11 citations · 12 across the 9 of their papers we have counts for
9 papers
SCOPE: Scalable Training-Free Semantic Coreset Selection for Long-Tailed Federated Learning
Md Anwar Hossen, Nathan R. Tallent, Luanzheng Guo +1
Scientific discovery increasingly relies on federated learning over data from high-resolution instruments, and these data are strongly non-IID and long-tailed. Coreset selection co…
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…
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…
SuperSAM: Crafting a SAM Supernetwork via Structured Pruning and Unstructured Parameter Prioritization
Waqwoya Abebe, Sadegh Jafari, Sixing Yu +6
Neural Architecture Search (NAS) is a powerful approach of automating the design of efficient neural architectures. In contrast to traditional NAS methods, recently proposed one-sh…
Final Report for CHESS: Cloud, High-Performance Computing, and Edge for Science and Security
Nathan Tallent, Jan Strube, Luanzheng Guo +9
Automating the theory-experiment cycle requires effective distributed workflows that utilize a computing continuum spanning lab instruments, edge sensors, computing resources at mu…
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…
SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentation
Waqwoya Abebe, Jan Strube, Luanzheng Guo +5
Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics - where are boundaries locate…