WebMay 17, 2024 · Generative Flow Networks (GFlowNets) are a machine-learning technique for generating compositional objects at a frequency proportional to their associated reward. In this article, we are going to unpack what all those words mean, outline why GFlowNets are useful, talk about how they are trained, and then we’ll dissect a TensorFlow 2 … WebRobust Scheduling with GFlowNets . Finding the best way to schedule operations in a computation graph is a classical NP-hard problem which is central to compiler optimization. However, evaluating the goodness of a schedule on the target hardware can be very time-consuming. Traditional approaches as well as previous machine learning ones ...
ROBUST SCHEDULING WITH GFLOWNETS
WebMar 2, 2024 · This work introduces a technique to control the trade-off between diversity and goodness of the proposed schedules at inference time and shows that conditioning the … http://arxiv-export3.library.cornell.edu/pdf/2203.04115v1 all screen monitor
Generative Flow Networks - Yoshua Bengio
WebOct 22, 2024 · ABSTRACT: Generative Flow Networks (or GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, with a training objective that makes them approximately sample in proportion to a given reward function. We show a number of additional theoretical properties of GFlowNets. WebI am excited to announce that our paper, "Robust Scheduling with GFlowNets", has been accepted at #ICLR2024 🎉 This work is the final result of David Zhang's summer internship #ICLR2024 🎉 This WebGFlowNets (Bengio et al.,2024a) provide a way to learn such a stochastic policy, and unlike Markov chain Monte Carlo (MCMC) methods (which also have this ability) amor-tizes the cost of each new i.i.d. sample (which may require a lengthy chain, with MCMC methods) into the cost of train-ing the generative model. As such, this paper is motivated by all scrips17.1