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Found 7 papers, 2 papers with code
On the Impossible Safety of Large AI Models
Large AI Models (LAIMs), of which large language models are the most prominent recent example, showcase some impressive performance.
Tournesol: A quest for a large, secure and trustworthy database of reliable human judgments
We outline the structure of the Tournesol database, the key features of the Tournesol platform and the main hurdles that must be overcome to make it a successful project.
Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)
We study Byzantine collaborative learning, where $n$ nodes seek to collectively learn from each others' local data.
Distributed Momentum for Byzantine-resilient Learning
Momentum is a variant of gradient descent that has been proposed for its benefits on convergence.
Genuinely Distributed Byzantine Machine Learning
The third, Minimum-Diameter Averaging (MDA), is a statistically-robust gradient aggregation rule whose goal is to tolerate Byzantine workers.
Fast and Robust Distributed Learning in High Dimension
Given $n$ workers, $f$ of which are arbitrary malicious (Byzantine) and $m=n-f$ are not, we prove that multi-Bulyan can ensure a strong form of Byzantine resilience, as well as an ${\frac{m}{n}}$ slowdown, compared to averaging, the fastest (but non Byzantine resilient) rule for distributed machine learning.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
The Probabilistic Fault Tolerance of Neural Networks in the Continuous Limit
We study fault tolerance of neural networks subject to small random neuron/weight crash failures in a probabilistic setting.
