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Understanding polysemanticity in neural networks through coding theory
Despite substantial efforts, neural network interpretability remains an elusive goal, with previous research failing to provide succinct explanations of most single neurons' impact on the network output.
Shadows of quantum machine learning
Quantum machine learning is often highlighted as one of the most promising uses for a quantum computer to solve practical problems.
High Dimensional Quantum Machine Learning With Small Quantum Computers
In an attempt to placate this limitation techniques can be applied for evaluating a quantum circuit using a machine with fewer qubits than the circuit naively requires.
Parametrized quantum policies for reinforcement learning
With the advent of real-world quantum computing, the idea that parametrized quantum computations can be used as hypothesis families in a quantum-classical machine learning system is gaining increasing traction.
