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Found 10 papers, 2 papers with code
A Study of AI Population Dynamics with Million-agent Reinforcement Learning
We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning.
Scalable Relaxations of Sparse Packing Constraints: Optimal Biocontrol in Predator-Prey Network
Cascades represent rapid changes in networks.
Deep Reasoning Networks: Thinking Fast and Slow
At a high level, DRNets encode a structured latent space of the input data, which is constrained to adhere to prior knowledge by a reasoning module.
Deep Reasoning Networks: Thinking Fast and Slow, for Pattern De-mixing
We introduce Deep Reasoning Networks (DRNets), an end-to-end framework that combines deep learning with reasoning for solving pattern de-mixing problems, typically in an unsupervised or weakly-supervised setting.
VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images
Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel-level by a human-machine hybrid algorithm (https://osf. io/nqjyw/, https://osf. io/t98fz/).
Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems
There has been an increasing interest in harnessing deep learning to tackle combinatorial optimization (CO) problems in recent years.
Fairness of Exposure in Stochastic Bandits
Contextual bandit algorithms have become widely used for recommendation in online systems (e. g. marketplaces, music streaming, news), where they now wield substantial influence on which items get exposed to the users.
Automating Crystal-Structure Phase Mapping: Combining Deep Learning with Constraint Reasoning
DRNets compensate for the limited data by exploiting and magnifying the rich prior knowledge about the thermodynamic rules governing the mixtures of crystals with constraint reasoning seamlessly integrated into neural network optimization.
Unsupervised Learning for Solving the Travelling Salesman Problem
Our loss function consists of two parts: one pushes the model to find the shortest path and the other serves as a surrogate for the constraint that the route should form a Hamiltonian Cycle.
Deep Reasoning Networks for Unsupervised Pattern De-mixing with Constraint Reasoning
We introduce Deep Reasoning Networks (DRNets), an end-to-end framework that combines deep learning with constraint reasoning for solving pattern de-mixing problems, typically in an unsupervised or very-weakly-supervised setting.
