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Found 5 papers, 0 papers with code
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data
The proliferation of generative models, combined with pretraining on web-scale data, raises a timely question: what happens when these models are trained on their own generated outputs?
Bridging Associative Memory and Probabilistic Modeling
Based on the observation that associative memory's energy functions can be seen as probabilistic modeling's negative log likelihoods, we build a bridge between the two that enables useful flow of ideas in both directions.
Deceptive Alignment Monitoring
As the capabilities of large machine learning models continue to grow, and as the autonomy afforded to such models continues to expand, the spectre of a new adversary looms: the models themselves.
FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation
We present FACADE, a novel probabilistic and geometric framework designed for unsupervised mechanistic anomaly detection in deep neural networks.
The impact of varying electrical stimulation parameters on neuromuscular response
We need a platform that will allow us to determine the optimal parameter space for these systems.
