no code implementations • 26 Dec 2023 • Juyoung Yun
This research embarks on pioneering the integration of gradient sampling optimization techniques, particularly StochGradAdam, into the pruning process of neural networks.
no code implementations • 30 Nov 2023 • Juyoung Yun
In the field of deep learning, the prevalence of models initially trained with 32-bit precision is a testament to its robustness and accuracy.
no code implementations • 25 Oct 2023 • Juyoung Yun
In the rapidly advancing domain of deep learning optimization, this paper unveils the StochGradAdam optimizer, a novel adaptation of the well-regarded Adam algorithm.
no code implementations • 25 Aug 2023 • Juyoung Yun
This concept of "controlled confusion" within network activations is posited to foster more robust learning, particularly in contexts that necessitate discerning subtle patterns.
no code implementations • 30 Jul 2023 • Juyoung Yun
This not only disrupts the learning process but also poses significant challenges in deploying dependable models in real-world applications.
no code implementations • 20 Jun 2023 • Juyoung Yun
Through the exploitation of the G-NM potential, we strive to advance the state-of-the-art in large-scale time series forecasting models.
no code implementations • 18 May 2023 • Juyoung Yun, Byungkon Kang, Francois Rameau, Zhoulai Fu
Contrary to literature that credits the success of noise-tolerated neural networks to regularization effects, our study-supported by a series of rigorous experiments-provides a quantitative explanation of why standalone IEEE 16-bit floating-point neural networks can perform on par with 32-bit and mixed-precision networks in various image classification tasks.
no code implementations • 30 Jan 2023 • Juyoung Yun, Byungkon Kang, Zhoulai Fu
Lowering the precision of neural networks from the prevalent 32-bit precision has long been considered harmful to performance, despite the gain in space and time.
no code implementations • 17 Jan 2023 • Juyoung Yun
In the era of space exploration, the implications of space weather have become increasingly evident.