| Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning. | 0 | 0.34 | 2022 |
| Uniform Priors for Data-Efficient Learning. | 0 | 0.34 | 2022 |
| Fortuitous Forgetting in Connectionist Networks. | 0 | 0.34 | 2022 |
| Learning to Combine Per-Example Solutions for Neural Program Synthesis. | 0 | 0.34 | 2021 |
| A Universal Representation Transformer Layer for Few-Shot Image Classification | 0 | 0.34 | 2021 |
| A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches. | 0 | 0.34 | 2021 |
| Improving Reproducibility In Machine Learning Research (A Report From The Neurips 2019 Reproducibility Program) | 0 | 0.34 | 2021 |
| Impact of Aliasing on Generalization in Deep Convolutional Networks. | 0 | 0.34 | 2021 |
| Learning a Universal Template for Few-shot Dataset Generalization | 0 | 0.34 | 2021 |
| Dibs: Diversity Inducing Information Bottleneck In Model Ensembles | 0 | 0.34 | 2021 |
| Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples | 1 | 0.35 | 2020 |
| Learning Graph Structure With A Finite-State Automaton Layer | 0 | 0.34 | 2020 |
| Revisiting Fundamentals of Experience Replay | 0 | 0.34 | 2020 |
| Algorithmic Improvements For Deep Reinforcement Learning Applied To Interactive Fiction | 0 | 0.34 | 2020 |
| Curriculum By Smoothing | 0 | 0.34 | 2020 |
| Language GANs Falling Short | 2 | 0.36 | 2020 |
| Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling | 0 | 0.34 | 2020 |
| Small-GAN: Speeding Up GAN Training Using Core-sets | 0 | 0.34 | 2020 |
| Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks | 0 | 0.34 | 2020 |
| Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification. | 1 | 0.35 | 2019 |
| The Hanabi Challenge: A New Frontier for AI Research. | 8 | 0.69 | 2019 |
| Recall Traces: Backtracking Models for Efficient Reinforcement Learning | 0 | 0.34 | 2019 |
| Hyperbolic Discounting and Learning over Multiple Horizons. | 0 | 0.34 | 2019 |
| InfoBot: Transfer and Exploration via the Information Bottleneck. | 4 | 0.38 | 2019 |
| A RAD approach to deep mixture models. | 0 | 0.34 | 2019 |
| Disentangling the independently controllable factors of variation by interacting with the world. | 2 | 0.36 | 2018 |
| Meta-Learning for Semi-Supervised Few-Shot Classification. | 18 | 0.65 | 2018 |
| Meta-Learning for Batch Mode Active Learning. | 1 | 0.35 | 2018 |
| Traffic Analytics With Low-Frame-Rate Videos. | 4 | 0.40 | 2018 |
| Blindfold Baselines for Embodied QA. | 1 | 0.35 | 2018 |
| Modulating early visual processing by language. | 29 | 0.92 | 2017 |
| Recurrent Mixture Density Network for Spatiotemporal Visual Attention. | 11 | 0.54 | 2017 |
| A Meta-Learning Perspective on Cold-Start Recommendations for Items. | 9 | 0.53 | 2017 |
| Movie Description. | 16 | 1.21 | 2017 |
| Multiscale sequence modeling with a learned dictionary. | 1 | 0.36 | 2017 |
| Document Neural Autoregressive Distribution Estimation. | 5 | 0.41 | 2017 |
| Optimization as a Model for Few-Shot Learning | 196 | 4.87 | 2017 |
| Learn to Track: Deep Learning for Tractography. | 3 | 0.40 | 2017 |
| Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. | 55 | 2.29 | 2017 |
| HoME: a Household Multimodal Environment. | 14 | 0.69 | 2017 |
| Neural Autoregressive Distribution Estimation. | 25 | 1.45 | 2016 |
| Hierarchical Memory Networks. | 7 | 0.46 | 2016 |
| MADE: Masked Autoencoder for Distribution Estimation. | 43 | 1.86 | 2015 |
| Domain-Adversarial Training of Neural Networks | 469 | 12.53 | 2015 |
| Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research. | 34 | 1.26 | 2015 |
| Within-brain classification for brain tumor segmentation. | 7 | 0.52 | 2015 |
| Guest Editorial: Deep Learning for Multimedia Computing. | 0 | 0.34 | 2015 |
| An Infinite Restricted Boltzmann Machine. | 3 | 0.38 | 2015 |
| Video Description Generation Incorporating Spatio-Temporal Features and a Soft-Attention Mechanism. | 22 | 1.09 | 2015 |
| Clustering is Efficient for Approximate Maximum Inner Product Search | 7 | 0.47 | 2015 |
