← Authors

Jaesik Choi

38 papers · 2015–2025 · 10 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (10) 🌍 Conference Polyglot (10) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (10) 🧭 Keyword Pioneer 🐝 Cross-Pollinator (5) πŸ† Grand Slam πŸ‘‘ Triple Crown πŸ”¬ Deep Specialist (11) πŸ”₯ Unstoppable (8) πŸ’Ž Century Club (38) ⚑ Prolific Year (6) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (189) ❓ The Questioner

Conferences

AAAI (10) ICML (6) EMNLP (5) IJCAI (5) ICLR (3) NIPS (3) CVPR (2) ICCV (2) ACL (1) AISTATS (1)

Top co-authors

Anh Tong (7) Jiyeon Han (6) Kyowoon Lee (5) Haedong Jeong (5) Cheongwoong Kang (3) Giyoung Jeon (3) Dahee Kwon (3) Hwanil Choi (3) Wonjoon Chang (3) Seong-Whan Lee (3)

Keywords

image generation (5) large language model (4) neural network (4) gaussian process (4) generative adversarial network (4) deep neural network (3) representation learning (3) input attribution (2) unsupervised learning (2) attribution method (2) convolutional neural network (2) generative model (2) reinforcement learning (2) domain adaptation (2) neural network interpretability (2) feature learning (2) feature attribution (2) time series (2) diffusion model (2) integrated gradient (2)

Papers

Deontological Keyword Bias: The Impact of Modal Expressions on Normative Judgments of Language Models ACL 2025
Enhancing Creative Generation on Stable Diffusion-based Models CVPR 2025
Rethinking Shapley Value for Negative Interactions in Non-convex Games ICLR 2025
Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations ICCV 2025
When Format Changes Meaning: Investigating Semantic Inconsistency of Large Language Models EMNLP 2025
Diverse Rare Sample Generation with Pretrained GANs AAAI 2025
xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition AAAI 2025
Local Manifold Approximation and Projection for Manifold-Aware Diffusion Planning ICML 2025
Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning ICLR 2025
Memorizing Documents with Guidance in Large Language Models IJCAI 2024
Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement Learning IJCAI 2024
Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision AAAI 2024
Pathwise Explanation of ReLU Neural Networks AISTATS 2024
Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings AAAI 2024
CR-COPEC: Causal Rationale of Corporate Performance Changes to learn from Financial Reports EMNLP 2023
Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans NIPS 2023
Impact of Co-occurrence on Factual Knowledge of Large Language Models EMNLP 2023
Beyond Single Path Integrated Gradients for Reliable Input Attribution via Randomized Path Sampling ICCV 2023
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images ICLR 2023
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills ICML 2023
Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks? IJCAI 2022
An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks AAAI 2022
Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network NIPS 2022
Learning Fractional White Noises in Neural Stochastic Differential Equations NIPS 2022
The Global Banking Standards QA Dataset (GBS-QA) EMNLP 2021
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior AAAI 2021
Automatic Correction of Internal Units in Generative Neural Networks CVPR 2021
Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations AAAI 2021
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems AAAI 2021
Conditional Temporal Neural Processes with Covariance Loss ICML 2021
Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks AAAI 2020
An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks AAAI 2020
Discovering Latent Covariance Structures for Multiple Time Series ICML 2019
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes IJCAI 2019
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling ICML 2018
Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series ICML 2016
A Deterministic Partition Function Approximation for Exponential Random Graph Models IJCAI 2015
Reading Documents for Bayesian Online Change Point Detection EMNLP 2015