no code implementations • 1 Mar 2024 • Toki Tahmid Inan, Mingrui Liu, Amarda Shehu
Our investigation encompasses a wide array of techniques, including SGD and its variants, flat-minima optimizers, and new algorithms we propose under the Basin Hopping framework.
no code implementations • 4 Oct 2022 • Parastoo Kamranfar, David Lattanzi, Amarda Shehu, Daniel Barbará
The MIL-based formulation performs no worse than single instance learning on easy to moderate datasets and outperforms single-instance learning on more challenging datasets.
no code implementations • 1 Oct 2022 • Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, William Wuest, Amarda Shehu, Liang Zhao
Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design.
no code implementations • 17 Jun 2022 • Anowarul Kabir, Amarda Shehu
The increasing number of protein sequences decoded from genomes is opening up new avenues of research on linking protein sequence to function with transformer neural networks.
no code implementations • 28 Feb 2022 • Yuanqi Du, Xiaojie Guo, Amarda Shehu, Liang Zhao
Recent advances in deep graph generative models treat molecule design as graph generation problems which provide new opportunities toward the breakthrough of this long-lasting problem.
no code implementations • 4 Oct 2021 • Yuanjie Lu, Parastoo Kamranfar, David Lattanzi, Amarda Shehu
However, a key shortcoming of state-of-the-art methods is their inability to take into account information of various modalities, for instance the impact of maintenance downtime on traffic flows.
no code implementations • 20 Apr 2021 • Wanli Qiao, Amarda Shehu
The mean shift (MS) algorithm is a nonparametric method used to cluster sample points and find the local modes of kernel density estimates, using an idea based on iterative gradient ascent.
no code implementations • 3 Oct 2020 • Nasrin Akhter, Gopinath Chennupati, Hristo Djidjev, Amarda Shehu
Consensus methods show varied success in handling the challenge of decoy selection despite some issues associated with clustering large decoy sets and decoy sets that do not show much structural similarity.
1 code implementation • 9 Jun 2020 • Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, Yanfang Ye
Disentangled representation learning has recently attracted a significant amount of attention, particularly in the field of image representation learning.
1 code implementation • 8 Apr 2020 • Xiaojie Guo, Yuanqi Du, Sivani Tadepalli, Liang Zhao, Amarda Shehu
Much scientific enquiry across disciplines is founded upon a mechanistic treatment of dynamic systems that ties form to function.