1 citations · 2 across the 5 of their papers we have counts for
5 papers
Fast Topology-Aware Lossy Data Compression with Full Preservation of Critical Points and Local Order
Alex Fallin, Nathaniel Gorski, Tripti Agarwal +3
Many scientific codes and instruments generate large amounts of floating-point data at high rates that must be compressed before they can be stored. Typically, only lossy compressi…
TopoSZp: Lightweight Topology-Aware Error-controlled Compression for Scientific Data
Tripti Agarwal, Sheng Di, Xin Liang +5
Error-bounded lossy compression is essential for managing the massive data volumes produced by large-scale HPC simulations. While state-of-the-art compressors such as SZ and ZFP pr…
Derailing Non-Answers via Logit Suppression at Output Subspace Boundaries in RLHF-Aligned Language Models
Harvey Dam, Jonas Knochelmann, Vinu Joseph +1
We introduce a method to reduce refusal rates of large language models (LLMs) on sensitive content without modifying model weights or prompts. Motivated by the observation that ref…
Local-Order Auxiliary Losses Can Improve Autoencoder Reconstruction
Harvey Dam, Martin Burtscher, Tripti Agarwal +1
Mean-squared error is the default objective for training autoencoders, yet compressed reconstructions often depend not only on pointwise accuracy but also on preserving local spati…
HoSZp: An Efficient Homomorphic Error-bounded Lossy Compressor for Scientific Data
Tripti Agarwal, Sheng Di, Jiajun Huang +7
Error-bounded lossy compression has been a critical technique to significantly reduce the sheer amounts of simulation datasets for high-performance computing (HPC) scientific appli…