Publications related to Gradient boosting

Semantic Perturbations with Normalizing Flows for Improved Generalization

Martin Jaggi, Sebastian Urban Stich, Tatjana Chavdarova

Data augmentation is a widely adopted technique for avoiding overfitting when training deep neural networks. However, this approach requires domain-specific knowledge and is often limited to a fixed set of hard-coded transformations. Recently, several work ...

IEEE2021

NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization

Dan Alistarh, Fartash Faghri

As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform parallel model training. One popular communication-compression method for data-para ...

MICROTOME PUBL2021

Emerging perovskite quantum dot solar cells: feasible approaches to boost performance

Ulf Anders Hagfeldt

Lead halide perovskite quantum dots (PQDs), also called perovskite nanocrystals, are considered as one of the most promising classes of photovoltaic materials for solar cells due to their prominent optoelectronic properties and simple preparation technique ...

ROYAL SOC CHEMISTRY2021

Accelerating Gradient Boosting Machines

Sai Praneeth Reddy Karimireddy

Gradient Boosting Machine (GBM) introduced by Friedman (2001) is a widely popular ensembling technique and is routinely used in competitions such as Kaggle and the KDD-Cup (Chen and Guestrin, 2016). In this work, we propose an Accelerated Gradient Boosting ...

ADDISON-WESLEY PUBL CO2020

When Is Amplification Necessary for Composition in Randomized Query Complexity?

Mika Tapani Göös

Suppose we have randomized decision trees for an outer function f and an inner function g. The natural approach for obtaining a randomized decision tree for the composed function (f∘ gⁿ)(x¹,…,xⁿ) = f(g(x¹),…,g(xⁿ)) involves amplifying the success probabili ...

Schloss Dagstuhl - Leibniz-Zentrum für Informatik2020

Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

Sabine Süsstrunk, Majed El Helou, Ruofan Zhou

Super-resolution and denoising are ill-posed yet fundamental image restoration tasks. In blind settings, the degradation kernel or the noise level are unknown. This makes restoration even more challenging, notably for learning-based methods, as they tend t ...

2020

Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning

Nikolaos Stergiopoulos, Georgios Rovas, Vasiliki Bikia, Stamatia Zoi Pagoulatou

Cardiac and aortic characteristics are crucial for cardiovascular disease detection. However, noninvasive estimation of aortic hemodynamics and cardiac contractility is still challenging. This paper investigated the potential of estimating aortic systolic ...

2020

Regression Networks for Meta-Learning Few-Shot Classification

Matthias Grossglauser, Arnout Jan J Devos

We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class. In high dimensional embedding spaces the directi ...

2020

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

Mahsa Shoaran, Bingzhao Zhu

Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications such as medical and IoT devices. This paper introduces a machine learning model based on oblique decision ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

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Gradient boosting

Postmortem memory of public figures in news and social media

Semantic Perturbations with Normalizing Flows for Improved Generalization

NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization

Emerging perovskite quantum dot solar cells: feasible approaches to boost performance

Accelerating Gradient Boosting Machines

When Is Amplification Necessary for Composition in Randomized Query Complexity?

Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning

Regression Networks for Meta-Learning Few-Shot Classification

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

Graph Chatbot

Chat with Graph Search

Emerging perovskite quantum dot solar cells: feasible approaches to boost performance

Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning

When Is Amplification Necessary for Composition in Randomized Query Complexity?

Accelerating Gradient Boosting Machines

Regression Networks for Meta-Learning Few-Shot Classification

Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

Postmortem memory of public figures in news and social media

Semantic Perturbations with Normalizing Flows for Improved Generalization

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification

NUQSGD: Provably Communication-efficient Data-parallel SGD via Nonuniform Quantization