1 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
Learning More Generalized Experts by Merging Experts in Mixture-of-Experts
Sejik Park
We observe that incorporating a shared layer in a mixture-of-experts can lead to performance degradation. This leads us to hypothesize that learning shared features poses challenge…
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…
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…
Emotional Voice Conversion using Multitask Learning with Text-to-speech
Tae-Ho Kim, Sungjae Cho, Shinkook Choi +2
Voice conversion (VC) is a task to transform a person's voice to different style while conserving linguistic contents. Previous state-of-the-art on VC is based on sequence-to-seque…