Zobrazeno 1 - 10
of 7 427
pro vyhledávání: '"So, Minyoung"'
Autor:
Kim, Minjun, Jang, Ohtae, Song, Haekang, Shin, Heesub, Ok, Jaewoo, Back, Minyoung, Youn, Jaehyuk, Kim, Sungho
Synthetic aperture radar technology is crucial for high-resolution imaging under various conditions; however, the acquisition of real-world synthetic aperture radar data for deep learning-based automatic target recognition remains challenging due to
Externí odkaz:
http://arxiv.org/abs/2409.14060
While success in many robotics tasks can be determined by only observing the final state and how it differs from the initial state - e.g., if an apple is picked up - many tasks require observing the full motion of the robot to correctly determine suc
Externí odkaz:
http://arxiv.org/abs/2409.10683
We introduce an improved CNOT synthesis algorithm that considers nearest-neighbour interactions and CNOT gate error rates in noisy intermediate-scale quantum (NISQ) hardware. Compared to IBM's Qiskit compiler, it improves the fidelity of a synthesize
Externí odkaz:
http://arxiv.org/abs/2405.19891
To improve performance in contemporary deep learning, one is interested in scaling up the neural network in terms of both the number and the size of the layers. When ramping up the width of a single layer, graceful scaling of training has been linked
Externí odkaz:
http://arxiv.org/abs/2405.14813
We argue that representations in AI models, particularly deep networks, are converging. First, we survey many examples of convergence in the literature: over time and across multiple domains, the ways by which different neural networks represent data
Externí odkaz:
http://arxiv.org/abs/2405.07987
Collecting and labeling real datasets to train the person search networks not only requires a lot of time and effort, but also accompanies privacy issues. The weakly-supervised and unsupervised domain adaptation methods have been proposed to alleviat
Externí odkaz:
http://arxiv.org/abs/2404.00626
This paper explores the utilization of Temporal Graph Networks (TGN) for financial anomaly detection, a pressing need in the era of fintech and digitized financial transactions. We present a comprehensive framework that leverages TGN, capable of capt
Externí odkaz:
http://arxiv.org/abs/2404.00060
Autor:
Oh, Minyoung, Sim, Jae-Young
Lifelong person re-identification (LReID) assumes a practical scenario where the model is sequentially trained on continuously incoming datasets while alleviating the catastrophic forgetting in the old datasets. However, not only the training dataset
Externí odkaz:
http://arxiv.org/abs/2403.10022
The scalability of deep learning models is fundamentally limited by computing resources, memory, and communication. Although methods like low-rank adaptation (LoRA) have reduced the cost of model finetuning, its application in model pre-training rema
Externí odkaz:
http://arxiv.org/abs/2402.16828
Autor:
Park, Minyoung, Do, Mirae, Shin, YeonJae, Yoo, Jaeseok, Hong, Jongkwang, Kim, Joongrock, Lee, Chul
Advanced techniques using Neural Radiance Fields (NeRF), Signed Distance Fields (SDF), and Occupancy Fields have recently emerged as solutions for 3D indoor scene reconstruction. We introduce a novel two-phase learning approach, H2O-SDF, that discrim
Externí odkaz:
http://arxiv.org/abs/2402.08138