1 code implementation • 5 Mar 2025 • Wonjun Kang, Kevin Galim, Yuchen Zeng, Minjae Lee, Hyung Il Koo, Nam Ik Cho
At every timestep, our method directly affects the state at the current step, leading to more effective adaptation.
no code implementations • 28 Dec 2024 • Hyucksung Kwon, Kyungmo Koo, Janghyeon Kim, Woongkyu Lee, Minjae Lee, Hyungdeok Lee, Yousub Jung, JaeHan Park, Yosub Song, Byeongsu Yang, Haerang Choi, Guhyun Kim, Jongsoon Won, Woojae Shin, Changhyun Kim, Gyeongcheol Shin, Yongkee Kwon, Ilkon Kim, Euicheol Lim, John Kim, Jungwook Choi
Processing-in-Memory (PIM) maximizes memory bandwidth by moving compute to the data and can address the memory bandwidth challenges; however, PIM is not necessarily scalable to accelerate long-context LLM because of limited per-module memory capacity and the inflexibility of fixed-functional unit PIM architecture and static memory management.
no code implementations • 25 Aug 2024 • Seongmin Park, Minjae Lee, Junwon Choi, Jungwook Choi
To facilitate actual acceleration with this novel convolution approach, we designed SPADE+ as a cost-efficient augmentation to existing embedded sparse convolution accelerators.
1 code implementation • CVPR 2024 • Minhyuk Seo, Hyunseo Koh, Wonje Jeung, Minjae Lee, San Kim, Hankook Lee, Sungjun Cho, Sungik Choi, Hyunwoo Kim, Jonghyun Choi
Online continual learning suffers from an underfitted solution due to insufficient training for prompt model update (e. g., single-epoch training).
no code implementations • 16 Mar 2024 • Minhyuk Seo, Seongwon Cho, Minjae Lee, Diganta Misra, Hyeonbeom Choi, Seon Joo Kim, Jonghyun Choi
Requiring extensive human supervision is often impractical for continual learning due to its cost, leading to the emergence of 'name-only continual learning' that only provides the name of new concepts (e. g., classes) without providing supervised samples.
no code implementations • 26 Sep 2023 • Hee-Soo Heo, Kihyun Nam, Bong-Jin Lee, Youngki Kwon, Minjae Lee, You Jin Kim, Joon Son Chung
In the field of speaker verification, session or channel variability poses a significant challenge.
1 code implementation • 18 Jul 2023 • Minjae Lee, KyungMin Kim, Taesoo Kim, Sangdon Park
$\texttt{SGen}^{\texttt{Sup}}$, a direct modification of the selective prediction, is a supervised learning algorithm which exploits entailment-labeled data, annotated by humans.
no code implementations • 12 Jun 2023 • Hyeondey Kim, Jinwoo Nam, Minjae Lee, Yun Jegal, Kyungwoo Song
To do so, knowledge tracing systems should trace the knowledge state of the students by utilizing their problem-solving history and knowledge about the problems.
no code implementations • 1 Jun 2023 • Jee-weon Jung, Soonshin Seo, Hee-Soo Heo, Geonmin Kim, You Jin Kim, Young-ki Kwon, Minjae Lee, Bong-Jin Lee
The task of speaker change detection (SCD), which detects points where speakers change in an input, is essential for several applications.
no code implementations • 12 May 2023 • Minjae Lee, Seongmin Park, Hyungmin Kim, Minyong Yoon, Janghwan Lee, Jun Won Choi, Nam Sung Kim, Mingu Kang, Jungwook Choi
3D object detection using point cloud (PC) data is essential for perception pipelines of autonomous driving, where efficient encoding is key to meeting stringent resource and latency requirements.
no code implementations • 8 Aug 2022 • Doyub Kim, Minjae Lee, Ken Museth
We introduce NeuralVDB, which improves on an existing industry standard for efficient storage of sparse volumetric data, denoted VDB [Museth 2013], by leveraging recent advancements in machine learning.
no code implementations • NeurIPS 2017 • Kyuhong Shim, Minjae Lee, Iksoo Choi, Yoonho Boo, Wonyong Sung
The approximate probability of each word can be estimated with only a small part of the weight matrix by using a few large singular values and the corresponding elements for most of the words.
no code implementations • 30 Sep 2016 • Minjae Lee, Kyuyeon Hwang, Jinhwan Park, Sungwook Choi, Sungho Shin, Wonyong Sung
The weights are quantized to 6 bits to store all of them in the on-chip memory of an FPGA.
no code implementations • 30 Dec 2015 • Kyuyeon Hwang, Minjae Lee, Wonyong Sung
In this paper, we propose a context-aware keyword spotting model employing a character-level recurrent neural network (RNN) for spoken term detection in continuous speech.