• Publications
  • Influence
Visualizing Data using t-SNE
We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of StochasticExpand
CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning
TLDR
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. Expand
Dimensionality Reduction: A Comparative Review
TLDR
The results of the experiments reveal that nonlinear techniques perform well on selected artificial tasks, but that this strong performance does not necessarily extend to real-world tasks. Expand
Accelerating t-SNE using tree-based algorithms
TLDR
Variants of the Barnes-Hut algorithm and of the dual-tree algorithm that approximate the gradient used for learning t-SNE embeddings in O(N log N) are developed and shown to substantially accelerate and make it possible to learnembeddings of data sets with millions of objects. Expand
Countering Adversarial Images using Input Transformations
TLDR
This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system, and shows that total variance minimization and image quilting are very effective defenses in practice, when the network is trained on transformed images. Expand
Feature Denoising for Improving Adversarial Robustness
TLDR
It is suggested that adversarial perturbations on images lead to noise in the features constructed by these networks, and new network architectures are developed that increase adversarial robustness by performing feature denoising. Expand
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
TLDR
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. Expand
Inferring and Executing Programs for Visual Reasoning
TLDR
A model for visual reasoning that consists of a program generator that constructs an explicit representation of the reasoning process to be performed, and an execution engine that executes the resulting program to produce an answer is proposed. Expand
Exploring the Limits of Weakly Supervised Pretraining
TLDR
This paper presents a unique study of transfer learning with large convolutional networks trained to predict hashtags on billions of social media images and shows improvements on several image classification and object detection tasks, and reports the highest ImageNet-1k single-crop, top-1 accuracy to date. Expand
Multi-Scale Dense Networks for Resource Efficient Image Classification
TLDR
Experiments demonstrate that the proposed framework substantially improves the existing state-of-the-art in both image classification with computational resource limits at test time and budgeted batch classification. Expand
...
1
2
3
4
5
...