66 citations · 83 across the 4 of their papers we have counts for
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cs.LG2022★ 6 cited
Learning GFlowNets from partial episodes for improved convergence and stability
Kanika Madan, Jarrid Rector-Brooks, Maksym Korablyov +6
Generative flow networks (GFlowNets) are a family of algorithms for training a sequential sampler of discrete objects under an unnormalized target density and have been successfull…
cs.LG2017★ 66 cited
Learning to Compute Word Embeddings On the Fly
Dzmitry Bahdanau, Tom Bosc, Stanisław Jastrzębski +3
Words in natural language follow a Zipfian distribution whereby some words are frequent but most are rare. Learning representations for words in the "long tail" of this distributio…
cs.LG2016★ 6 cited
Learning to Learn Neural Networks
Tom Bosc
Meta-learning consists in learning learning algorithms. We use a Long Short Term Memory (LSTM) based network to learn to compute on-line updates of the parameters of another neural…