| Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data. | 0 | 0.34 | 2022 |
| Theoretical bounds on estimation error for meta-learning | 0 | 0.34 | 2021 |
| Wandering Within a World: Online Contextualized Few-Shot Learning | 0 | 0.34 | 2021 |
| Environment Inference For Invariant Learning | 0 | 0.34 | 2021 |
| On Monotonic Linear Interpolation of Neural Network Parameters | 0 | 0.34 | 2021 |
| Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach | 0 | 0.34 | 2020 |
| Understanding the Limitations of Conditional Generative Models | 0 | 0.34 | 2020 |
| Fairness through Causal Awareness - Learning Causal Latent-Variable Models for Biased Data. | 4 | 0.39 | 2019 |
| Slang Generation as Categorization. | 0 | 0.34 | 2019 |
| Lorentzian Distance Learning for Hyperbolic Representations. | 1 | 0.35 | 2019 |
| LanczosNet: Multi-Scale Deep Graph Convolutional Networks. | 3 | 0.37 | 2019 |
| Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models. | 0 | 0.34 | 2019 |
| Efficient Graph Generation with Graph Recurrent Attention Networks | 0 | 0.34 | 2019 |
| High-Level Perceptual Similarity is Enabled by Learning Diverse Tasks. | 0 | 0.34 | 2019 |
| Aggregated Momentum: Stability Through Passive Damping | 0 | 0.34 | 2019 |
| Dimensionality Reduction for Representing the Knowledge of Probabilistic Models. | 1 | 0.35 | 2019 |
| Conditional Generative Models are not Robust. | 0 | 0.34 | 2019 |
| Leveraging Constraint Logic Programming for Neural Guided Program Synthesis. | 0 | 0.34 | 2018 |
| Graph Partition Neural Networks for Semi-Supervised Classification. | 3 | 0.38 | 2018 |
| Neural Relational Inference for Interacting Systems. | 33 | 0.81 | 2018 |
| Reviving and Improving Recurrent Back-Propagation. | 3 | 0.38 | 2018 |
| Predict Responsibly - Improving Fairness and Accuracy by Learning to Defer. | 0 | 0.34 | 2018 |
| Excessive Invariance Causes Adversarial Vulnerability. | 4 | 0.38 | 2018 |
| Learning Adversarially Fair and Transferable Representations. | 15 | 0.57 | 2018 |
| Predict Responsibly: Increasing Fairness by Learning To Defer. | 4 | 0.51 | 2018 |
| Meta-Learning for Semi-Supervised Few-Shot Classification. | 18 | 0.65 | 2018 |
| Neural Guided Constraint Logic Programming for Program Synthesis. | 1 | 0.36 | 2018 |
| Joint Embeddings of Scene Graphs and Images | 0 | 0.34 | 2017 |
| Prototypical Networks for Few-shot Learning. | 196 | 4.09 | 2017 |
| Dualing GANs. | 0 | 0.34 | 2017 |
| Deep Spectral Clustering Learning. | 14 | 0.56 | 2017 |
| End-to-End Instance Segmentation with Recurrent Attention | 35 | 1.15 | 2017 |
| Stochastic Segmentation Trees For Multiple Ground Truths | 1 | 0.35 | 2017 |
| Causal Effect Inference with Deep Latent-Variable Models. | 14 | 0.66 | 2017 |
| Gated Graph Sequence Neural Networks | 0 | 0.34 | 2016 |
| Learning Deep Parsimonious Representations. | 4 | 0.39 | 2016 |
| Understanding the Effective Receptive Field in Deep Convolutional Neural Networks. | 32 | 1.00 | 2016 |
| Generative Moment Matching Networks. | 111 | 7.94 | 2015 |
| Guest Editors’ Introduction: Special Section on Higher Order Graphical Models in Computer Vision | 1 | 0.35 | 2015 |
| Image Question Answering: A Visual Semantic Embedding Model and a New Dataset. | 34 | 1.61 | 2015 |
| Gated Graph Sequence Neural Networks | 66 | 1.38 | 2015 |
| Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models. | 273 | 22.38 | 2014 |
| Learning unbiased features. | 1 | 0.42 | 2014 |
| Input Warping for Bayesian Optimization of Non-Stationary Functions. | 35 | 2.23 | 2014 |
| New learning methods for supervised and unsupervised preference aggregation. | 13 | 0.70 | 2014 |
| A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data. | 11 | 0.57 | 2013 |
| Exploring Compositional High Order Pattern Potentials for Structured Output Learning | 19 | 0.86 | 2013 |
| Learning Fair Representations. | 22 | 1.48 | 2013 |
| CRF framework for supervised preference aggregation | 6 | 0.46 | 2013 |
| Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning. | 17 | 0.79 | 2013 |
