1 citations · 2 across the 15 of their papers we have counts for
7 papers · 1 filter
Exploring a Layer-Wise Design Space for KV Cache Eviction
Chao Fei, Kaihua Liang, Panos Kalnis +4
KV cache eviction methods typically use a single retention-rule family throughout a model, making eviction-method identity a model-level design choice. Yet Transformer layers diffe…
Can Deep Neural Networks Improve Compression of Very Large Scientific Data?
Muhannad Alhumaidi, Guozhong Li, Spiros Skiadopoulos +1
Error-bounded lossy compression is a fundamental technique for managing the rapidly growing volumes of scientific data produced by modern simulations and observational instruments.…
LLMComp: A Language Modeling Paradigm for Error-Bounded Scientific Data Compression (Technical Report)
Guozhong Li, Muhannad Alhumaidi, Spiros Skiadopoulos +1
The rapid growth of high-resolution scientific simulations and observation systems is generating massive spatiotemporal datasets, making efficient, error-bounded compression increa…
LLMs Meet Cross-Modal Time Series Analytics: Overview and Directions
Chenxi Liu, Hao Miao, Cheng Long +3
Large Language Models (LLMs) have emerged as a promising paradigm for time series analytics, leveraging their massive parameters and the shared sequential nature of textual and tim…
GraphComp: Extreme Error-bounded Compression of Scientific Data via Temporal Graph Autoencoders
Guozhong Li, Muhannad Alhumaidi, Spiros Skiadopoulos +2
The generation of voluminous scientific data poses significant challenges for efficient storage, transfer, and analysis. Recently, error-bounded lossy compression methods emerged d…
RED: Effective Trajectory Representation Learning with Comprehensive Information
Silin Zhou, Shuo Shang, Lisi Chen +2
Trajectory representation learning (TRL) maps trajectories to vectors that can then be used for various downstream tasks, including trajectory similarity computation, trajectory cl…
Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient Modeling
Hussein Abdallah, Waleed Afandi, Panos Kalnis +1
A Knowledge Graph (KG) is a heterogeneous graph encompassing a diverse range of node and edge types. Heterogeneous Graph Neural Networks (HGNNs) are popular for training machine le…