1 citations · 4 across the 4 of their papers we have counts for
3 papers · 1 filter
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…
On the generalization of learning algorithms that do not converge
Nisha Chandramoorthy, Andreas Loukas, Khashayar Gatmiry +1
Generalization analyses of deep learning typically assume that the training converges to a fixed point. But, recent results indicate that in practice, the weights of deep neural ne…
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
Nikolaos Karalias, Joshua Robinson, Andreas Loukas +1
Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects. But, discrete domains are (1) not naturally a…