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Representation Learning

2843 directly classified papers

Papers per year

Papers

Causal Feature Learning for Utility-Maximizing Agents PGM 2020
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness CVPR 2020
Self-Attentive Hawkes Process ICML 2020
Boosting Deep Face Recognition via Disentangling Appearance and Geometry WACV 2020
Looking deeper into Time for Activities of Daily Living Recognition WACV 2020
Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion WACV 2020
DropNet: Reducing Neural Network Complexity via Iterative Pruning ICML 2020
Fiedler Regularization: Learning Neural Networks with Graph Sparsity ICML 2020
Deep Gaussian Markov Random Fields ICML 2020
ControlVAE: Controllable Variational Autoencoder ICML 2020
Explorable Super Resolution CVPR 2020
Implicit Neural Representations with Periodic Activation Functions NIPS 2020
On the Theory of Transfer Learning: The Importance of Task Diversity NIPS 2020
Learning Invariants through Soft Unification NIPS 2020
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes NIPS 2020
Learning the Geometry of Wave-Based Imaging NIPS 2020
Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction NIPS 2020
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error NIPS 2020
Predictive Information Accelerates Learning in RL NIPS 2020
On Convergence of Nearest Neighbor Classifiers over Feature Transformations NIPS 2020
Learning Causal Effects via Weighted Empirical Risk Minimization NIPS 2020
A Biologically Plausible Neural Network for Slow Feature Analysis NIPS 2020
From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering NIPS 2020
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations NIPS 2020
Learning outside the Black-Box: The pursuit of interpretable models NIPS 2020