AI Technology & Industry Review
A DeepMind research team presents the One Pass ImageNet (OPIN) problem, designed to study the space and compute efficiency of deep learning in a streaming setting with constrained data storage and to develop model training systems where each example is passed to the system only once.
A research team from Google Brain and New York University says the Natural Language Understanding (NLU) evaluation system is “broken” and proposes four criteria for improving NLU benchmarks.
To track progress in natural language generation (NLG) models, 55 researchers from more than 40 prestigious institutions have proposed GEM (Generation, Evaluation, and Metrics), a “living benchmark” NLG evaluation environment.
Google Research and DeepMind debut Long-Range Arena (LRA) benchmark for Transformer research on tasks with long sequence lengths.
The industry-standard MLPerf benchmark today released the results of the third round of its ongoing ML Training Systems competition.
Google AI researchers introduce Meta-Dataset, a large-scale and diverse benchmark for measuring the ability of few-shot classification models.
Deep Fashion3D contains 2,078 3D garment models reconstructed from real-world garments in 10 different clothing categories.
The new benchmark for wide-baseline image matching includes a 30k image dataset with depth maps and accurate pose information.
Researchers propose a flexible GNN benchmarking framework that can also accommodate the needs of researchers to add new datasets and models.
The study introduces an Event Recognition in Aerial video (ERA) dataset comprising 2,866 aerial videos collected from YouTube and annotated with labels from 25 different classes corresponding to an event that can be seen unfolding over a period of five seconds.
A team from Google Research introduced FixMatch, an algorithm that combines two common SSL methods for deep networks: pseudo-labeling (aka self-training) and consistency regularization.
In an effort to sustain RL’s momentum, a team of researchers from Machine Zone, Google Brain, and California Institute of Technology have introduced a new software framework and benchmark for reproducible reinforcement learning research.