7 citations · 7 across the 12 of their papers we have counts for
15 papers
OneShot: Label-Efficient Detector Weighting for Time Series Anomaly Detection
Seunghan Lee, Jun Seo, Jaehoon Lee +9
Time series (TS) anomaly detection (AD) lacks a universal detector, with the best choice determined by the series rather than by the algorithm. Existing methods therefore either se…
XForecast: The KDD 2026 Finance Day Challenge on News-Grounded Financial Forecasting
Jaehoon Lee, Jun Seo, Seunghan Lee +10
Financial forecasting has traditionally relied on numerical time-series (TS) signals, while textual information such as news remains largely underutilized. We introduce XForecast,…
EXAONE Finance 1.0: An Attention-free Time Series Foundation Model for Financial Time Series
Seunghan Lee, Jaehoon Lee, Jun Seo +9
This technical report presents EXAONE Forecast for Finance (EXAONE Finance), a financial time series foundation model (TSFM) tailored to financial forecasting. While recent TSFMs a…
FinVerse: Toward More Realistic Evaluation for Financial Time-Series Forecasting
Jaehoon Lee, Jun Seo, Seunghan Lee +17
Time-series foundation models (TSFMs) are increasingly evaluated on broad benchmarks that apply uniform conventional forecast evaluation metrics across heterogeneous time-series. H…
ReasonCast: Towards Explainable Time Series Forecasting with Reasoning
Seunghan Lee, Jun Seo, Jaehoon Lee +9
Most time series (TS) models are specialized for a single task, either understanding (i.e., returning text answers about a TS) or generation (i.e., returning a numeric forecast). O…
Beyond Magnitude and Shape: A Direction-Aware Loss for Time Series Forecasting
Seunghan Lee, Jaehoon Lee, Jun Seo +9
The direction of change --- whether a series will move up or down --- is often as important as its exact value in decisiondriven applications such as risk management and financial…
AdaTKG: Adaptive Memory for Temporal Knowledge Graph Reasoning
Seunghan Lee, Jun Seo, Jaehoon Lee +7
Temporal knowledge graphs (TKGs) represent time-stamped relational facts and support a wide range of reasoning tasks over evolving events. However, existing methods produce entity…
FinSTaR: Towards Financial Reasoning with Time Series Reasoning Models
Seunghan Lee, Jun Seo, Jaehoon Lee +7
Time series (TS) reasoning models (TSRMs) have shown promising capabilities in general domains, yet they consistently fail on financial domain, which exhibit unique characteristics…
Rethinking Multimodal Fusion for Time Series: Text Modalities Need Constrained Fusion
Seunghan Lee, Jun Seo, Jaehoon Lee +7
Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting. However, most existing metho…
Not All Retrievals are Useful: Cross-Attention for Input-Aware RAG in Time Series Forecasting
Seunghan Lee, Jaehoon Lee, Jun Seo +7
Retrieval-augmented generation (RAG) enhances zero-shot time series (TS) forecasting by leveraging external knowledge bases, yet existing approaches overlook input-level relevance…
FinTexTS: Financial Text-Paired Time-Series Dataset via Semantic-Based and Multi-Level Pairing
Jaehoon Lee, Suhwan Park, Taeyoon Lim +9
The financial domain involves a variety of important time-series problems. Recently, time-series analysis methods that jointly leverage textual and numerical information have gaine…
EXAONE Path 2.0: Pathology Foundation Model with End-to-End Supervision
Myeongjang Pyeon, Janghyeon Lee, Minsoo Lee +7
In digital pathology, whole-slide images (WSIs) are often difficult to handle due to their gigapixel scale, so most approaches train patch encoders via self-supervised learning (SS…
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images
Jiyeon Han, Hwanil Choi, Yunjey Choi +3
Evaluation metrics in image synthesis play a key role to measure performances of generative models. However, most metrics mainly focus on image fidelity. Existing diversity metrics…
Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?
Hwanil Choi, Wonjoon Chang, Jaesik Choi
Even though Generative Adversarial Networks (GANs) have shown a remarkable ability to generate high-quality images, GANs do not always guarantee the generation of photorealistic im…
Automatic Correction of Internal Units in Generative Neural Networks
Ali Tousi, Haedong Jeong, Jiyeon Han +2
Generative Adversarial Networks (GANs) have shown satisfactory performance in synthetic image generation by devising complex network structure and adversarial training scheme. Even…