Junik Bae

I'm an undergraduate student in Seoul National University majoring in Computer Science and Engineering. Currently, I am advised by Prof. Youngwoon Lee and focusing on unsupervised RL research projects. My ambition is to develop innovative models that enhance decision-making processes and contribute novel insights and knowledge to mankind.

In the past, I served in Korean Air Force, specializing in deep learning model development. After that, I joined Naver Cloud Speech team as a research intern. During my undergraduate studies, I was involved in an undergraduate research program at Language and Data Intelligence Lab, focusing on Natural Language Understanding for code refinement. Also, I worked as a lab intern at SNU Vision and Learning Lab, studying Retrieval augmented generation in NLP.

Email  /  CV  /  Github

Research and Projects

My primary focus is on improving unsupervised reinforcement learning. Previously, I worked on projects in various fields, including computer vision, machine learning, speech recognition, and text-to-speech.

TLDR is a novel unsupervised GCRL algorithm that leverages Temporal Distance-aware Representations to improve both goal-directed exploration and goal-conditioned policy learning. TLDR significantly outperforms previous unsupervised GCRL methods in achieving a wide variety of states.

We introduce ESREAL, a novel unsupervised learning framework designed to reduce hallucinations in vision-language models by accurately identifying and penalizing hallucinated tokens. ESREAL results in significant reductions in hallucinations in models like LLaVA, InstructBLIP, and mPLUG-Owl2, without needing image-text pairs.

2nd Place, Awarded by Korean Minister of Science and Technology: 10,000,000 KRW.

Developed a model targeting the tasks of Korean Free-chat, Command-chat and Dialect-chat recognition using a variant of wav2vec model. Implemented Transformer LM beam-search decoder for the inference, enabling it to consider long-range contextual information effectively.

1st Place, Awarded by Korean Minister of Science and Technology: 20,000,000 KRW

Developed a model for Change Segmentation and Image Denoising tasks, utilizing a variant of U-Net model and several engineering techniques for the tasks.

(400+ stars) Implemented Microsoft’s NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality, which was a SOTA for the LJ Speech Dataset.

Implemented goal conditioned learning to transformer world-model-based agents, and tested with Atari Breakout. Empirically showed that the generative ability of transformer decoder models can be utilized to generate goal-conditioned trajectories. Final project for Data Science and Reinforcement Learning Course (2023-1).

Hostings & Talks

Delivered a comprehensive 5-week seminar series on Diffusion Models to the research team members in in Naver Cloud Voice Team. This series provided a theoretical explanation of Diffusion Models and an in-depth look at the practical applications in Text-to-Speech domain.

Hosted a presentation titled ’Understanding RLHF in Detail’ at Deepest (SNU Deep Learning Research Club), providing an in-depth explanation of Proximal Policy Optimization (PPO) with preferences datasets and Direct Policy Optimization (DPO).

Hosted a presentation titled 'A Path Towards Autonomous AI' at Deepest (SNU Deep Learning Research Club), which explains motivations for Joint-Embedding Predictive Archiecture proposed by Yann Lecun, and some recent works utilizing this architecture.

Website source code credit to Dr. Jon Barron.