Sebastian Pineda-Arango | DeepAI

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∙ 06/06/2023

Quick-Tune: Quickly Learning Which Pretrained Model to Finetune and How

With the ever-increasing number of pretrained models, machine learning p...

0 Sebastian Pineda-Arango, et al. ∙

∙ 05/23/2023

Deep Pipeline Embeddings for AutoML

Automated Machine Learning (AutoML) is a promising direction for democra...

0 Sebastian Pineda-Arango, et al. ∙

∙ 05/22/2023

Breaking the Paradox of Explainable Deep Learning

Deep Learning has achieved tremendous results by pushing the frontier of...

0 Arlind Kadra, et al. ∙

∙ 03/27/2023

Deep Ranking Ensembles for Hyperparameter Optimization

Automatically optimizing the hyperparameters of Machine Learning algorit...

0 Abdus Salam Khazi, et al. ∙

∙ 12/20/2021

Transformers Can Do Bayesian Inference

Currently, it is hard to reap the benefits of deep learning for Bayesian...

0 Samuel Müller, et al. ∙

∙ 08/05/2021

Multimodal Meta-Learning for Time Series Regression

Recent work has shown the efficiency of deep learning models such as Ful...

0 Sebastian Pineda-Arango, et al. ∙

∙ 06/11/2021

HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML

Hyperparameter optimization (HPO) is a core problem for the machine lear...

0 Sebastian Pineda-Arango, et al. ∙

∙ 07/30/2020

Improving Sample Efficiency with Normalized RBF Kernels

In deep learning models, learning more with less data is becoming more i...

0 Sebastian Pineda-Arango, et al. ∙

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