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Architectures

27,448 papers (33 classified directly here)

Subtopics

Neural Networks (11,723) Transformers (10,397) Graph Neural Networks (4,522) Autoencoders (1,154) Convolutional Neural Networks (973) Recurrent Neural Networks (593) Generative Adversarial Networks (18) Encoder-Decoder (14)

Papers per year

27
213
2915
3174
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Papers (including subtopics)

Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently NIPS 2019
Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks NIPS 2019
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder NIPS 2019
Controllable Text-to-Image Generation NIPS 2019
Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces NIPS 2019
Graph Normalizing Flows NIPS 2019
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction NIPS 2019
Neural networks grown and self-organized by noise NIPS 2019
HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs NIPS 2019
Gradient Dynamics of Shallow Univariate ReLU Networks NIPS 2019
Chirality Nets for Human Pose Regression NIPS 2019
Augmented Neural ODEs NIPS 2019
Regularizing Trajectory Optimization with Denoising Autoencoders NIPS 2019
Diffusion Improves Graph Learning NIPS 2019
A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks NIPS 2019
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective NIPS 2019
Hamiltonian Neural Networks NIPS 2019
Unconstrained Monotonic Neural Networks NIPS 2019
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. NIPS 2019
Convolution with even-sized kernels and symmetric padding NIPS 2019
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks NIPS 2019
Are Sixteen Heads Really Better than One? NIPS 2019
Stacked Capsule Autoencoders NIPS 2019
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules NIPS 2019
A coupled autoencoder approach for multi-modal analysis of cell types NIPS 2019