8 citations · 12 across the 6 of their papers we have counts for
8 papers
High-order entropy-stable isothermal wall boundary condition for the compressible Navier--Stokes equations
Mohammed Sayyari, Nail K. Yamaleev
Entropy-stable boundary conditions are critical for ensuring that the corresponding numerical scheme satisfies the discrete entropy inequality, thus mimicking the second law of the…
Keep-or-Drop? Adaptive Tokenizer for Compact Video Representation
Yeonkyeong Lee, Hyunsung Go, Jongmin Kim +2
Latent diffusion models have emerged as a dominant framework for high-fidelity image and video synthesis, operating in compact latent spaces with variational autoencoders (VAEs) to…
Slot-MLLM: Object-Centric Visual Tokenization for Multimodal LLM
Donghwan Chi, Hyomin Kim, Yoonjin Oh +7
Recently, multimodal large language models (MLLMs) have emerged as a key approach in achieving artificial general intelligence. In particular, vision-language MLLMs have been devel…
MAGVLT: Masked Generative Vision-and-Language Transformer
Sungwoong Kim, Daejin Jo, Donghoon Lee +1
While generative modeling on multimodal image-text data has been actively developed with large-scale paired datasets, there have been limited attempts to generate both image and te…
LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward
Daejin Jo, Sungwoong Kim, Daniel Wontae Nam +4
Episodic count has been widely used to design a simple yet effective intrinsic motivation for reinforcement learning with a sparse reward. However, the use of episodic count in a h…
Insights From the NeurIPS 2021 NetHack Challenge
Eric Hambro, Sharada Mohanty, Dmitrii Babaev +26
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge. Participants were tasked with developing a program or agent that can win (i.e., 'ascend' i…
Automated Learning Rate Scheduler for Large-batch Training
Chiheon Kim, Saehoon Kim, Jongmin Kim +2
Large-batch training has been essential in leveraging large-scale datasets and models in deep learning. While it is computationally beneficial to use large batch sizes, it often re…
Advanced Policies: A First-Principles Path from Policy Gradient to Q-Learning
Donghoon Lee
We apply entropy to reinforcement learning in two ways: entropy-augmented reward defines the soft objective, while relative-entropy regularization controls the size of a policy-imp…