Search Results for author:
Found 5 papers, 3 papers with code
Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search
In the past few years, Differentiable Neural Architecture Search (DNAS) rapidly imposed itself as the trending approach to automate the discovery of deep neural network architectures.
NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese Networks
In this article, we present NASiam, a novel approach that uses for the first time differentiable NAS to improve the multilayer perceptron projector and predictor (encoder/predictor pair) architectures inside siamese-networks-based contrastive learning frameworks (e. g., SimCLR, SimSiam, and MoCo) while preserving the simplicity of previous baselines.
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Values
This study proposes a novel approach to explain cooperative strategies in multiagent RL using Shapley values, a game theory concept used in XAI that successfully explains the rationale behind decisions taken by Machine Learning algorithms.
D-DARTS: Distributed Differentiable Architecture Search
This approach is accompanied by a novel metric that measures the distance between architectures inside our custom search space.
Explainability in Deep Reinforcement Learning
A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature relevance techniques to explain a deep neural network (DNN) output or explaining models that ingest image source data.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+2
