L
Laurens van der Maaten
Researcher at Facebook
Publications - 127
Citations - 79845
Laurens van der Maaten is an academic researcher from Facebook. The author has contributed to research in topics: Computer science & Network architecture. The author has an hindex of 47, co-authored 118 publications receiving 54188 citations. Previous affiliations of Laurens van der Maaten include Maastricht University & Delft University of Technology.
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CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
Justin Johnson,Bharath Hariharan,Laurens van der Maaten,Li Fei-Fei,C. Lawrence Zitnick,Ross Girshick +5 more
TL;DR: This work presents a diagnostic dataset that tests a range of visual reasoning abilities and uses this dataset to analyze a variety of modern visual reasoning systems, providing novel insights into their abilities and limitations.
Proceedings ArticleDOI
Self-Supervised Learning of Pretext-Invariant Representations
TL;DR: This work develops Pretext-Invariant Representation Learning (PIRL), a new state-of-the-art in self-supervised learning from images that learns invariant representations based on pretext tasks that substantially improves the semantic quality of the learned image representations.
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Densely Connected Convolutional Networks
TL;DR: The Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion, and has several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.
Book ChapterDOI
Exploring the Limits of Weakly Supervised Pretraining
Dhruv Mahajan,Ross Girshick,Vignesh Ramanathan,Kaiming He,Manohar Paluri,Yixuan Li,Ashwin Bharambe,Laurens van der Maaten +7 more
TL;DR: In this paper, the authors presented a transfer learning approach with large convolutional networks trained to predict hashtags on billions of social media images and reported the highest ImageNet-1k single-crop, top-1 accuracy to date.
Proceedings ArticleDOI
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
TL;DR: This work introduces new sparse convolutional operations that are designed to process spatially-sparse data more efficiently, and uses them to develop Spatially-Sparse Convolutional networks, which outperform all prior state-of-the-art models on two tasks involving semantic segmentation of 3D point clouds.