no code implementations • 26 Apr 2024 • Yoonsoo Nam, Nayara Fonseca, Seok Hyeong Lee, Ard Louis
Deep learning models can exhibit what appears to be a sudden ability to solve a new problem as training time ($T$), training data ($D$), or model size ($N$) increases, a phenomenon known as emergence.
no code implementations • 18 Sep 2023 • Yoonsoo Nam, Adam Lehavi, Daniel Yang, Digbalay Bose, Swabha Swayamdipta, Shrikanth Narayanan
Video summarization remains a huge challenge in computer vision due to the size of the input videos to be summarized.
no code implementations • 22 Oct 2021 • Yizhang Lou, Chris Mingard, Yoonsoo Nam, Soufiane Hayou
Recent work by Baratin et al. (2021) sheds light on an intriguing pattern that occurs during the training of deep neural networks: some layers align much more with data compared to other layers (where the alignment is defined as the euclidean product of the tangent features matrix and the data labels matrix).
no code implementations • 4 Sep 2020 • Heungseok Park, Yoonsoo Nam, Ji-Hoon Kim, Jaegul Choo
HyperTendril takes a novel approach to effectively steering hyperparameter optimization through an iterative, interactive tuning procedure that allows users to refine the search spaces and the configuration of the AutoML method based on their own insights from given results.