4 citations · 17 across the 59 of their papers we have counts for
6 papers · 1 filter
Robust Deep Learning from Crowds with Belief Propagation
Hoyoung Kim, Seunghyuk Cho, Dongwoo Kim +1
Crowdsourcing systems enable us to collect large-scale dataset, but inherently suffer from noisy labels of low-paid workers. We address the inference and learning problems using su…
Learning Continuous Representation of Audio for Arbitrary Scale Super Resolution
Jaechang Kim, Yunjoo Lee, Seunghoon Hong +1
Audio super resolution aims to predict the missing high resolution components of the low resolution audio signals. While audio in nature is a continuous signal, current approaches…
Gradient Inversion with Generative Image Prior
Jinwoo Jeon, Jaechang Kim, Kangwook Lee +2
Federated Learning (FL) is a distributed learning framework, in which the local data never leaves clients devices to preserve privacy, and the server trains models on the data via…
Multi-armed Bandit Algorithm against Strategic Replication
Suho Shin, Seungjoon Lee, Jungseul Ok
We consider a multi-armed bandit problem in which a set of arms is registered by each agent, and the agent receives reward when its arm is selected. An agent might strategically su…
Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning
Byungchan Ko, Jungseul Ok
In deep reinforcement learning (RL), data augmentation is widely considered as a tool to induce a set of useful priors about semantic consistency and improve sample efficiency and…
Transfer Learning in Bandits with Latent Continuity
Hyejin Park, Seiyun Shin, Kwang-Sung Jun +1
Structured stochastic multi-armed bandits provide accelerated regret rates over the standard unstructured bandit problems. Most structured bandits, however, assume the knowledge of…