3 citations · 5 across the 9 of their papers we have counts for
9 papers
LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models
Taekhyun Park, Yongjae Lee, Dohee Kim +1
Looped computation shows promise in improving the reasoning-oriented performance of LLMs by scaling test-time compute. However, existing approaches typically require either trainin…
ACFormer: Mitigating Non-linearity with Auto Convolutional Encoder for Time Series Forecasting
Gawon Lee, Hanbyeol Park, Minseop Kim +2
Time series forecasting (TSF) faces challenges in modeling complex intra-channel temporal dependencies and inter-channel correlations. Although recent research has highlighted the…
IConv: Focusing on Local Variation with Channel Independent Convolution for Multivariate Time Series Forecasting
Gawon Lee, Hanbyeol Park, Minseop Kim +2
Real-world time-series data often exhibit non-stationarity, including changing trends, irregular seasonality, and residuals. In terms of changing trends, recently proposed multi-la…
JustDense: Just using Dense instead of Sequence Mixer for Time Series analysis
TaekHyun Park, Yongjae Lee, Daesan Park +2
Sequence and channel mixers, the core mechanism in sequence models, have become the de facto standard in time series analysis (TSA). However, recent studies have questioned the nec…
Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning
Sunghyun Sim, Ling Liu, Hyerim Bae
Process mining is a methodology for the derivation and analysis of process models based on the event log. When process mining is employed to analyze business processes, the process…
Angular triangle distance for ordinal metric learning
Imam Mustafa Kamal, Hyerim Bae
Deep metric learning (DML) aims to automatically construct task-specific distances or similarities of data, resulting in a low-dimensional representation. Several significant metri…
Metric Learning as a Service with Covariance Embedding
Imam Mustafa Kamal, Hyerim Bae, Ling Liu
With the emergence of deep learning, metric learning has gained significant popularity in numerous machine learning tasks dealing with complex and large-scale datasets, such as inf…
Semi-supervised binary classification with latent distance learning
Imam Mustafa Kamal, Hyerim Bae
Binary classification (BC) is a practical task that is ubiquitous in real-world problems, such as distinguishing healthy and unhealthy objects in biomedical diagnostics and defecti…
Correlation recurrent units: A novel neural architecture for improving the predictive performance of time-series data
Sunghyun Sim, Dohee Kim, Hyerim Bae
The time-series forecasting (TSF) problem is a traditional problem in the field of artificial intelligence. Models such as Recurrent Neural Network (RNN), Long Short Term Memory (L…