SOTAVerified

BIG-bench Machine Learning

This branch include most common machine learning fundamental algorithms.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3251–3300 of 10033 papers

TitleStatusHype
Dynamic detection of mobile malware using smartphone data and machine learning—0
Dynamic Difficulty Awareness Training for Continuous Emotion Prediction—0
Dynamic fairness - Breaking vicious cycles in automatic decision making—0
Dynamic Fusion based Federated Learning for COVID-19 Detection—0
Recommending Dream Jobs in a Biased Real World—0
Smart Laptop Bag with Machine Learning for Activity Recognition—0
Anticipating cryptocurrency prices using machine learning—0
Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability—0
Dynamic optimization with side information—0
Direct Uncertainty Prediction for Medical Second Opinions—0
Dynamic Privacy For Distributed Machine Learning Over Network—0
Dynamic Reliability Management in Neuromorphic Computing—0
Directive Explanations for Actionable Explainability in Machine Learning Applications—0
A two-step machine learning approach for crop disease detection: an application of GAN and UAV technology—0
Dyslexia and Dysgraphia prediction: A new machine learning approach—0
DysLexML: Screening Tool for Dyslexia Using Machine Learning—0
A Machine Learning Approach for Early Detection of Fish Diseases by Analyzing Water Quality—0
Early Detection of In-Memory Malicious Activity based on Run-time Environmental Features—0
Direct Generation of Protein Conformational Ensembles via Machine Learning—0
A two-level machine learning framework for predictive maintenance: comparison of learning formulations—0
Early Prediction of 30-day ICU Re-admissions Using Natural Language Processing and Machine Learning—0
Early Warning System for Seismic Events in Coal Mines Using Machine Learning—0
Earthquake magnitude prediction in Hindukush region using machine learning techniques—0
Ease.ml: Towards Multi-tenant Resource Sharing for Machine Learning Workloads—0
EasyASR: A Distributed Machine Learning Platform for End-to-end Automatic Speech Recognition—0
DiPSeN: Differentially Private Self-normalizing Neural Networks For Adversarial Robustness in Federated Learning—0
Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation—0
ECG beat classification using machine learning and pre-trained convolutional neural networks—0
ECGDetect: Detecting Ischemia via Deep Learning—0
ECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT technique—0
Echo-CGC: A Communication-Efficient Byzantine-tolerant Distributed Machine Learning Algorithm in Single-Hop Radio Network—0
Echo State Network for two-dimensional turbulent moist Rayleigh-Bénard convection—0
A Multi-Engine Approach to Answer Set Programming—0
ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification—0
ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models—0
ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection—0
ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models—0
ECNU at SemEval-2018 Task 3: Exploration on Irony Detection from Tweets via Machine Learning and Deep Learning Methods—0
Adversarial Machine Learning based Partial-model Attack in IoT—0
Diminishing Returns Shape Constraints for Interpretability and Regularization—0
A Tunable Model for Graph Generation Using LSTM and Conditional VAE—0
Economic Event Detection in Company-Specific News Text—0
Economic Recession Prediction Using Deep Neural Network—0
EDEN: Evolutionary Deep Networks for Efficient Machine Learning—0
Edge Learning with Unmanned Ground Vehicle: Joint Path, Energy and Sample Size Planning—0
EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks—0
EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression—0
Dimensionality reduction with missing values imputation—0
Dimensionality reduction: theoretical perspective on practical measures—0
A Multi-Batch L-BFGS Method for Machine Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Fb232account and password 100—Unverified