Referenced Papers (17)
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, Jason Weston
, 2009
"Cited as the foundational work on curriculum learning, a method inspired by human learning to improve model convergence and performance."
On the power of curriculum learning in training deep networks
Guy Hacohen, Daphna Weinshall
arXiv [cs.LG], 2019
"Cited as an example of curriculum learning being applied in the domain of computer vision."
Learning from children: Improving image-caption pretraining via curriculum
Hammad A Ayyubi, Rahul Lokesh, Alireza Zareian, Bo Wu, Shih-Fu Chang
arXiv [cs.CV], 2023
"Cited as an example of curriculum learning being applied in multimodal settings."
Competence-based curriculum learning for neural machine translation
Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabas Poczos, Tom M Mitchell
arXiv [cs.CL], 2019
"Cited for their work on sentence length and word rarity as difficulty metrics for curriculum learning in NLP."
On curriculum learning for commonsense reasoning
Adyasha Maharana, Mohit Bansal
, 2022
"Cited as a work applying curriculum learning in NLP, specifically in areas like machine translation and common sense reasoning."
Curriculum learning for language modeling
Daniel Campos
arXiv [cs.CL], 2021
"Cited as a work applying curriculum learning in NLP, specifically in areas like machine translation and common sense reasoning."
Less is more: Pre-training cross-lingual Small-Scale Language Models with cognitively-plausible curriculum learning strategies
Suchir Salhan, Richard Diehl Martinez, Zébulon Goriely, Paula Buttery
arXiv [cs.CL], 2024
"Cited as a work applying curriculum learning in NLP, specifically in areas like machine translation and common sense reasoning."
Findings of the 2016 conference on machine translation
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck
, 2016
"Cited for machine translation benchmarks that show curriculum learning benefits transformers but not RNNs."
How many words does ChatGPT know? The answer is ChatWords
Gonzalo Martínez, Javier Conde, Pedro Reviriego, Elena Merino-Gómez, José Alberto Hernández, Fabrizio Lombardi
arXiv [cs.CL], 2023
"Cited as a work that showed curriculum learning does not consistently improve model performance over NLP benchmarks."
The open images dataset V4
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset
Int. J. Comput. Vis., 2020
"Cited as the source for the 'Localized Narratives' image caption dataset, used in the multimodal track of the BabyLM challenge."
Improved Training with Curriculum GANs
Rishi Sharma, Shane Barratt, Stefano Ermon, Vijay Pande
arXiv [cs.LG], 2018
"Cited as the source for the 'Conceptual Captions 3M' image caption dataset, used in the multimodal track of the BabyLM challenge."
The CHILDES Project: Tools for Analyzing Talk (third edition): Volume I: Transcription format and programs, Volume II: The database
Brian MacWhinney
Comput. Linguist. Assoc. Comput. Linguist., 2000
"Cited as the source for the 'CHILDES' child-directed speech dataset, used in the multimodal track of the BabyLM challenge."
A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics
Martin Gerlach, Francesc Font-Clos
arXiv [cs.CL], 2018
"Cited as the source for the 'Project Gutenberg (children's stories)' written English dataset, used in the multimodal track of the BabyLM challenge."
Dialogue act modeling for automatic tagging and recognition of conversational speech
Andreas Stolcke, Klaus Ries, Noah Coccaro, Elizabeth Shriberg, Rebecca Bates, Daniel Jurafsky
Comput. Linguist. Assoc. Comput. Linguist., 2000
"Cited as the source for the 'Switchboard Dialog Act Corpus' dialogue dataset, used in the multimodal track of the BabyLM challenge."
Winoground: Probing vision and language models for visio-linguistic compositionality
Tristan Thrush, Ryan Jiang, Max Bartolo, Amanpreet Singh, Adina Williams, Douwe Kiela
arXiv [cs.CV], 2022
"Cited as the source for the 'Winground' multimodal dataset, used for evaluating compositional understanding."
Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, Devi Parikh
arXiv [cs.CV], 2016
"Cited as the source for the 'VQAv2' visual question answering dataset, used for evaluating model performance in answering questions given a picture."
SSR: Alignment-aware modality Connector for speech language models
Weiting Tan, Hirofumi Inaguma, Ning Dong, Paden Tomasello, Xutai Ma
arXiv [cs.CL], 2024
"Cited as the source for the 'DevBench' multimodal dataset, which contains tasks related to lexical visual vocabulary, receptive grammar, and semantic odd-one-out."