Citation Impact
Citing Papers
Hash Kernels for Structured Data
2009
Dropout: a simple way to prevent neural networks from overfitting
2014 StandoutNobel
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization
2010 Standout
Monotonic calibrated interpolated look-up tables
2016
Simple random search of static linear policies is competitive for reinforcement learning
2018
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
2010 Standout
Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling
2007
On the Consistency of Multi-Label Learning.
2011
OTL: A Framework of Online Transfer Learning
2010
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
2006 Standout
A linear speedup analysis of distributed deep learning with sparse and quantized communication
2018
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
2019 Standout
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
2018 Standout
SignalP 5.0 improves signal peptide predictions using deep neural networks
2019 Standout
Computational pathology: Challenges and promises for tissue analysis
2011
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
2015 Standout
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 Standout
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
2018 Standout
Sharing Visual Features for Multiclass and Multiview Object Detection
2007
Deep learning in histopathology: the path to the clinic
2021 Standout
Improved protein structure prediction using potentials from deep learning
2020 StandoutNatureNobel
Brain tumor segmentation with Deep Neural Networks
2016 Standout
The Graph Neural Network Model
2008 Standout
Applications of machine learning in drug discovery and development
2019 Standout
Sample selection bias and presence‐only distribution models: implications for background and pseudo‐absence data
2009 Standout
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
2021 Standout
Tracking-Learning-Detection
2011 Standout
Recurrent Neural Networks for Multivariate Time Series with Missing Values
2018 Standout
A Fast Algorithm for Learning a Ranking Function from Large-Scale Data Sets
2008
Taking the Human Out of the Loop: A Review of Bayesian Optimization
2015 Standout
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
2017 Standout
All-optical machine learning using diffractive deep neural networks
2018 StandoutScience
Federated Learning in Mobile Edge Networks: A Comprehensive Survey
2020 Standout
Deep Learning Approach for Intelligent Intrusion Detection System
2019 Standout
Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey
2020 Standout
The WEKA data mining software
2009 Standout
The Pascal Visual Object Classes (VOC) Challenge
2009 Standout
Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
2012
A Comprehensive Survey on Transfer Learning
2020 Standout
A survey on Image Data Augmentation for Deep Learning
2019 Standout
Optimization problems for machine learning: A survey
2020
Adaptive regularization of weight vectors
2013
On label dependence and loss minimization in multi-label classification
2012
Human-level concept learning through probabilistic program induction
2015 StandoutScience
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey
2019 Standout
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
2014 Standout
A survey on semi-supervised learning
2019 Standout
Understanding deep learning (still) requires rethinking generalization
2021 Standout
On hyperparameter optimization of machine learning algorithms: Theory and practice
2020 Standout
An overview of multi-task learning
2017
Ensembles of nested dichotomies for multi-class problems
2004
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
2018 Standout
A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load
2017 Standout
A Non-Stochastic Learning Approach to Energy Efficient Mobility Management
2016
Multilabel classification via calibrated label ranking
2008
Recent advances in convolutional neural networks
2017 Standout
Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox
2018 Standout
Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images
2015 Standout
Semi-Supervised Learning
2006 Standout
Data clustering: 50 years beyond K-means
2009 Standout
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
2019 Standout
A tutorial on spectral clustering
2007 Standout
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
2016 Standout
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning
2019
A Review on Multi-Label Learning Algorithms
2013 Standout
Deep learning with convolutional neural networks for EEG decoding and visualization
2017 Standout
Multimodal Machine Learning: A Survey and Taxonomy
2018 Standout
Communication-Efficient Distributed Dual Coordinate Ascent
2014
Target Classification Using the Deep Convolutional Networks for SAR Images
2016 Standout
Deep Learning in Medical Image Analysis
2017 Standout
Works of Ofer Dekel being referenced
Online Learning of Multiple Tasks with a Shared Loss
2007
Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback.
2010
Log-Linear Models for Label Ranking
2003
The Forgetron: A Kernel-Based Perceptron on a Fixed Budget
2005
Vox Populi: Collecting High-Quality Labels from a Crowd
2009
A Boosting Algorithm for Label Covering in Multilabel Problems
2007
Online Passive-Aggressive Algorithms
2003
Optimal Distributed Online Prediction
2011
Multiclass Learning by Probabilistic Embeddings
2002
Learning to classify with missing and corrupted features
2009
Smooth ε-Insensitive Regression by Loss Symmetrization
2003
Online Bandit Learning against an Adaptive Adversary: from Regret to Policy Regret
2012
The Forgetron: A Kernel-Based Perceptron on a Budget
2008
Optimal Distributed Online Prediction using Mini-Batches
2010