15 citations · 27 across the 9 of their papers we have counts for
10 papers
Dynamic Tokenization via Reinforcement Patching: End-to-end Training and Zero-shot Transfer
Yulun Wu, Sravan Kumar Ankireddy, Samuel Sharpe +4
Efficiently aggregating spatial or temporal horizons to acquire compact representations has become a unifying principle in modern deep learning models, yet learning data-adaptive r…
TimeSqueeze: Dynamic Patching for Efficient Time Series Forecasting
Sravan Kumar Ankireddy, Nikita Seleznev, Nam H. Nguyen +4
Transformer-based time series foundation models face a fundamental trade-off in choice of tokenization: point-wise embeddings preserve temporal fidelity but scale poorly with seque…
Deep TPC: Temporal-Prior Conditioning for Time Series Forecasting
Filippos Bellos, NaveenJohn Premkumar, Yannis Avrithis +2
LLM-for-time series (TS) methods typically treat time shallowly, injecting positional or prompt-based cues once at the input of a largely frozen decoder, which limits temporal reas…
Temporal Tokenization Strategies for Event Sequence Modeling with Large Language Models
Zefang Liu, Nam H. Nguyen, Yinzhu Quan +1
Representing continuous time is a critical and under-explored challenge in modeling temporal event sequences with large language models (LLMs). Various strategies like byte-level r…
BEDTime: A Unified Benchmark for Automatically Describing Time Series
Medhasweta Sen, Zachary Gottesman, Jiaxing Qiu +3
Recent works propose complex multi-modal models that handle both time series and language, ultimately claiming high performance on complex tasks like time series reasoning and cros…
VITRO: Vocabulary Inversion for Time-series Representation Optimization
Filippos Bellos, Nam H. Nguyen, Jason J. Corso
Although LLMs have demonstrated remarkable capabilities in processing and generating textual data, their pre-trained vocabularies are ill-suited for capturing the nuanced temporal…