1
Facial expressions of emotion states and their neuronal correlates in mice
2
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
3
PyTorch: An Imperative Style, High-Performance Deep Learning Library
4
Crowd-sourcing materials-science challenges with the NOMAD 2018 Kaggle competition
6
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
7
FCHL revisited: Faster and more accurate quantum machine learning.
8
Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs
9
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks.
10
IMPRESSION – prediction of NMR parameters for 3-dimensional chemical structures using machine learning with near quantum chemical accuracy
11
A robotic platform for flow synthesis of organic compounds informed by AI planning
12
A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning
13
SciPy 1.0: fundamental algorithms for scientific computing in Python
14
Superhuman AI for multiplayer poker
15
Cormorant: Covariant Molecular Neural Networks
16
Joint Learning of Hierarchical Word Embeddings from a Corpus and a Taxonomy
17
Opinion: Toward an international definition of citizen science
18
The Tracking Machine Learning Challenge: Accuracy Phase
19
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
20
Lessons for artificial intelligence from the study of natural stupidity
21
Reducing BERT Pre-Training Time from 3 Days to 76 Minutes
22
Crystal symmetry determination in electron diffraction using machine learning
23
Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals
24
Human-level performance in 3D multiplayer games with population-based reinforcement learning
25
Chemical shifts in molecular solids by machine learning
26
Averaging Weights Leads to Wider Optima and Better Generalization
27
Artificial intelligence faces reproducibility crisis.
28
Alchemical and structural distribution based representation for universal quantum machine learning.
29
Residual Gated Graph ConvNets
30
Decoupled Weight Decay Regularization
31
Fixing Weight Decay Regularization in Adam
32
Graph Attention Networks
33
Remote optimization of an ultracold atoms experiment by experts and citizen scientists
34
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
35
Attention is All you Need
36
Neural Message Passing for Quantum Chemistry
37
Quantum-chemical insights from deep tensor neural networks
38
SGDR: Stochastic Gradient Descent with Warm Restarts
40
“Ask Ernö”: a self-learning tool for assignment and prediction of nuclear magnetic resonance spectra
41
The Higgs Machine Learning Challenge
42
Choosing experiments to accelerate collective discovery
43
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space
44
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.
45
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
46
Crowd science user contribution patterns and their implications
47
Adam: A Method for Stochastic Optimization
48
Neural Machine Translation by Jointly Learning to Align and Translate
49
Quantum chemistry structures and properties of 134 kilo molecules
50
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
51
Algorithm discovery by protein folding game players
52
Open Babel: An open chemical toolbox
53
Fast and accurate modeling of molecular atomization energies with machine learning.
54
Atom-centered symmetry functions for constructing high-dimensional neural network potentials.
55
Scikit-learn: Machine Learning in Python
56
Predicting protein structures with a multiplayer online game
57
970 million druglike small molecules for virtual screening in the chemical universe database GDB-13.
58
Generalized neural-network representation of high-dimensional potential-energy surfaces.
59
Ensembles of Learning Machines
60
Modern Multidimensional Scaling: Theory and Applications
61
Modern multidimensional scaling
62
Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields
63
Original Contribution: Training a 3-node neural network is NP-complete
64
Self‐consistent molecular orbital methods 25. Supplementary functions for Gaussian basis sets
65
Self—Consistent Molecular Orbital Methods. XII. Further Extensions of Gaussian—Type Basis Sets for Use in Molecular Orbital Studies of Organic Molecules
66
Self‐Consistent Molecular‐Orbital Methods. IX. An Extended Gaussian‐Type Basis for Molecular‐Orbital Studies of Organic Molecules
67
Nonmetric multidimensional scaling: A numerical method
68
Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
69
Mulbregt, and SciPy 1. 0 Contributors
70
The TrackML high-energy physics tracking challenge on Kaggle
71
We analyzed 16,625 papers to figure out where AI is headed
72
TensorFlow: Largescale machine learning
73
The Higgs boson machine learning challenge
74
THE THEIL-SEN ESTIMATORS IN A MULTIPLE LINEAR REGRESSION MODEL
75
NMRPredict Modgraph Consultants, Ltd, 1348 Graham Place, Escondido, CA 92129. http://www.modgraph-usa.com. Contact company for pricing information.
76
Self‐consistent molecular orbital methods. XX. A basis set for correlated wave functions
77
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
78
Rdkit: Open-source cheminformatics
79
Predicting Molecular Properties-Competition Finalized
