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Found 7 papers, 1 papers with code
Random Relabeling for Efficient Machine Unlearning
Learning algorithms and data are the driving forces for machine learning to bring about tremendous transformation of industrial intelligence.
Energy-based Generative Models for Target-specific Drug Discovery
Drug targets are the main focus of drug discovery due to their key role in disease pathogenesis.
Analysis of Power-Oriented Fault Injection Attacks on Spiking Neural Networks
We investigate the impact of the attacks on digit classification tasks and find that in the worst-case scenario, classification accuracy is reduced by 85. 65%.
Scalable Variational Quantum Circuits for Autoencoder-based Drug Discovery
The architectural strategies in hybrid quantum classical networks such as, adjustable quantum layer depth, heterogeneous learning rates, and patched quantum circuits are proposed to learn high dimensional dataset such as, ligand-targeted drugs.
Drug Discovery Approaches using Quantum Machine Learning
Traditional drug discovery pipeline takes several years and cost billions of dollars.
Quantum Generative Models for Small Molecule Drug Discovery
We propose a qubit-efficient quantum GAN with a hybrid generator (QGAN-HG) to learn richer representation of molecules via searching exponentially large chemical space with few qubits more efficiently than classical GAN.
Quantum-soft QUBO Suppression for Accurate Object Detection
Non-maximum suppression (NMS) has been adopted by default for removing redundant object detections for decades.
