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Found 5 papers, 2 papers with code
Logic Synthesis with Generative Deep Neural Networks
While deep learning has achieved significant success in various domains, its application to logic circuit design has been limited due to complex constraints and strict feasibility requirement.
Circuit Transformer: A Transformer That Preserves Logical Equivalence
In this study, we introduce a generative neural model, the "Circuit Transformer", which eliminates such wrong predictions and produces logic circuits strictly equivalent to given Boolean functions.
Self-consistent Gradient-like Eigen Decomposition in Solving Schrödinger Equations
The Schr\"odinger equation is at the heart of modern quantum mechanics.
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics Network
Traditional solutions on these problems leverage combinatorial optimization with demand and supply forecasting.
MEMD: A Diversity-Promoting Learning Framework for Short-Text Conversation
Neural encoder-decoder models have been widely applied to conversational response generation, which is a research hot spot in recent years.
