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researcher

Qitong Wang

Harvard University

14 papers hereh-index 8171 citations22 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author11
  • last author1

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • cs.DB7
  • cs.LG3
  • cs.CV1
  • cs.GR1
  • eess.IV1
  • q-fin.ST1
affiliations
  • Harvard University
Homepage
same name
  • Qitong Wang — 8 papers, h 2
  • Qitong Wang — 4 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedDumpy: A Compact and Adaptive Index for Large Data Series Collections

18 citations · 50 across the 14 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.DB2026★ 13 cited

SEAnet: A Deep Learning Architecture for Data Series Similarity Search

Qitong Wang, Themis Palpanas

A key operation for massive data series collection analysis is similarity search. According to recent studies, SAX-based indexes offer state-of-the-art performance for similarity s…

cs.LG2026

A Lightweight Sparse Interaction Network for Time Series Forecasting

Xu Zhang, Qitong Wang, Peng Wang +1

Recent work shows that linear models can outperform several transformer models in long-term time-series forecasting (TSF). However, instead of explicitly performing temporal intera…

cs.LG2026

SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting

Xu Zhang, Qitong Wang, Peng Wang +1

Modeling multiscale patterns is crucial for long-term time series forecasting (TSF). However, redundancy and noise in time series, together with semantic gaps between non-adjacent…

cs.LG2026

ParisKV: Fast and Drift-Robust KV-Cache Retrieval for Long-Context LLMs

Yanlin Qi, Xinhang Chen, Huiqiang Jiang +3

KV-cache retrieval is essential for long-context LLM inference, yet existing methods struggle with distribution drift and high latency at scale. We introduce ParisKV, a drift-robus…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.