10 citations · 12 across the 4 of their papers we have counts for
5 papers
How I learned to stop worrying and love StopGrads: Stationarity, Convergence, and a case study on Flow Map Learning
Max W. Shen, Mark Goldstein, Zichu Wang +2
Stopgrads are widely used in training machine learning models, but stopgrads can alter the gradient, stationary points and convergence guarantees of the original objective, which c…
Towards Understanding and Improving GFlowNet Training
Max W. Shen, Emmanuel Bengio, Ehsan Hajiramezanali +3
Generative flow networks (GFlowNets) are a family of algorithms that learn a generative policy to sample discrete objects with non-negative reward . Learning objectives g…
Hierarchically branched diffusion models leverage dataset structure for class-conditional generation
Alex M. Tseng, Max Shen, Tommaso Biancalani +1
Class-labeled datasets, particularly those common in scientific domains, are rife with internal structure, yet current class-conditional diffusion models ignore these relationships…
Conditional Diffusion with Less Explicit Guidance via Model Predictive Control
Max W. Shen, Ehsan Hajiramezanali, Gabriele Scalia +4
How much explicit guidance is necessary for conditional diffusion? We consider the problem of conditional sampling using an unconditional diffusion model and limited explicit guida…
Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient
Max W. Shen
The problem of human trust in artificial intelligence is one of the most fundamental problems in applied machine learning. Our processes for evaluating AI trustworthiness have subs…