Yao Lu
I am a Postdoc at Peking University.
I got my PhD from Australian National University and Master from University of Helsinki, both in computer science.
My research interest is neural networks, including their theory, architectures and learning algorithms.
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Learning Sequence Attractors in Recurrent Networks with Hidden Neurons
Yao Lu, Si Wu
Neural Networks, 2024
[paper]
[code]
We pointed out the problem of recurrent networks of only visible binary neurons in generating sequences
and solved it by adding hidden neurons. A three-factor learning algorithm with theoretical guarantees
is proposed to learn sequences as attractors for the networks with hidden neurons.
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Bidirectionally Self-Normalizing Neural Networks
Yao Lu, Stephen Gould, Thalaiyasingam Ajanthan
Neural Networks, 2023
[paper]
[code]
[slides]
We theoretically solve the vanishing/exploding gradients problem in feedforward neural networks. Key idea: constraining signal propagation in both directions via a new class of activation functions and orthogonal weight matrices based on high-dimensional probability theory.
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Learning to Estimate Hidden Motions with Global Motion Aggregation
Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, Richard Hartley
IEEE International Conference on Computer Vision (ICCV), 2021
[paper]
[code]
We obtained the state-of-art optical flow estimation by integrating global motion features to handle occlusion.
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Learning Optical Flow from a Few Matches
Shihao Jiang, Yao Lu, Hongdong Li, Richard Hartley
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021
[paper]
[code]
We found high performance optical flow estimation can be achieved with a few candidate correspondences.
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Devon: Deformable Volume Network for Learning Optical Flow
Yao Lu, Jack Valmadre, Heng Wang, Juho Kannala, Mehrtash Harandi, Philip Torr
IEEE Winter Conference on Applications of Computer Vision (WACV), 2020
[paper]
[code]
[slides]
We pointed out the problem of image warping in estimating optical flow and proposed the deformable cost volume to solve the problem.
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Doubly Stochastic Neighbor Embedding on Spheres
Yao Lu, Jukka Corander, Zhirong Yang
Pattern Recognition Letters, 2019
[paper]
[code]
[demo]
We proposed a new method for data visualization, making it on spheres!
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Learning Image Relations with Contrast Association Networks
Yao Lu, Zhirong Yang, Juho Kannala, Samuel Kaski
International Joint Conference on Neural Networks (IJCNN), 2019
[paper]
[code]
[slides]
We proposed a new neural network module, Contrast Association Units, to model the relations between two sets of input variables.
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Block Mean Approximation for Efficient Second Order Optimization
Yao Lu, Mehrtash Harandi, Richard Hartley, Razvan Pascanu
ICML workshop on Modern Trends in Nonconvex Optimization for Machine Learning, 2018
[paper]
[code]
[slides]
We proposed a new matrix approximation method which allows efficient matrix inversion. We then applied the method to second order optimization algorithms for training neural networks.
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Oblivious Neural Network Predictions via MiniONN Transformations
Jian Liu, Mika Juuti, Yao Lu, N. Asokan
ACM Conference on Computer and Communications Security (CCS), 2017
[paper]
[code]
We proposed a new method for privacy-preserving predictions with trained neural networks.
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Unsupervised Learning on Neural Network Outputs: With Application in Zero-shot Learning
Yao Lu
International Joint Conference on Artificial Intelligence (IJCAI), 2016
[paper]
[code]
We found visual attributes of object classes can be unsuperisedly learned by applying Independent Component Analysis on the softmax outputs of a trained ConvNet. We showed such attributes can be useful for object recognition by performing zero-shot learning experiments on the ImageNet dataset of over 20,000 object classes.
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A Fast Projected Fixed-Point Algorithm for Large Graph Matching
Yao Lu, Kaizhu Huang, Cheng-Lin Liu
Pattern Recognition, 2016
[paper]
[code]
We designed a fast graph matching algorithm with time complexity O(n^3) per iteration, where n is the size of a graph. We proved its convergence rate. It takes within 10 seconds to match two graphs of 1,000 nodes on a PC.
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An Algorithm for Maximum Common Subgraph of Planar Triangulation Graphs
Yao Lu, Horst Bunke, Cheng-Lin Liu
Graph-Based Representations in Pattern Recognition (GbR), 2013
[paper]
[code]
We designed a fast Maximum Common Subgraph (MCS) algorithm for Planar Triangulation Graphs. Its time complexity is O(mnk), where n is the size of one graph, m is the size of the other graph and k is the size of their MCS.
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Traveling Bumps and Their Collisions in a Two-Dimensional Neural Field
Yao Lu, Yuzuru Sato, Shun-ichi Amari
Neural Computation, 2011
[paper]
[code]
We found a unique traveling bump solution, which was unknown to exist before, in a set of two-dimensional neural network equations.
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4D Convolution and Cost Volume in CUDA
Yao Lu
[code]
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A Simple ConvNet in 200 MATLAB Lines
Yao Lu
[code]
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Twitter Sentiment Analysis with ConvNet
Han Xiao, Yao Lu
[code]
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