen, Luanzheng Guo +2
Searching for the -nearest neighbors (KNN) in multimodal data retrieval is computationally expensive, particularly due to the inherent difficulty in comparing similarity measure…
PowerTrip: Exploiting Federated Heterogeneous Datacenter Power for Distributed ML Training
Talha Mehboob, Luanzheng Guo, Nathan Tallent +2
The exponential growth of large-scale AI models has led to computational and power demands that can exceed the capacity of a single data center. This is due to the limited power su…
HybHuff: Lossless Compression for Hypergraphs via Entropy-Guided Huffman-Bitwise Coordination
Tianyu Zhao, Dongfang Zhao, Luanzheng Guo +1
Hypergraphs provide a natural representation for many-to-many relationships in data-intensive applications, yet their scalability is often hindered by high memory consumption. Whil…