학술행사
- 발표자 최재성 박사(고등과학원)
- 개최일시 2024-11-14 14:00-16:00
- 장소 국가수리과학연구소 산업수학혁신센터(판교)
유튜브 스트리밍 예정입니다: https://www.youtube.com/live/2vbdfFuT064
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일시: 2024년 11월 14일(목), 14:00-16:00
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장소: 판교 테크노밸리 산업수학혁신센터 세미나실
- 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소
- 무료주차는 2시간 지원됩니다.
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발표자: 최재성 박사(고등과학원)
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주요내용: How reservoir computing differs from conventional neural networks and its potential applications
Reservoir computing emerged as a distinctive paradigm in handling temporal tasks. This framework significantly reduces trainable parameters compared to conventional neural networks while maintaining computational performance. Beyond its initial appeal for efficiency, it has gained attention for its physical implementation and unique applications in nonlinear dynamics-based computing. This presentation explores how reservoir computing opens new possibilities in specialized tasks, including associative memory for dynamics and unsupervised denoising, demonstrating its complementary role to conventional neural networks.
유튜브 스트리밍 예정입니다: https://www.youtube.com/live/2vbdfFuT064
-
일시: 2024년 11월 14일(목), 14:00-16:00
-
장소: 판교 테크노밸리 산업수학혁신센터 세미나실
- 경기 성남시 수정구 대왕판교로 815, 기업지원허브 231호 국가수리과학연구소
- 무료주차는 2시간 지원됩니다.
-
발표자: 최재성 박사(고등과학원)
-
주요내용: How reservoir computing differs from conventional neural networks and its potential applications
Reservoir computing emerged as a distinctive paradigm in handling temporal tasks. This framework significantly reduces trainable parameters compared to conventional neural networks while maintaining computational performance. Beyond its initial appeal for efficiency, it has gained attention for its physical implementation and unique applications in nonlinear dynamics-based computing. This presentation explores how reservoir computing opens new possibilities in specialized tasks, including associative memory for dynamics and unsupervised denoising, demonstrating its complementary role to conventional neural networks.
