Standout Papers
- Self Supervised Boosting (2002)
- Auto-Encoding Variational Bayes (2013)
- Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation (2006)
- Wormholes Improve Contrastive Divergence (2003)
- An Introduction to Variational Autoencoders (2019)
- Exponential Family Harmoniums with an Application to Information Retrieval (2004)
- Topographic Product Models Applied to Natural Scene Statistics (2005)
- Learning Sparse Topographic Representations with Products of Student-t Distributions (2002)
Citation Impact
Citing Papers
Hallucinating symmetric protein assemblies
2022 StandoutScienceNobel
Overcoming catastrophic forgetting in neural networks
2017 StandoutNobel
On Tracking The Partition Function
2011
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines
2010
A Spike and Slab Restricted Boltzmann Machine
2011
Dropout: a simple way to prevent neural networks from overfitting
2014 StandoutNobel
On the Convergence Properties of Contrastive Divergence
2010
Deep visual analogy-making
2015
Learning a Parametric Embedding by Preserving Local Structure
2009
Visualizing Data using t-SNE
2008 StandoutNobel
Multiple Texture Boltzmann Machines
2012
Why Does Unsupervised Pre-training Help Deep Learning?
2010
Generative versus discriminative training of RBMs for classification of fMRI images
2008 StandoutNobel
The Recurrent Temporal Restricted Boltzmann Machine
2008 StandoutNobel
Sparse deep belief net model for visual area V2
2007
Factored 3-Way Restricted Boltzmann Machines For Modeling Natural Images
2010 StandoutNobel
Auxiliary deep generative models
2016
Learning Deep Boltzmann Machines using Adaptive MCMC
2010
Modeling image patches with a directed hierarchy of Markov random fields
2007 StandoutNobel
Exploring Strategies for Training Deep Neural Networks
2009
On Autoencoders and Score Matching for Energy Based Models
2011
Reading Tea Leaves: How Humans Interpret Topic Models
2009
Regularized estimation of image statistics by Score Matching
2010
Rethinking LDA: Why Priors Matter
2009
Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure
2007 StandoutNobel
Variational inference in nonconjugate models
2013
Inductive Principles for Restricted Boltzmann Machine Learning
2010
Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation
2008
Implicit Mixtures of Restricted Boltzmann Machines
2008 StandoutNobel
Learning in Markov Random Fields using Tempered Transitions
2009
Annealing paths for the evaluation of topic models
2014
Generating more realistic images using gated MRF's
2010 StandoutNobel
Bayesian Checking for Topic Models
2011
Factorial LDA: Sparse Multi-Dimensional Text Models
2012
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
2010
Scalable Inference for Logistic-Normal Topic Models
2013
Deep Boltzmann machines
2009 StandoutNobel
Weight Uncertainty in Neural Network
2015
Learning Deep Energy Models
2011
Advancing mathematics by guiding human intuition with AI
2021 StandoutNatureNobel
Probabilistic machine learning and artificial intelligence
2015 Nature
Human ADAR1 Prevents Endogenous RNA from Triggering Translational Shutdown
2018 StandoutNobel
Foundation models for generalist medical artificial intelligence
2023 StandoutNature
Emergence of complex cell properties by learning to generalize in natural scenes
2008 Nature
Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons
2021 StandoutNobel
Quantum machine learning
2017 StandoutNature
Human-level control through deep reinforcement learning
2015 StandoutNatureNobel
Machine learning & artificial intelligence in the quantum domain: a review of recent progress
2018
A Fast Learning Algorithm for Deep Belief Nets
2006 StandoutNobel
Salmon provides fast and bias-aware quantification of transcript expression
2017 Standout
Scalable estimation strategies based on stochastic approximations: classical results and new insights
2015
Invariant Scattering Convolution Networks
2013
A survey on deep learning in medical image analysis
2017 Standout
Deep learning and process understanding for data-driven Earth system science
2019 StandoutNature
The rise of deep learning in drug discovery
2018
Deep learning
2015 StandoutNatureNobel
Representation Learning: A Review and New Perspectives
2013 Standout
Neuroscience-Inspired Artificial Intelligence
2017 StandoutNobel
3D variability analysis: Resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM
2021
Discovering faster matrix multiplication algorithms with reinforcement learning
2022 StandoutNatureNobel
Learning multiple layers of representation
2007 StandoutNobel
Generating functional protein variants with variational autoencoders
2021
Ultrafast machine vision with 2D material neural network image sensors
2020 StandoutNature
Fast activation maximization for molecular sequence design
2021
Artificial intelligence in radiology
2018 Standout
Modeling Natural Images Using Gated MRFs
2013 StandoutNobel
De novo protein design by deep network hallucination
2021 StandoutNatureNobel
Machine learning: Trends, perspectives, and prospects
2015 StandoutScience
Machine learning for data-driven discovery in solid Earth geoscience
2019 StandoutScience
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines
2010 StandoutNobel
A Probabilistic Framework for Deep Learning
2016
All-optical machine learning using diffractive deep neural networks
2018 StandoutScience
Justifying and Generalizing Contrastive Divergence
2008
Deep generative models for T cell receptor protein sequences
2019
Where Do Features Come From?
2013 StandoutNobel
Learning the 2-D Topology of Images
2007
Shaping the Water-Harvesting Behavior of Metal–Organic Frameworks Aided by Fine-Tuned GPT Models
2023 StandoutNobel
Clustering by Passing Messages Between Data Points
2007 StandoutScience
A generalization of improved susceptibility propagation
2013
Reducing the Dimensionality of Data with Neural Networks
2006 StandoutScienceNobel
Tensors in computations
2021
Learning invariant features through topographic filter maps
2009
Neural Variational Inference and Learning in Belief Networks
2014
Quickly Generating Representative Samples from an RBM-Derived Process
2011
An Efficient Learning Procedure for Deep Boltzmann Machines
2012 StandoutNobel
Constructing Free-Energy Approximations and Generalized Belief Propagation Algorithms
2005
Quantum Shell in a Shell: Engineering Colloidal Nanocrystals for a High-Intensity Excitation Regime
2023 StandoutNobel
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
2015 Standout
Computing non-negative tensor factorizations
2008
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies
2010
Deep learning for AI
2021 StandoutNobel
Deep learning of the tissue-regulated splicing code
2014
In All Likelihood, Deep Belief Is Not Enough
2010
Human-level concept learning through probabilistic program induction
2015 StandoutScience
Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics
2006
Graphical Models, Exponential Families, and Variational Inference
2007
Time-Resolved Line Shapes of Single Quantum Emitters via Machine Learned Photon Correlations
2023 StandoutNobel
Acquisition of chess knowledge in AlphaZero
2022 StandoutNobel
Using recurrent neural networks to optimize dynamical decoupling for quantum memory
2017
Adversarial autoencoder ensemble for fast and probabilistic reconstructions of few-shot photon correlation functions for solid-state quantum emitters
2022 StandoutNobel
Computational design of mechanically coupled axle-rotor protein assemblies
2022 StandoutScienceNobel
GAN（Generative Adversarial Nets）
2017 Standout
De novo design of small beta barrel proteins
2023 StandoutNobel
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
2018 Standout
Predicting research trends with semantic and neural networks with an application in quantum physics
2020 StandoutNobel
stm: An R Package for Structural Topic Models
2019 Standout
Learning Deep Architectures for AI
2009 Standout
A Theory of Usable Information Under Computational Constraints
2020
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
2011 Standout
Clustering by fast search and find of density peaks
2014 StandoutScience
Recent advances in convolutional neural networks
2017 Standout
Learning What and Where to Draw
2016
Data clustering: 50 years beyond K-means
2009 Standout
Probabilistic topic models
2012 Standout
ChatGPT Chemistry Assistant for Text Mining and the Prediction of MOF Synthesis
2023 StandoutNobel
Disentangling Rotational Dynamics and Ordering Transitions in a System of Self-Organizing Protein Nanorods via Rotationally Invariant Latent Representations
2021 StandoutNobel
Visual Recognition and Inference Using Dynamic Overcomplete Sparse Learning
2007
Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation
2020
The Discrete Infinite Logistic Normal Distribution
2012
A Model of Text for Experimentation in the Social Sciences
2016 Standout
A Connection Between Score Matching and Denoising Autoencoders
2011
Inductive principles for learning Restricted Boltzmann Machines
2010
Tensor Decompositions and Applications
2009 Standout
Towards an integration of deep learning and neuroscience
2016
High-Resolution Image Synthesis with Latent Diffusion Models
2022 Standout
Semantic hashing
2008 StandoutNobel
Works of Max Welling being referenced
Collapsed Variational Inference for HDP
2007
Multi-HDP: a non parametric Bayesian model for tensor factorization
2008
On the choice of regions for generalized belief propagation
2004
Self Supervised Boosting
2002 StandoutNobel
Learning in Markov Random Fields with Contrastive Free Energies
2005
On Improving the Efficiency of the Iterative Proportional Fitting Procedure
2003
Robust Higher Order Statistics.
2005
Exponential Family Harmoniums with an Application to Information Retrieval
2004 StandoutNobel
Linear Response for Approximate Inference
2003
Asynchronous Distributed Learning of Topic Models
2008
Learning Sparse Topographic Representations with Products of Student-t Distributions
2002 StandoutNobel
Distributed Algorithms for Topic Models
2009
Collapsed variational Dirichlet process mixture models
2007
Wormholes Improve Contrastive Divergence
2003 StandoutNobel
SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
2020
Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation
2006 StandoutNobel
Unsupervised Organization of Image Collections: Taxonomies and Beyond
2011
An Introduction to Variational Autoencoders
2019 Standout
Accelerated Variational Dirichlet Process Mixtures
2007
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring
2012
Stochastic collapsed variational Bayesian inference for latent Dirichlet allocation
2013
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget
2013
Positive tensor factorization
2001
Semi-Supervised Learning with Deep Generative Models
2014
Auto-Encoding Variational Bayes
2013 Standout
Approximate inference in Boltzmann machines
2002
The Variational Fair Autoencoder
2016
An Introduction to Variational Autoencoders
2019
Linear Response Algorithms for Approximate Inference in Graphical Models
2004
On Smoothing and Inference for Topic Models
2012
Topographic Product Models Applied to Natural Scene Statistics
2005 StandoutNobel
Bayesian Learning via Stochastic Gradient Langevin Dynamics
2011
Herding Dynamic Weights for Partially Observed Random Field Models
2012