8 citations · 29 across the 10 of their papers we have counts for
11 papers
Make Prompts Adaptable: Bayesian Modeling for Vision-Language Prompt Learning with Data-Dependent Prior
Youngjae Cho, HeeSun Bae, Seungjae Shin +3
Recent Vision-Language Pretrained (VLP) models have become the backbone for many downstream tasks, but they are utilized as frozen model without learning. Prompt learning is a meth…
Loss-Curvature Matching for Dataset Selection and Condensation
Seungjae Shin, Heesun Bae, Donghyeok Shin +2
Training neural networks on a large dataset requires substantial computational costs. Dataset reduction selects or synthesizes data instances based on the large dataset, while mini…
Neural Posterior Regularization for Likelihood-Free Inference
Dongjun Kim, Kyungwoo Song, Seungjae Shin +3
A simulation is useful when the phenomenon of interest is either expensive to regenerate or irreproducible with the same context. Recently, Bayesian inference on the distribution o…
Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder
Hyemi Kim, Seungjae Shin, JoonHo Jang +4
The problem of fair classification can be mollified if we develop a method to remove the embedded sensitive information from the classification features. This line of separating th…
Sequential Likelihood-Free Inference with Neural Proposal
Dongjun Kim, Kyungwoo Song, YoonYeong Kim +4
Bayesian inference without the likelihood evaluation, or likelihood-free inference, has been a key research topic in simulation studies for gaining quantitatively validated simulat…
The layer number of -evenly distributed point sets
Ilkyoo Choi, Weonyoung Joo, Minki Kim
For a finite point set in , we consider a peeling process where the vertices of the convex hull are removed at each step. The layer number of a given point set…
Adversarial Likelihood-Free Inference on Black-Box Generator
Dongjun Kim, Weonyoung Joo, Seungjae Shin +2
Generative Adversarial Network (GAN) can be viewed as an implicit estimator of a data distribution, and this perspective motivates using the adversarial concept in the true input p…
Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation
Seungjae Shin, Kyungwoo Song, JoonHo Jang +3
Recent research demonstrates that word embeddings, trained on the human-generated corpus, have strong gender biases in embedding spaces, and these biases can result in the discrimi…
Generalized Gumbel-Softmax Gradient Estimator for Generic Discrete Random Variables
Weonyoung Joo, Dongjun Kim, Seungjae Shin +1
Estimating the gradients of stochastic nodes in stochastic computational graphs is one of the crucial research questions in the deep generative modeling community, which enables th…
Sequential Recommendation with Relation-Aware Kernelized Self-Attention
Mingi Ji, Weonyoung Joo, Kyungwoo Song +2
Recent studies identified that sequential Recommendation is improved by the attention mechanism. By following this development, we propose Relation-Aware Kernelized Self-Attention…
Dirichlet Variational Autoencoder
Weonyoung Joo, Wonsung Lee, Sungrae Park +1
This paper proposes Dirichlet Variational Autoencoder (DirVAE) using a Dirichlet prior for a continuous latent variable that exhibits the characteristic of the categorical probabil…