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18 papers · 2 filters
Keeping it Simple: Language Models can learn Complex Molecular Distributions
Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik
Deep generative models of molecules have grown immensely in popularity, trained on relevant datasets, these models are used to search through chemical space. The downstream utility…
GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks
Vineeth S. Bhaskara, Tristan Aumentado-Armstrong, Allan Jepson +1
Modern generative adversarial networks (GANs) predominantly use piecewise linear activation functions in discriminators (or critics), including ReLU and LeakyReLU. Such models lear…
Decentralized Federated Learning through Proxy Model Sharing
Shivam Kalra, Junfeng Wen, Jesse C. Cresswell +2
Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework tha…
Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment
Matthew J. Filipovich, Zhimu Guo, Mohammed Al-Qadasi +6
There has been growing interest in using photonic processors for performing neural network inference operations; however, these networks are currently trained using standard digita…
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Alexandros Karargyris, Renato Umeton, Micah J. Sheller +39
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving prov…
Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models
Daniel Flam-Shepherd, Tony Wu, Xuemei Gu +3
Quantum physics experiments produce interesting phenomena such as interference or entanglement, which are core properties of numerous future quantum technologies. The complex relat…