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Found 5 papers, 1 papers with code
Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks
To enable an ethical and legal use of machine learning algorithms, they must both be fair and protect the privacy of those whose data are being used.
Flatland-RL : Multi-Agent Reinforcement Learning on Trains
In order to probe the potential of Machine Learning (ML) research on Flatland, we (1) ran a first series of RL and IL experiments and (2) design and executed a public Benchmark at NeurIPS 2020 to engage a large community of researchers to work on this problem.
Meta-learnt priors slow down catastrophic forgetting in neural networks
Current training regimes for deep learning usually involve exposure to a single task / dataset at a time.
The Temporal Singularity: time-accelerated simulated civilizations and their implications
Provided significant future progress in artificial intelligence and computing, it may ultimately be possible to create multiple Artificial General Intelligences (AGIs), and possibly entire societies living within simulated environments.
Denoising Autoencoders for Overgeneralization in Neural Networks
Despite the recent developments that allowed neural networks to achieve impressive performance on a variety of applications, these models are intrinsically affected by the problem of overgeneralization, due to their partitioning of the full input space into the fixed set of target classes used during training.
