2.1k citations · 6.9k across the 23 of their papers we have counts for
8 papers · 1 filter
Whose Ground Truth? Accounting for Individual and Collective Identities Underlying Dataset Annotation
Remi Denton, Mark Díaz, Ian Kivlichan +2
Human annotations play a crucial role in machine learning (ML) research and development. However, the ethical considerations around the processes and decisions that go into buildin…
Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research
Bernard Koch, Emily Denton, Alex Hanna +1
Benchmark datasets play a central role in the organization of machine learning research. They coordinate researchers around shared research problems and serve as a measure of progr…
AI and the Everything in the Whole Wide World Benchmark
Inioluwa Deborah Raji, Emily M. Bender, Amandalynne Paullada +2
There is a tendency across different subfields in AI to valorize a small collection of influential benchmarks. These benchmarks operate as stand-ins for a range of anointed common…
Data and its (dis)contents: A survey of dataset development and use in machine learning research
Amandalynne Paullada, Inioluwa Deborah Raji, Emily M. Bender +2
Datasets have played a foundational role in the advancement of machine learning research. They form the basis for the models we design and deploy, as well as our primary medium for…
Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure
Ben Hutchinson, Andrew Smart, Alex Hanna +5
Rising concern for the societal implications of artificial intelligence systems has inspired demands for greater transparency and accountability. However the datasets which empower…
Characterising Bias in Compressed Models
Sara Hooker, Nyalleng Moorosi, Gregory Clark +2
The popularity and widespread use of pruning and quantization is driven by the severe resource constraints of deploying deep neural networks to environments with strict latency, me…
Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement Learning
Sainbayar Sukhbaatar, Emily Denton, Arthur Szlam +1
In hierarchical reinforcement learning a major challenge is determining appropriate low-level policies. We propose an unsupervised learning scheme, based on asymmetric self-play fr…
Unsupervised Learning of Disentangled Representations from Video
Remi Denton, Vighnesh Birodkar
We present a new model DrNET that learns disentangled image representations from video. Our approach leverages the temporal coherence of video and a novel adversarial loss to learn…