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Representation Learning

4,925 papers

Papers per year

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Papers

Statistically and Computationally Efficient Linear Meta-representation Learning NIPS 2021
Partition and Code: learning how to compress graphs NIPS 2021
EIGNN: Efficient Infinite-Depth Graph Neural Networks NIPS 2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise NIPS 2021
Compressive Visual Representations NIPS 2021
Convergence and Alignment of Gradient Descent with Random Backpropagation Weights NIPS 2021
Learning Graph Cellular Automata NIPS 2021
End-to-end reconstruction meets data-driven regularization for inverse problems NIPS 2021
Geometry Processing with Neural Fields NIPS 2021
Learning Disentangled Behavior Embeddings NIPS 2021
Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization NIPS 2021
Intriguing Properties of Vision Transformers NIPS 2021
Adaptive Diffusion in Graph Neural Networks NIPS 2021
Learning State Representations from Random Deep Action-conditional Predictions NIPS 2021
A Framework to Learn with Interpretation NIPS 2021
One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective NIPS 2021
Recognizing Vector Graphics without Rasterization NIPS 2021
Domain Adaptation with Invariant Representation Learning: What Transformations to Learn? NIPS 2021
Learning Debiased Representation via Disentangled Feature Augmentation NIPS 2021
Adversarial Training Helps Transfer Learning via Better Representations NIPS 2021
Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning NIPS 2021
Set Prediction in the Latent Space NIPS 2021
Which Mutual-Information Representation Learning Objectives are Sufficient for Control? NIPS 2021
Pragmatic Image Compression for Human-in-the-Loop Decision-Making NIPS 2021
Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows NIPS 2021