no code implementations • 13 Apr 2024 • Mukul Gagrani, Raghavv Goel, Wonseok Jeon, Junyoung Park, Mingu Lee, Christopher Lott
We show that a language-only model can serve as a good draft model for speculative decoding with LLaVA 7B, bypassing the need for image tokens and their associated processing components from the draft model.
no code implementations • 29 Feb 2024 • Raghavv Goel, Mukul Gagrani, Wonseok Jeon, Junyoung Park, Mingu Lee, Christopher Lott
In this paper, we propose a simple draft model training framework for direct alignment to chat-capable target models.
no code implementations • 21 Feb 2024 • Wonseok Jeon, Mukul Gagrani, Raghavv Goel, Junyoung Park, Mingu Lee, Christopher Lott
We empirically evaluate RSD with Llama 2 and OPT models, showing that RSD outperforms the baseline methods, consistently for fixed draft sequence length and in most cases for fixed computational budgets at LLM.
1 code implementation • 10 Nov 2023 • Junyoung Park, Jin Kim, Hyeongjun Kwon, Ilhoon Yoon, Kwanghoon Sohn
Given the inevitability of domain shifts during inference in real-world applications, test-time adaptation (TTA) is essential for model adaptation after deployment.
no code implementations • 22 Oct 2023 • Abhay Sobhanan, Junyoung Park, Jinkyoo Park, Changhyun Kwon
For each higher-level decision candidate, we predict the objective function values of the underlying vehicle routing problems using a pre-trained graph neural network without actually solving the routing problems.
1 code implementation • 29 Jun 2023 • Federico Berto, Chuanbo Hua, Junyoung Park, Minsu Kim, Hyeonah Kim, Jiwoo Son, Haeyeon Kim, Joungho Kim, Jinkyoo Park
To address these challenges, we introduce RL4CO, a unified Reinforcement Learning (RL) for Combinatorial Optimization (CO) library.
no code implementations • 5 Feb 2023 • Vivian W. H. Wong, Sang Hun Kim, Junyoung Park, Jinkyoo Park, Kincho H. Law
The interrupting swap-allowed blocking job shop problem (ISBJSSP) is a complex scheduling problem that is able to model many manufacturing planning and logistics applications realistically by addressing both the lack of storage capacity and unforeseen production interruptions.
no code implementations • 22 Nov 2022 • Haewon Jung, Junyoung Park, Jinkyoo Park
Convex quadratic programming (QP) is an important sub-field of mathematical optimization.
no code implementations • 24 Sep 2022 • Junsik Shin, Junyoung Park, JongWoong Park
To overcome the complication, lots of research related to vibration-based monitoring system with sensor has been devised.
no code implementations • 6 Jun 2022 • Minjun Kim, Junyoung Park, Jinkyoo Park
Inspired by CE, we propose Neuro CE (NCE), a fundamental operator of learned meta-heuristic, to solve various VRPs while overcoming the limitations of CE (i. e., the expensive $\mathcal{O}(n^4)$ search cost).
no code implementations • 1 Jun 2022 • Junyoung Park, Federico Berto, Arec Jamgochian, Mykel J. Kochenderfer, Jinkyoo Park
In this paper, we propose Meta-SysId, a meta-learning approach to model sets of systems that have behavior governed by common but unknown laws and that differentiate themselves by their context.
1 code implementation • 26 May 2022 • Minsu Kim, Junyoung Park, Jinkyoo Park
Deep reinforcement learning (DRL)-based combinatorial optimization (CO) methods (i. e., DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is capable of learning CO solvers less relying on problem-specific expert domain knowledge (heuristic method) and supervised labeled data (supervised learning method).
no code implementations • 29 Sep 2021 • Junyoung Park, Fangying Chen, Jinkyoo Park
We show that our model is able to outperform other baseline methods for most of the datasets.
no code implementations • 29 Sep 2021 • Junyoung Park, Chihyeon Song, Jinkyoo Park
On the physical heat diffusion, we further apply ICGNN to solve a design optimization problem, which seeks to find the optimal heater allocations while considering the optimal operation of the heaters, by using a gradient-based method.
no code implementations • 30 Aug 2021 • Yeseul Kim, Kyung Hwan Kim, Junyoung Park, Hong In Yoon, Wonmo Sung
Total 10, 4, and 13 features were extracted for best performing one-year survival/progression status RFC models and RSF model via the recursive feature elimination process.
no code implementations • 22 Jun 2021 • Michael Poli, Stefano Massaroli, Clayton M. Rabideau, Junyoung Park, Atsushi Yamashita, Hajime Asama, Jinkyoo Park
We introduce the framework of continuous-depth graph neural networks (GNNs).
no code implementations • 6 Jun 2021 • Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park
We propose ScheduleNet, a RL-based real-time scheduler, that can solve various types of multi-agent scheduling problems.
1 code implementation • ICLR 2022 • Junyoung Park, Jinhyun Choo, Jinkyoo Park
We propose the convergent graph solver (CGS), a deep learning method that learns iterative mappings to predict the properties of a graph system at its stationary state (fixed point) with guaranteed convergence.
no code implementations • 2 Jun 2021 • Junyoung Park, Jaehyeong Chun, Sang Hun Kim, Youngkook Kim, Jinkyoo Park
In solving the formulated problem, the proposed framework employs a GNN to learn that node features that embed the spatial structure of the JSSP represented as a graph (representation learning) and derive the optimum scheduling policy that maps the embedded node features to the best scheduling action (policy learning).
no code implementations • 1 Jan 2021 • Junyoung Park, Sanzhar Bakhtiyarov, Jinkyoo Park
From the RL perspective, Minmax mTSP raises several significant challenges, such as the cooperation of multiple workers and the need for a well-engineered reward function.
no code implementations • 8 Jun 2020 • Jin-Hwa Kim, Junyoung Park, Yongseok Choi
To validate our method, we experiment on meta-transfer learning and few-shot learning tasks for multiple settings.
1 code implementation • 18 Nov 2019 • Michael Poli, Stefano Massaroli, Junyoung Park, Atsushi Yamashita, Hajime Asama, Jinkyoo Park
We introduce the framework of continuous--depth graph neural networks (GNNs).
no code implementations • ICLR 2020 • Yongseok Choi, Junyoung Park, Subin Yi, Dong-Yeon Cho
Although few-shot learning research has advanced rapidly with the help of meta-learning, its practical usefulness is still limited because most of them assumed that all meta-training and meta-testing examples came from a single domain.
no code implementations • 5 Jun 2019 • Junyoung Park, Subin Yi, Yongseok Choi, Dong-Yeon Cho, Jiwon Kim
Metric-based few-shot learning methods try to overcome the difficulty due to the lack of training examples by learning embedding to make comparison easy.