Benchmarks
Greatest papers with code
ShapeNet: An Information-Rich 3D Model Repository
We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects.
Adversarial Autoencoders
• eriklindernoren/PyTorch-GAN
• 
In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior distribution.
Ranked #4 on
Unsupervised Image Classification
on MNIST
DATA VISUALIZATION DIMENSIONALITY REDUCTION UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST VARIATIONAL INFERENCE
VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution
• uber-common/deep-neuroevolution
• 
Recent advances in deep neuroevolution have demonstrated that evolutionary algorithms, such as evolution strategies (ES) and genetic algorithms (GA), can scale to train deep neural networks to solve difficult reinforcement learning (RL) problems.
Torchbearer: A Model Fitting Library for PyTorch
We introduce torchbearer, a model fitting library for pytorch aimed at researchers working on deep learning or differentiable programming.
jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics
This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques.
Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks
Rapidly creating effective visualizations using expressive grammars is challenging for users who have limited time and limited skills in statistics and data visualization.
A high-bias, low-variance introduction to Machine Learning for physicists
The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists.
VizML: A Machine Learning Approach to Visualization Recommendation
Data visualization should be accessible for all analysts with data, not just the few with technical expertise.
Doubly Stochastic Neighbor Embedding on Spheres
To solve this problem, we introduce a fast normalization method and normalize the similarity matrix to be doubly stochastic such that all the data points have equal total similarities.
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
In this work, our main goal is to understand what aspects of DR methods are important for preserving both local and global structure: it is difficult to design a better method without a true understanding of the choices we make in our algorithms and their empirical impact on the lower-dimensional embeddings they produce.