156 citations · 227 across the 15 of their papers we have counts for
16 papers
Inference-Time Scaling in Diffusion Models through Iterative Partial Refinement
Taegu Kang, Jaesik Yoon, Sungjin Ahn
Inference-time scaling has emerged as a major approach for improving reasoning capabilities, and has been increasingly applied to diffusion models. However, existing inference-time…
Compositional Monte Carlo Tree Diffusion for Extendable Planning
Jaesik Yoon, Hyeonseo Cho, Sungjin Ahn
Monte Carlo Tree Diffusion (MCTD) integrates diffusion models with structured tree search to enable effective trajectory exploration through stepwise reasoning. However, MCTD remai…
Loopholing Discrete Diffusion: Deterministic Bypass of the Sampling Wall
Mingyu Jo, Jaesik Yoon, Justin Deschenaux +2
Discrete diffusion models offer a promising alternative to autoregressive generation through parallel decoding, but they suffer from a sampling wall: once categorical sampling occu…
Fast Monte Carlo Tree Diffusion: 100x Speedup via Parallel Sparse Planning
Jaesik Yoon, Hyeonseo Cho, Yoshua Bengio +1
Diffusion models have recently emerged as a powerful approach for trajectory planning. However, their inherently non-sequential nature limits their effectiveness in long-horizon re…
Adaptive Inference-Time Scaling via Cyclic Diffusion Search
Gyubin Lee, Truong Nhat Nguyen Bao, Jaesik Yoon +4
Diffusion models have demonstrated strong generative capabilities across domains ranging from image synthesis to complex reasoning tasks. However, most inference-time scaling metho…
Monte Carlo Tree Diffusion for System 2 Planning
Jaesik Yoon, Hyeonseo Cho, Doojin Baek +2
Diffusion models have recently emerged as a powerful tool for planning. However, unlike Monte Carlo Tree Search (MCTS)-whose performance naturally improves with inference-time comp…
Spatially-Aware Transformer for Embodied Agents
Junmo Cho, Jaesik Yoon, Sungjin Ahn
Episodic memory plays a crucial role in various cognitive processes, such as the ability to mentally recall past events. While cognitive science emphasizes the significance of spat…
Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming
Hany Hamed, Subin Kim, Dongyeong Kim +2
Model-based reinforcement learning (MBRL) has been a primary approach to ameliorating the sample efficiency issue as well as to make a generalist agent. However, there has not been…
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning
Jaesik Yoon, Yi-Fu Wu, Heechul Bae +1
Unsupervised object-centric representation (OCR) learning has recently drawn attention as a new paradigm of visual representation. This is because of its potential of being an effe…
TransDreamer: Reinforcement Learning with Transformer World Models
Chang Chen, Yi-Fu Wu, Jaesik Yoon +1
The Dreamer agent provides various benefits of Model-Based Reinforcement Learning (MBRL) such as sample efficiency, reusable knowledge, and safe planning. However, its world model…
Generative Video Transformer: Can Objects be the Words?
Yi-Fu Wu, Jaesik Yoon, Sungjin Ahn
Transformers have been successful for many natural language processing tasks. However, applying transformers to the video domain for tasks such as long-term video generation and sc…
Robustifying Sequential Neural Processes
Jaesik Yoon, Gautam Singh, Sungjin Ahn
When tasks change over time, meta-transfer learning seeks to improve the efficiency of learning a new task via both meta-learning and transfer-learning. While the standard attentio…
Sequential Neural Processes
Gautam Singh, Jaesik Yoon, Youngsung Son +1
Neural Processes combine the strengths of neural networks and Gaussian processes to achieve both flexible learning and fast prediction in stochastic processes. However, a large cla…
One-Shot Learning for Text-to-SQL Generation
Dongjun Lee, Jaesik Yoon, Jongyun Song +2
Most deep learning approaches for text-to-SQL generation are limited to the WikiSQL dataset, which only supports very simple queries. Recently, template-based and sequence-to-seque…
Bayesian Model-Agnostic Meta-Learning
Taesup Kim, Jaesik Yoon, Ousmane Dia +3
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem. In this paper,…
Deep Learning on Key Performance Indicators for Predictive Maintenance in SAP HANA
Jaekoo Lee, Byunghan Lee, Jongyoon Song +4
With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly,…