Search Results for author:
Found 32 papers, 14 papers with code
Real-Time Neural Character Rendering with Pose-Guided Multiplane Images
We propose pose-guided multiplane image (MPI) synthesis which can render an animatable character in real scenes with photorealistic quality.
Semi-Supervised Image-to-Image Translation using Latent Space Mapping
Thanks to the low dimensional feature space, it is easier to find the desired mapping function, resulting in improved quality of translation results as well as the stability of the translation model.
Tensor networks for unsupervised machine learning
Modeling the joint distribution of high-dimensional data is a central task in unsupervised machine learning.
Robust Mutual Learning for Semi-supervised Semantic Segmentation
The proposed robust mutual learning demonstrates state-of-the-art performance on semantic segmentation in low-data regime.
Boltzmann machines as two-dimensional tensor networks
Using numerical experiments, we demonstrate that the proposed algorithm is much more accurate than the state-of-the-art machine learning methods in estimating the partition function of restricted Boltzmann machines and deep Boltzmann machines, and have potential applications in training deep Boltzmann machines for general machine learning tasks.
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation
In this paper, we rely on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation.
Ranked #7 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
CoCosNet v2: Full-Resolution Correspondence Learning for Image Translation
We present the full-resolution correspondence learning for cross-domain images, which aids image translation.
Old Photo Restoration via Deep Latent Space Translation
Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize.
Supervised Learning with Projected Entangled Pair States
Tensor networks, a model that originated from quantum physics, has been gradually generalized as efficient models in machine learning in recent years.
Contact Area Detector using Cross View Projection Consistency for COVID-19 Projects
Instead of doing 3D scene reconstruction or transfer learning from deep networks, a mapping from the surface in the two camera views to the surface space is the only requirement.
Tropical Tensor Network for Ground States of Spin Glasses
We present a unified exact tensor network approach to compute the ground state energy, identify the optimal configuration, and count the number of solutions for spin glasses.
Statistical Mechanics Quantum Physics Computation
Bringing Old Photos Back to Life
Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize.
Cross-domain Correspondence Learning for Exemplar-based Image Translation
The output has the style (e. g., color, texture) in consistency with the semantically corresponding objects in the exemplar.
Ranked #1 on
Image-to-Image Translation
on ADE20K Labels-to-Photos
(FID metric)
Solving Quantum Statistical Mechanics with Variational Autoregressive Networks and Quantum Circuits
We extend the ability of unitary quantum circuits by interfacing it with classical autoregressive neural networks.
Quantum Physics
Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design
We introduce Yao, an extensible, efficient open-source framework for quantum algorithm design.
Quantum Physics Strongly Correlated Electrons Computational Physics
Contracting Arbitrary Tensor Networks: general approximate algorithm and applications in graphical models and quantum circuit simulations
We present a general method for approximately contracting tensor networks with an arbitrary connectivity.
Computational Physics Statistical Mechanics Strongly Correlated Electrons Quantum Physics
Phase transitions and optimal algorithms for semi-supervised classifications on graphs: from belief propagation to graph convolution network
For the first time, well-controlled benchmark datasets with asymptotially exact properties and optimal solutions could be produced for the evaluation of graph convolution neural networks, and for the theoretical understanding of their strengths and weaknesses.
Solving Statistical Mechanics on Sparse Graphs with Feedback Set Variational Autoregressive Networks
We propose a method for solving statistical mechanics problems defined on sparse graphs.
A streaming feature-based compression method for data from instrumented infrastructure
An increasing amount of civil engineering applications are utilising data acquired from infrastructure instrumented with sensing devices.
Tree Tensor Networks for Generative Modeling
Matrix product states (MPS), a tensor network designed for one-dimensional quantum systems, has been recently proposed for generative modeling of natural data (such as images) in terms of `Born machine'.
Shortcut Matrix Product States and its applications
Matrix Product States (MPS), also known as Tensor Train (TT) decomposition in mathematics, has been proposed originally for describing an (especially one-dimensional) quantum system, and recently has found applications in various applications such as compressing high-dimensional data, supervised kernel linear classifier, and unsupervised generative modeling.
Solving Statistical Mechanics Using Variational Autoregressive Networks
We propose a general framework for solving statistical mechanics of systems with finite size.
Weighted Community Detection and Data Clustering Using Message Passing
In the community detection problem in weighted and directed networks, we show that our algorithm significantly outperforms existing algorithms.
Spectral estimation of the percolation transition in clustered networks
There have been several spectral bounds for the percolation transition in networks, using spectrum of matrices associated with the network such as the adjacency matrix and the non-backtracking matrix.
Unsupervised Generative Modeling Using Matrix Product States
Generative modeling, which learns joint probability distribution from data and generates samples according to it, is an important task in machine learning and artificial intelligence.
Robust Spectral Detection of Global Structures in the Data by Learning a Regularization
Spectral methods are popular in detecting global structures in the given data that can be represented as a matrix.
Detectability thresholds and optimal algorithms for community structure in dynamic networks
We study the fundamental limits on learning latent community structure in dynamic networks.
Evaluating accuracy of community detection using the relative normalized mutual information
The Normalized Mutual Information (NMI) has been widely used to evaluate the accuracy of community detection algorithms.
Phase transitions in semisupervised clustering of sparse networks
For larger $k$ where a hard but detectable regime exists, we find that the easy/hard transition (the point at which efficient algorithms can do better than chance) becomes a line of transitions where the accuracy jumps discontinuously at a critical value of $\alpha$.
Scalable detection of statistically significant communities and hierarchies, using message-passing for modularity
We address this problem by using the modularity as a Hamiltonian at finite temperature, and using an efficient Belief Propagation algorithm to obtain the consensus of many partitions with high modularity, rather than looking for a single partition that maximizes it.
Spectral redemption: clustering sparse networks
Spectral algorithms are classic approaches to clustering and community detection in networks.
Model Selection for Degree-corrected Block Models
We present the first principled and tractable approach to model selection between standard and degree-corrected block models, based on new large-graph asymptotics for the distribution of log-likelihood ratios under the stochastic block model, finding substantial departures from classical results for sparse graphs.
