| Data-Driven Offline Optimization for Architecting Hardware Accelerators | 0 | 0.34 | 2022 |
| No MCMC for me: Amortized sampling for fast and stable training of energy-based models | 0 | 0.34 | 2021 |
| Oops I Took A Gradient: Scalable Sampling For Discrete Distributions | 0 | 0.34 | 2021 |
| An Imitation Learning Approach for Cache Replacement | 0 | 0.34 | 2020 |
| Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples | 1 | 0.35 | 2020 |
| Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach | 0 | 0.34 | 2020 |
| Neural Execution Engines: Learning to Execute Subroutines | 0 | 0.34 | 2020 |
| Big Self-Supervised Models are Strong Semi-Supervised Learners | 0 | 0.34 | 2020 |
| Amortized Bayesian Optimization over Discrete Spaces. | 0 | 0.34 | 2020 |
| Your classifier is secretly an energy based model and you should treat it like one | 0 | 0.34 | 2020 |
| Graph Normalizing Flows. | 0 | 0.34 | 2019 |
| Neural Networks for Modeling Source Code Edits. | 0 | 0.34 | 2019 |
| Learning Execution through Neural Code Fusion. | 0 | 0.34 | 2019 |
| Flexibly Fair Representation Learning by Disentanglement. | 5 | 0.39 | 2019 |
| Meta-Learning for Semi-Supervised Few-Shot Classification. | 18 | 0.65 | 2018 |
| Learning Memory Access Patterns. | 4 | 0.48 | 2018 |
| Prototypical Networks for Few-shot Learning. | 196 | 4.09 | 2017 |
| An online sequence-to-sequence model for noisy speech recognition. | 1 | 0.37 | 2017 |
| Taking the Human Out of the Loop: A Review of Bayesian Optimization | 251 | 8.32 | 2016 |
| Generative Moment Matching Networks. | 111 | 7.94 | 2015 |
| Scalable Bayesian Optimization Using Deep Neural Networks | 76 | 2.92 | 2015 |
| The Variational Fair Autoencoder. | 2 | 0.37 | 2015 |
| Predicting Deep Zero-Shot Convolutional Neural Networks using Textual Descriptions | 75 | 2.01 | 2015 |
| Learning unbiased features. | 1 | 0.42 | 2014 |
| Input Warping for Bayesian Optimization of Non-Stationary Functions. | 35 | 2.23 | 2014 |
| Freeze-Thaw Bayesian Optimization. | 30 | 1.18 | 2014 |
| Multi-Task Bayesian Optimization. | 96 | 3.71 | 2013 |
| Learning Fair Representations. | 22 | 1.48 | 2013 |
| Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. | 17 | 0.79 | 2013 |
| Cardinality Restricted Boltzmann Machines. | 14 | 0.59 | 2012 |
| Estimating the Hessian by Back-propagating Curvature. | 9 | 1.25 | 2012 |
| Probabilistic n-Choose-k Models for Classification and Ranking. | 4 | 0.39 | 2012 |
| Fast Exact Inference for Recursive Cardinality Models | 28 | 1.10 | 2012 |
| Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults. | 1 | 0.37 | 2012 |
| On Autoencoders and Score Matching for Energy Based Models. | 38 | 2.61 | 2011 |
| Inductive Principles for Restricted Boltzmann Machine Learning | 54 | 2.25 | 2010 |
| Inductive Principles for Restricted Boltzmann Machine Learning | 0 | 0.34 | 2010 |
| A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets | 29 | 1.49 | 2010 |
