Fetching the paper…
Reading the bibliography…
Automated machine learning (AutoML) is a collection of techniques designed to automate the machine learning development process.
Language models are few-shot learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 1901
Earlier work this paper cites.
Fastfcn: Rethinking dilated convolution in the backbone for semantic segmentation
Wu, H.; Zhang, J.; Huang, K.; Liang, K.; and Yu, Y. 2019 · 1903
Earlier work this paper cites.
FCOS: Fully Convolutional One-Stage Object Detection
Tian, Z.; Shen, C.; Chen, H.; and He, T. 2019 · 1904
Earlier work this paper cites.
MMDetection: Open MMLab Detection Toolbox and Benchmark
Chen, K.; Wang, J.; Pang, J.; Cao, Y.; Xiong, Y.; Li, X.; Sun, S.; Feng, W.; Liu, Z.; Xu, J.; Zhang, Z.; Cheng, D.; Zhu, C.; Cheng, T.; Zhao, Q.; Li, B.; Lu, X.; Zhu, R.; Wu, Y.; Dai, J.; Wang, J.; Shi, J.; Ouyang, W.; Loy, C. C.; and Lin, D. 2019 · 1906
Earlier work this paper cites.
Auto-keras: An efficient neural architecture search system
Jin, H.; Song, Q.; and Hu, X. 2019 · 1956
Earlier work this paper cites.
WordNet: a lexical database for English
Miller, G. A. 1995 · 1995
Earlier work this paper cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2020 · 2010
Earlier work this paper cites.
VEGA: Towards an End-to-End Configurable AutoML Pipeline
Wang, B.; Xu, H.; Zhang, J.; Chen, C.; Fang, X.; Kang, N.; Hong, L.; Zhang, W.; Li, Y.; Liu, Z.; Li, Z.; Liu, W.; and Zhang, T. 2020 · 2011
Earlier work this paper cites.
Random search for hyper-parameter optimization
Bergstra, J.; and Bengio, Y. 2012 · 2012
Earlier work this paper cites.
On the importance of initialization and momentum in deep learning
Sutskever, I.; Martens, J.; Dahl, G.; and Hinton, G. 2013 · 2013
Earlier work this paper cites.
Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms
Thornton, C.; Hutter, F.; Hoos, H. H.; and Leyton-Brown, K. 2013 · 2013
Earlier work this paper cites.
Generating Sequences With Recurrent Neural Networks
Graves, A. 2014 · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2014 · 2014
Earlier work this paper cites.
Efficient and robust automated machine learning
Feurer, M.; Klein, A.; Eggensperger, K.; Springenberg, J.; Blum, M.; and Hutter, F. 2015 · 2015
Earlier work this paper cites.
Fast r-cnn
Girshick, R. 2015 · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Cordts, M.; Omran, M.; Ramos, S.; Rehfeld, T.; Enzweiler, M.; Benenson, R.; Franke, U.; Roth, S.; and Schiele, B. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
Earlier work this paper cites.
SSD: Single Shot MultiBox Detector
Liu, W.; Anguelov, D.; Erhan, D.; Szegedy, C.; Reed, S.; Fu, C.-Y.; and Berg, A. C. 2016 · 2016
Earlier work this paper cites.
Hyperparameter optimization with approximate gradient
Pedregosa, F. 2016 · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; and Wojna, Z. 2016 · 2016
Earlier work this paper cites.
Rethinking atrous convolution for semantic image segmentation
Chen, L.-C.; Papandreou, G.; Schroff, F.; and Adam, H. 2017 · 2017
Earlier work this paper cites.
Forward and reverse gradient-based hyperparameter optimization
Franceschi, L.; Donini, M.; Frasconi, P.; and Pontil, M. 2017 · 2017
Cited alongside, same era.
Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2017 · 2017
Cited alongside, same era.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and Hinton, G. E. 2017 · 2017
Cited alongside, same era.
Focal loss for dense object detection
Lin, T.-Y.; Goyal, P.; Girshick, R.; He, K.; and Dollár, P. 2017 · 2017
Cited alongside, same era.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2017 · 2017
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
Shelhamer, E.; Long, J.; and Darrell, T. 2017 · 2017
Amazon SageMaker Autopilot: a white box AutoML solution at scale
Das, P.; Ivkin, N.; Bansal, T.; Rouesnel, L.; Gautier, P.; Karnin, Z.; Dirac, L.; Ramakrishnan, L.; Perunicic, A.; Shcherbatyi, I.; et al. 2020 · 2020
Later among the works it cites.
OpenMMLab’s Model Deployment Toolbox
Contributors, M. 2021 · 2021
Later among the works it cites.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; Uszkoreit, J.; and Houlsby, N. 2021 · 2021
Later among the works it cites.
YOLOX: Exceeding YOLO Series in 2021
Ge, Z.; Liu, S.; Wang, F.; Li, Z.; and Sun, J. 2021 · 2021
Later among the works it cites.
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z.; Lin, Y.; Cao, Y.; Hu, H.; Wei, Y.; Zhang, Z.; Lin, S.; and Guo, B. 2021 · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Pyramid Scene Parsing Network
Zhao, H.; Shi, J.; Qi, X.; Wang, X.; and Jia, J. 2017 · 2017
Cited alongside, same era.
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C.; Zhu, Y.; Papandreou, G.; Schroff, F.; and Adam, H. 2018 · 2018
Cited alongside, same era.
Squeeze-and-excitation networks
Hu, J.; Shen, L.; and Sun, G. 2018 · 2018
Cited alongside, same era.
Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ma, N.; Zhang, X.; Zheng, H.-T.; and Sun, J. 2018 · 2018
Cited alongside, same era.
YOLOv3: An Incremental Improvement
Redmon, J.; and Farhadi, A. 2018 · 2018
Cited alongside, same era.
Simple Baselines for Human Pose Estimation and Tracking
Xiao, B.; Wu, H.; and Wei, Y. 2018 · 2018
Cited alongside, same era.
MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
Mehta, S.; and Rastegari, M. 2021 · 2021
Later among the works it cites.
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
Xie, E.; Wang, W.; Yu, Z.; Anandkumar, A.; Alvarez, J. M.; and Luo, P. 2021 · 2021
Later among the works it cites.
Deformable DETR: Deformable Transformers for End-to-End Object Detection
Zhu, X.; Su, W.; Lu, L.; Li, B.; Wang, X.; and Dai, J. 2021 · 2021
Later among the works it cites.
Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
Zimmer, L.; Lindauer, M.; and Hutter, F. 2021 · 2021
Later among the works it cites.
Palm: Scaling language modeling with pathways
Chowdhery, A.; Narang, S.; Devlin, J.; Bosma, M.; Mishra, G.; Roberts, A.; Barham, P.; Chung, H. W.; Sutton, C.; Gehrmann, S.; et al. 2022 · 2022
Later among the works it cites.
LoRA: Low-Rank Adaptation of Large Language Models
Hu, E. J.; Shen, Y.; Wallis, P.; Allen-Zhu, Z.; Li, Y.; Wang, S.; Wang, L.; and Chen, W. 2022 · 2022
Later among the works it cites.
SMAC3: A versatile Bayesian optimization package for hyperparameter optimization
Lindauer, M.; Eggensperger, K.; Feurer, M.; Biedenkapp, A.; Deng, D.; Benjamins, C.; Ruhkopf, T.; Sass, R.; and Hutter, F. 2022 · 2022
Later among the works it cites.
Self-instruct: Aligning language model with self generated instructions
Wang, Y.; Kordi, Y.; Mishra, S.; Liu, A.; Smith, N. A.; Khashabi, D.; and Hajishirzi, H. 2022 · 2022
Later among the works it cites.
OpenMMLab’s Pre-training Toolbox and Benchmark
Contributors, M. 2023 · 2023
Later among the works it cites.
CAAFE: Combining Large Language Models with Tabular Predictors for Semi-Automated Data Science
Hollmann, N.; Müller, S.; and Hutter, F. 2023 · 2023
Later among the works it cites.
Autokeras: An automl library for deep learning
Jin, H.; Chollet, F.; Song, Q.; and Hu, X. 2023 · 2023
Later among the works it cites.
OpenAI. 2023 · 2023
Later among the works it cites.
Using large language models for hyperparameter optimization
Zhang, M. R.; Desai, N.; Bae, J.; Lorraine, J.; and Ba, J. 2023 · 2023
Later among the works it cites.
Can GPT-4 Perform Neural Architecture Search?
Zheng, M.; Su, X.; You, S.; Wang, F.; Qian, C.; Xu, C.; and Albanie, S. 2023 · 2023
Later among the works it cites.
DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning
Guo, S.; Deng, C.; Wen, Y.; Chen, H.; Chang, Y.; and Wang, J. 2024 · 2024
Closest in time.
Data interpreter: An llm agent for data science
Hong, S.; Lin, Y.; Liu, B.; Liu, B.; Wu, B.; Zhang, C.; Wei, C.; Li, D.; Chen, J.; Zhang, J.; et al. 2024 · 2024
Closest in time.
MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
Huang, Q.; Vora, J.; Liang, P.; and Leskovec, J. 2024 · 2024
Closest in time.
Limits and possibilities for “Ethical AI” in open source: A study of deepfakes
Widder, D. G.; Nafus, D.; Dabbish, L.; and Herbsleb, J. 2022 · 2046
Closest in time.