3 papers
cs.IR2026
Exploring Bottom-Up Clustering for Creating Semantic IDs
Leah Woldemariam, Sudhanshu Garg, Taha Belkhouja +2
The success of generative retrieval has largely been attributed to the use of Semantic IDs, which improve over arbitrary item-level identifiers such as hashes by capturing the sema…
cs.LG2023
Compressed and Sparse Models for Non-Convex Decentralized Learning
Andrew Campbell, Hang Liu, Leah Woldemariam +1
Recent research highlights frequent model communication as a significant bottleneck to the efficiency of decentralized machine learning (ML), especially for large-scale and over-pa…
cs.IT2023
Low-Complexity Vector Source Coding for Discrete Long Sequences with Unknown Distributions
Leah Woldemariam, Hang Liu, Anna Scaglione
In this paper, we propose a source coding scheme that represents data from unknown distributions through frequency and support information. Existing encoding schemes often compress…