conftrace_

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
'10
'15
'20
'25

Papers (including subtopics)

DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling NIPS 2020
Hybrid Models for Learning to Branch NIPS 2020
The Convolution Exponential and Generalized Sylvester Flows NIPS 2020
Regularizing Towards Permutation Invariance In Recurrent Models NIPS 2020
GCN meets GPU: Decoupling “When to Sample” from “How to Sample” NIPS 2020
Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model NIPS 2020
Supervised Contrastive Learning NIPS 2020
Collegial Ensembles NIPS 2020
Modern Hopfield Networks and Attention for Immune Repertoire Classification NIPS 2020
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks NIPS 2020
The Origins and Prevalence of Texture Bias in Convolutional Neural Networks NIPS 2020
Time-Reversal Symmetric ODE Network NIPS 2020
Handling Missing Data with Graph Representation Learning NIPS 2020
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles NIPS 2020
Semialgebraic Optimization for Lipschitz Constants of ReLU Networks NIPS 2020
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings NIPS 2020
Untangling tradeoffs between recurrence and self-attention in artificial neural networks NIPS 2020
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks NIPS 2020
Parameterized Explainer for Graph Neural Network NIPS 2020
Recursive Inference for Variational Autoencoders NIPS 2020
What Do Neural Networks Learn When Trained With Random Labels? NIPS 2020
Universal Function Approximation on Graphs NIPS 2020
Graph Stochastic Neural Networks for Semi-supervised Learning NIPS 2020
Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks NIPS 2020
On the universality of deep learning NIPS 2020