6 citations · 10 across the 10 of their papers we have counts for
9 papers · 1 filter
AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Masking
Jungkyu Kim, Taeyoung Park, Kibok Lee
Score-based diffusion models have emerged as prominent deep generative models; however, their application to tabular data remains challenging because their backbones assume fully s…
When and Where to Reset Matters for Long-Term Test-Time Adaptation
Taejun Lim, Joong-Won Hwang, Kibok Lee
When continual test-time adaptation (TTA) persists over the long term, errors accumulate in the model and further cause it to predict only a few classes for all inputs, a phenomeno…
Channel Normalization for Time Series Channel Identification
Seunghan Lee, Taeyoung Park, Kibok Lee
Channel identifiability (CID) refers to the ability to distinguish between individual channels in time series (TS) modeling. The absence of CID often results in producing identical…
To Predict or Not To Predict? Proportionally Masked Autoencoders for Tabular Data Imputation
Jungkyu Kim, Kibok Lee, Taeyoung Park
Masked autoencoders (MAEs) have recently demonstrated effectiveness in tabular data imputation. However, due to the inherent heterogeneity of tabular data, the uniform random maski…
Sequential Order-Robust Mamba for Time Series Forecasting
Seunghan Lee, Juri Hong, Kibok Lee +1
Mamba has recently emerged as a promising alternative to Transformers, offering near-linear complexity in processing sequential data. However, while channels in time series (TS) da…
ANT: Adaptive Noise Schedule for Time Series Diffusion Models
Seunghan Lee, Kibok Lee, Taeyoung Park
Advances in diffusion models for generative artificial intelligence have recently propagated to the time series (TS) domain, demonstrating state-of-the-art performance on various t…
Dataset-Driven Channel Masks in Transformers for Multivariate Time Series
Seunghan Lee, Taeyoung Park, Kibok Lee
Recent advancements in foundation models have been successfully extended to the time series (TS) domain, facilitated by the emergence of large-scale TS datasets. However, previous…
Learning to Embed Time Series Patches Independently
Seunghan Lee, Taeyoung Park, Kibok Lee
Masked time series modeling has recently gained much attention as a self-supervised representation learning strategy for time series. Inspired by masked image modeling in computer…
Soft Contrastive Learning for Time Series
Seunghan Lee, Taeyoung Park, Kibok Lee
Contrastive learning has shown to be effective to learn representations from time series in a self-supervised way. However, contrasting similar time series instances or values from…