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Found 6 papers, 1 papers with code
Scalable Bayesian Learning with posteriors
Although theoretically compelling, Bayesian learning with modern machine learning models is computationally challenging since it requires approximating a high dimensional posterior distribution.
Thermodynamic Natural Gradient Descent
Second-order training methods have better convergence properties than gradient descent but are rarely used in practice for large-scale training due to their computational overhead.
Thermodynamic Computing System for AI Applications
Recent breakthroughs in artificial intelligence (AI) algorithms have highlighted the need for novel computing hardware in order to truly unlock the potential for AI.
Exploiting Inductive Biases in Video Modeling through Neural CDEs
We introduce a novel approach to video modeling that leverages controlled differential equations (CDEs) to address key challenges in video tasks, notably video interpolation and mask propagation.
Thermodynamic AI and the fluctuation frontier
Hence, we propose a novel computing paradigm, where software and hardware become inseparable.
Continuous-time dynamics and error scaling of noisy highly-entangling quantum circuits
We investigate the continuous-time dynamics of highly-entangling intermediate-scale quantum circuits in the presence of dissipation and decoherence.
Quantum Physics
