11 citations · 34 across the 7 of their papers we have counts for
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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…
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model
HeeSun Bae, Seungjae Shin, Byeonghu Na +3
Noisy labels are inevitable yet problematic in machine learning society. It ruins the generalization of a classifier by making the classifier over-fitted to noisy labels. Existing…
Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation
Dongjun Kim, Seungjae Shin, Kyungwoo Song +2
Recent advances in diffusion models bring state-of-the-art performance on image generation tasks. However, empirical results from previous research in diffusion models imply an inv…
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…
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…
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…
Bivariate Beta-LSTM
Kyungwoo Song, JoonHo Jang, Seung jae Shin +1
Long Short-Term Memory (LSTM) infers the long term dependency through a cell state maintained by the input and the forget gate structures, which models a gate output as a value in…