Particle swarm optimization

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Explores actor-critic networks and the advantage actor critic method for optimizing parameters to maximize return.

Explores challenges in brain region modeling using atlases and strategies to compensate for missing data and assumptions.

Focuses on assembling neural network building blocks and dealing with data sparseness using various strategies and assumptions.

Covers classical optimization problems, brute force algorithms, and integer linear optimization.

Covers the scaling of language models, focusing on training efficiency and deployment considerations.

Explores adaptive optimization methods that adapt locally and converge without knowing the smoothness constant.

Explores fractional factorial designs, from types to analysis and model building.

Covers efficient GPU-accelerated join optimization for complex queries, focusing on improving optimization times and heuristic plan quality.

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Explores memory-efficient adaptive optimization for large-scale learning and the challenges of memory overhead in training large models.