32 citations · 33 across the 8 of their papers we have counts for
8 papers
EntroCoT: Enhancing Chain-of-Thought via Adaptive Entropy-Guided Segmentation
Zihang Li, Yuhang Wang, Yikun Zong +6
Chain-of-Thought (CoT) prompting has significantly enhanced the mathematical reasoning capabilities of Large Language Models. We find existing fine-tuning datasets frequently suffe…
LiNo: Advancing Recursive Residual Decomposition of Linear and Nonlinear Patterns for Robust Time Series Forecasting
Guoqi Yu, Yaoming Li, Xiaoyu Guo +4
Forecasting models are pivotal in a data-driven world with vast volumes of time series data that appear as a compound of vast Linear and Nonlinear patterns. Recent deep time series…
INT-FlashAttention: Enabling Flash Attention for INT8 Quantization
Shimao Chen, Zirui Liu, Zhiying Wu +6
As the foundation of large language models (LLMs), self-attention module faces the challenge of quadratic time and memory complexity with respect to sequence length. FlashAttention…
SimiSketch: Efficiently Estimating Similarity of streaming Multisets
Fenghao Dong, Yang He, Yutong Liang +4
The challenge of estimating similarity between sets has been a significant concern in data science, finding diverse applications across various domains. However, previous approache…
CuckooGraph: A Scalable and Space-Time Efficient Data Structure for Large-Scale Dynamic Graphs
Zhuochen Fan, Yalun Cai, Zirui Liu +4
Graphs play an increasingly important role in various big data applications. However, existing graph data structures cannot simultaneously address the performance bottlenecks cause…
CAFE: Towards Compact, Adaptive, and Fast Embedding for Large-scale Recommendation Models
Hailin Zhang, Zirui Liu, Boxuan Chen +4
Recently, the growing memory demands of embedding tables in Deep Learning Recommendation Models (DLRMs) pose great challenges for model training and deployment. Existing embedding…
Experimental Analysis of Large-scale Learnable Vector Storage Compression
Hailin Zhang, Penghao Zhao, Xupeng Miao +4
Learnable embedding vector is one of the most important applications in machine learning, and is widely used in various database-related domains. However, the high dimensionality o…
ChameleMon: Shifting Measurement Attention as Network State Changes
Kaicheng Yang, Yuhan Wu, Ruijie Miao +8
Flow-level network measurement is critical to many network applications. Among various measurement tasks, packet loss detection and heavy-hitter detection are two most important me…