199 papers with code • 1 benchmarks • 3 datasets
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Most implemented papers
Predicting the direction of stock market prices using random forest
In this paper, we propose a novel way to minimize the risk of investment in stock market by predicting the returns of a stock using a class of powerful machine learning algorithms known as ensemble learning.
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding
This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks.
DebiasedDTA: A Framework for Improving the Generalizability of Drug-Target Affinity Prediction Models
Here, we present DebiasedDTA, a novel drug-target affinity (DTA) prediction model training framework that addresses dataset biases to improve the generalizability of affinity prediction models.
Gossip Learning with Linear Models on Fully Distributed Data
Machine learning over fully distributed data poses an important problem in peer-to-peer (P2P) applications.
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition.
An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy
In this paper, we provide a holistic view of Etsy's promoted listings' CTR prediction system and propose an ensemble learning approach which is based on historical or behavioral signals for older listings as well as content-based features for new listings.
Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions
Learning algorithms that aggregate predictions from an ensemble of diverse base classifiers consistently outperform individual methods.
Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation
As opposed to manual feature engineering which is tedious and difficult to scale, network representation learning has attracted a surge of research interests as it automates the process of feature learning on graphs.
General audio tagging with ensembling convolutional neural network and statistical features
Audio tagging is challenging due to the limited size of data and noisy labels.
Ensemble Knowledge Distillation for Learning Improved and Efficient Networks
Ensemble models comprising of deep Convolutional Neural Networks (CNN) have shown significant improvements in model generalization but at the cost of large computation and memory requirements.