1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2024
ResidualDroppath: Enhancing Feature Reuse over Residual Connections
Sejik Park
Residual connections are one of the most important components in neural network architectures for mitigating the vanishing gradient problem and facilitating the training of much de…
cs.AI2024
Diverse Feature Learning by Self-distillation and Reset
Sejik Park
Our paper addresses the problem of models struggling to learn diverse features, due to either forgetting previously learned features or failing to learn new ones. To overcome this…
cs.LG2023★ 1 cited
Learning to Discover Skills through Guidance
Hyunseung Kim, Byungkun Lee, Hojoon Lee +4
In the field of unsupervised skill discovery (USD), a major challenge is limited exploration, primarily due to substantial penalties when skills deviate from their initial trajecto…