Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Yani Ioannou

Yani Ioannou

Measuring Neural Net Robustness with Constraints


Jun 16, 2017
Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, Antonio Criminisi

Figure 1 for Measuring Neural Net Robustness with Constraints
Figure 2 for Measuring Neural Net Robustness with Constraints
Figure 3 for Measuring Neural Net Robustness with Constraints
Figure 4 for Measuring Neural Net Robustness with Constraints

  Access Paper or Ask Questions

Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups


Nov 30, 2016
Yani Ioannou, Duncan Robertson, Roberto Cipolla, Antonio Criminisi

Figure 1 for Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups
Figure 2 for Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups
Figure 3 for Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups
Figure 4 for Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups
* Updated full version of paper, in full letter paper two-column paper. Includes many textual changes, updated CIFAR10 results, and new analysis of inter/intra-layer correlation 
  Access Paper or Ask Questions

Refining Architectures of Deep Convolutional Neural Networks


Apr 22, 2016
Sukrit Shankar, Duncan Robertson, Yani Ioannou, Antonio Criminisi, Roberto Cipolla

Figure 1 for Refining Architectures of Deep Convolutional Neural Networks
Figure 2 for Refining Architectures of Deep Convolutional Neural Networks
Figure 3 for Refining Architectures of Deep Convolutional Neural Networks
Figure 4 for Refining Architectures of Deep Convolutional Neural Networks
* 9 pages, 6 figures, CVPR 2016 
  Access Paper or Ask Questions

Decision Forests, Convolutional Networks and the Models in-Between


Mar 03, 2016
Yani Ioannou, Duncan Robertson, Darko Zikic, Peter Kontschieder, Jamie Shotton, Matthew Brown, Antonio Criminisi

Figure 1 for Decision Forests, Convolutional Networks and the Models in-Between
Figure 2 for Decision Forests, Convolutional Networks and the Models in-Between
Figure 3 for Decision Forests, Convolutional Networks and the Models in-Between
Figure 4 for Decision Forests, Convolutional Networks and the Models in-Between
* Microsoft Research Technical Report 
  Access Paper or Ask Questions

Training CNNs with Low-Rank Filters for Efficient Image Classification


Feb 07, 2016
Yani Ioannou, Duncan Robertson, Jamie Shotton, Roberto Cipolla, Antonio Criminisi

Figure 1 for Training CNNs with Low-Rank Filters for Efficient Image Classification
Figure 2 for Training CNNs with Low-Rank Filters for Efficient Image Classification
Figure 3 for Training CNNs with Low-Rank Filters for Efficient Image Classification
Figure 4 for Training CNNs with Low-Rank Filters for Efficient Image Classification
* International Conference on Learning Representations (ICLR), San Juan, Puerto Rico, 2-4 May 2016 
 * Published as a conference paper at ICLR 2016. v3: updated ICLR status. v2: Incorporated reviewer's feedback including: Amend Fig. 2 and 5 descriptions to explain that there are no ReLUs within the figures. Fix headings of Table 5 - Fix typo in the sentence at bottom of page 6. Add ref. to Predicting Parameters in Deep Learning. Fix Table 6, GMP-LR and GMP-LR-2x had incorrect numbers of filters 
  Access Paper or Ask Questions

Difference of Normals as a Multi-Scale Operator in Unorganized Point Clouds


Sep 08, 2012
Yani Ioannou, Babak Taati, Robin Harrap, Michael Greenspan

Figure 1 for Difference of Normals as a Multi-Scale Operator in Unorganized Point Clouds
Figure 2 for Difference of Normals as a Multi-Scale Operator in Unorganized Point Clouds
Figure 3 for Difference of Normals as a Multi-Scale Operator in Unorganized Point Clouds
Figure 4 for Difference of Normals as a Multi-Scale Operator in Unorganized Point Clouds
* Proceedings of the 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission (3DIMPVT) 
 * To be published in proceedings of 3DIMPVT 2012 
  Access Paper or Ask Questions