no code implementations • 6 Apr 2024 • Tianle Pu, Changjun Fan, Mutian Shen, Yizhou Lu, Li Zeng, Zohar Nussinov, Chao Chen, Zhong Liu
The technique is originated from physics, but is very effective in enabling RL agents to explore to continuously improve the solutions during test.
no code implementations • 18 Jul 2020 • Raj Kishore, Zohar Nussinov, Kisor Kumar Sahu
Unsupervised machine learning methods can be of great help in many traditional engineering disciplines, where huge amount of labeled data is not readily available or is extremely difficult or costly to generate.
no code implementations • 5 Feb 2020 • Brendon Lutnick, Wen Dong, Zohar Nussinov, Pinaki Sarder
We propose a fast statistical down-sampling of input image pixels based on the respective color features, and a new iterative method to minimize the Potts model energy considering pixel to segment relationship.
no code implementations • 2 Jan 2020 • Raj Kishore, S. Swayamjyoti, Shreeja Das, Ajay K. Gogineni, Zohar Nussinov, D. Solenov, Kisor K. Sahu
In the current work, we aim to better understand the generic intuition underlying unsupervised ML with a focus on physical systems.
no code implementations • 18 Aug 2017 • Patrick Chao, Tahereh Mazaheri, Bo Sun, Nicholas B. Weingartner, Zohar Nussinov
We introduce a statistical physics inspired supervised machine learning algorithm for classification and regression problems.
no code implementations • 27 Jun 2014 • Bo Sun, Blake Leonard, Peter Ronhovde, Zohar Nussinov
The {\it ensemble} of replicas evolves as to maximize the inter-replica correlation while simultaneously minimize the local intra-replica cost function (e. g., the total path length in the Traveling Salesman Problem within each replica).
no code implementations • 23 Aug 2012 • Dandan Hu, Pinaki Sarder, Peter Ronhovde, Sandra Orthaus, Samuel Achilefu, Zohar Nussinov
The proposed MCD method outperformed a popular spectral clustering based method in performing FLIM image segmentation.