
Innovative extension of deep solver for FBSDEs with jumps, using neural networks to tackle high-dimensional problems in option pricing and counterparty credit risk.

Explore innovative approaches to solve high-dimensional optimal control problems using Tensor Train approximation, with applications to destabilized viscous Burgers and diffusion equations.

Explore causal behavioral modeling and discrete choice models for consumer demand. Learn about heterogeneity, market simulation, and applications in marketing science, with insights on neural network representations.

Karl Friston explores the connection between perceptual inference and decision-making in cognitive neuroscience, examining whether these seemingly different processes share underlying information theoretic principles.

Explore how songbirds develop neural sequences for vocalizations, examining the role of auditory memory in shaping motor circuits and facilitating song learning through reinforcement mechanisms.

Explore reinforcement learning theory using predictive maps, bridging model-based and model-free approaches. Discover insights into hippocampal spatial representation and entorhinal grid cells.

Explore the neural circuitry of basal ganglia and its role in reinforcement learning, focusing on representation, coding, and computation in neural networks.

Explore the fusion of AI and scientific computing for predicting and controlling complex fluid systems, with insights on novel algorithms and the importance of interdisciplinary expertise.

Explore machine learning applications in gravitational wave detector science, focusing on noise subtraction and advanced control systems to enhance sensitivity and robustness in astrophysical research.

Exploring computational approaches to understand impaired motivation and effort in psychosis, focusing on reinforcement learning and decision-making to aid treatment development.

Explore decision-making and learning in life sciences, focusing on advanced concepts and their applications in biological systems and research.

Explore deep learning applications in fluid mechanics, including flow prediction and control using CNNs, autoencoders, and GANs. Learn about non-intrusive sensing and transfer learning in turbulent flows.

Explore reinforcement learning applications in Bayesian estimation for noise-driven coherent rotation of spin qubits, enhancing understanding of quantum systems and control.

Explore reinforcement learning techniques for disentangling quantum states using partial observations, enhancing understanding of complex quantum systems.

Explore advanced reinforcement learning techniques applied to quantum physics, focusing on cutting-edge research and practical applications in the field.

Explore advanced reinforcement learning techniques applied to quantum physics, focusing on cutting-edge research and practical applications in the field.

Explore reinforcement learning applications in quantum physics, covering fundamental concepts and cutting-edge research in this interdisciplinary field.

Explore how Large Language Models enhance drug discovery through molecular simulation, reaction prediction, and database interaction for individual disease treatments.

Explore protein design workflows using reinforcement learning and preference optimization techniques for advanced molecular engineering.

Explore Fokker-Planck-Inverse Reinforcement Learning for revealing agent behavior rationale. Learn about a novel physics-aware algorithm inferring transition and reward functions from observed trajectories.

Explore deploying deep learning services at the edge using NVIDIA Jetson and IBM technologies, covering setup, development, and practical applications in machine learning and AI.