Search Results for author: Tianchen Zhao

Found 17 papers, 6 papers with code

Scalable neural quantum states architecture for quantum chemistry

no code implementations11 Aug 2022 Tianchen Zhao, James Stokes, Shravan Veerapaneni

Variational optimization of neural-network representations of quantum states has been successfully applied to solve interacting fermionic problems.

CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance

no code implementations CVPR 2022 Tianchen Zhao, Niansong Zhang, Xuefei Ning, He Wang, Li Yi, Yu Wang

We propose CodedVTR (Codebook-based Voxel TRansformer), which improves data efficiency and generalization ability for 3D sparse voxel transformers.

3D Semantic Segmentation

Explore the Potential of CNN Low Bit Training

no code implementations1 Jan 2021 Kai Zhong, Xuefei Ning, Tianchen Zhao, Zhenhua Zhu, Shulin Zeng, Guohao Dai, Yu Wang, Huazhong Yang

Through this dynamic precision framework, we can reduce the bit-width of convolution, which is the most computational cost, while keeping the training process close to the full precision floating-point training.

Quantization

Discovering Robust Convolutional Architecture at Targeted Capacity: A Multi-Shot Approach

1 code implementation22 Dec 2020 Xuefei Ning, Junbo Zhao, Wenshuo Li, Tianchen Zhao, Yin Zheng, Huazhong Yang, Yu Wang

In this paper, considering scenarios with capacity budget, we aim to discover adversarially robust architecture at targeted capacities.

Neural Architecture Search

Learning Self-Consistency for Deepfake Detection

1 code implementation ICCV 2021 Tianchen Zhao, Xiang Xu, Mingze Xu, Hui Ding, Yuanjun Xiong, Wei Xia

We propose a new method to detect deepfake images using the cue of the source feature inconsistency within the forged images.

DeepFake Detection Face Swapping +2

aw_nas: A Modularized and Extensible NAS framework

1 code implementation25 Nov 2020 Xuefei Ning, Changcheng Tang, Wenshuo Li, Songyi Yang, Tianchen Zhao, Niansong Zhang, Tianyi Lu, Shuang Liang, Huazhong Yang, Yu Wang

Neural Architecture Search (NAS) has received extensive attention due to its capability to discover neural network architectures in an automated manner.

Adversarial Robustness Neural Architecture Search

Meta Variational Monte Carlo

no code implementations20 Nov 2020 Tianchen Zhao, James Stokes, Oliver Knitter, Brian Chen, Shravan Veerapaneni

An identification is found between meta-learning and the problem of determining the ground state of a randomly generated Hamiltonian drawn from a known ensemble.

Meta-Learning Variational Monte Carlo

Exploring the Potential of Low-bit Training of Convolutional Neural Networks

no code implementations4 Jun 2020 Kai Zhong, Xuefei Ning, Guohao Dai, Zhenhua Zhu, Tianchen Zhao, Shulin Zeng, Yu Wang, Huazhong Yang

For training a variety of models on CIFAR-10, using 1-bit mantissa and 2-bit exponent is adequate to keep the accuracy loss within $1\%$.

Quantization

Natural evolution strategies and variational Monte Carlo

1 code implementation9 May 2020 Tianchen Zhao, Giuseppe Carleo, James Stokes, Shravan Veerapaneni

A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization.

Combinatorial Optimization Variational Monte Carlo

DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation

1 code implementation ECCV 2020 Xuefei Ning, Tianchen Zhao, Wenshuo Li, Peng Lei, Yu Wang, Huazhong Yang

In budgeted pruning, how to distribute the resources across layers (i. e., sparsity allocation) is the key problem.

INFORMATION MAXIMIZATION AUTO-ENCODING

no code implementations ICLR 2019 Dejiao Zhang, Tianchen Zhao, Laura Balzano

Unlike the Variational Autoencoder framework, IMAE starts from a stochastic encoder that seeks to map each input data to a hybrid discrete and continuous representation with the objective of maximizing the mutual information between the data and their representations.

Disentanglement Informativeness

Diversity-Sensitive Conditional Generative Adversarial Networks

no code implementations ICLR 2019 Dingdong Yang, Seunghoon Hong, Yunseok Jang, Tianchen Zhao, Honglak Lee

We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN).

Image Inpainting Image-to-Image Translation +2

Information Theoretic Interpretation of Deep learning

no code implementations21 Mar 2018 Tianchen Zhao

We interpret part of the experimental results of Shwartz-Ziv and Tishby [2017].

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