Search Results for author: Ziyu Wang

Found 55 papers, 28 papers with code

NeReF: Neural Refractive Field for Fluid Surface Reconstruction and Implicit Representation

no code implementations8 Mar 2022 Ziyu Wang, Wei Yang, Junming Cao, Lan Xu, Junqing Yu, Jingyi Yu

We present a novel neural refractive field(NeReF) to recover wavefront of transparent fluids by simultaneously estimating the surface position and normal of the fluid front.

Surface Reconstruction

DynaMixer: A Vision MLP Architecture with Dynamic Mixing

1 code implementation28 Jan 2022 Ziyu Wang, Wenhao Jiang, Yiming Zhu, Li Yuan, Yibing Song, Wei Liu

In contrast with vision transformers and CNNs, the success of MLP-like models shows that simple information fusion operations among tokens and channels can yield a good representation power for deep recognition models.

Image Classification

C$^2$SP-Net: Joint Compression and Classification Network for Epilepsy Seizure Prediction

no code implementations26 Oct 2021 Di wu, Yi Shi, Ziyu Wang, Jie Yang, Mohamad Sawan

Although compressive sensing (CS) can be adopted to compress the signals to reduce communication bandwidth requirement, it needs a complex reconstruction procedure before the signal can be used for seizure prediction.

Compressive Sensing Seizure prediction

iButter: Neural Interactive Bullet Time Generator for Human Free-viewpoint Rendering

no code implementations12 Aug 2021 Liao Wang, Ziyu Wang, Pei Lin, Yuheng Jiang, Xin Suo, Minye Wu, Lan Xu, Jingyi Yu

To fill this gap, in this paper we propose a neural interactive bullet-time generator (iButter) for photo-realistic human free-viewpoint rendering from dense RGB streams, which enables flexible and interactive design for human bullet-time visual effects.

Video Generation

Quasi-Bayesian Dual Instrumental Variable Regression

1 code implementation NeurIPS 2021 Ziyu Wang, Yuhao Zhou, Tongzheng Ren, Jun Zhu

Recent years have witnessed an upsurge of interest in employing flexible machine learning models for instrumental variable (IV) regression, but the development of uncertainty quantification methodology is still lacking.

Bayesian Inference

Scalable Quasi-Bayesian Inference for Instrumental Variable Regression

no code implementations NeurIPS 2021 Ziyu Wang, Yuhao Zhou, Tongzheng Ren, Jun Zhu

Recent years have witnessed an upsurge of interest in employing flexible machine learning models for instrumental variable (IV) regression, but the development of uncertainty quantification methodology is still lacking.

Bayesian Inference

A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction

no code implementations5 May 2021 Ziyu Wang, Jie Yang, Mohamad Sawan

Accurate prediction of epileptic seizures allows patients to take preventive measures in advance to avoid possible injuries.

EEG Seizure prediction

Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization

no code implementations ICLR 2021 Michael R. Zhang, Tom Le Paine, Ofir Nachum, Cosmin Paduraru, George Tucker, Ziyu Wang, Mohammad Norouzi

This modeling choice assumes that different dimensions of the next state and reward are conditionally independent given the current state and action and may be driven by the fact that fully observable physics-based simulation environments entail deterministic transition dynamics.

Continuous Control Data Augmentation

MirrorNeRF: One-shot Neural Portrait Radiance Field from Multi-mirror Catadioptric Imaging

no code implementations6 Apr 2021 Ziyu Wang, Liao Wang, Fuqiang Zhao, Minye Wu, Lan Xu, Jingyi Yu

In this paper, we propose MirrorNeRF - a one-shot neural portrait free-viewpoint rendering approach using a catadioptric imaging system with multiple sphere mirrors and a single high-resolution digital camera, which is the first to combine neural radiance field with catadioptric imaging so as to enable one-shot photo-realistic human portrait reconstruction and rendering, in a low-cost and casual capture setting.

Benchmarks for Deep Off-Policy Evaluation

3 code implementations ICLR 2021 Justin Fu, Mohammad Norouzi, Ofir Nachum, George Tucker, Ziyu Wang, Alexander Novikov, Mengjiao Yang, Michael R. Zhang, Yutian Chen, Aviral Kumar, Cosmin Paduraru, Sergey Levine, Tom Le Paine

Off-policy evaluation (OPE) holds the promise of being able to leverage large, offline datasets for both evaluating and selecting complex policies for decision making.

Continuous Control Decision Making +1

Regularized Behavior Value Estimation

no code implementations17 Mar 2021 Caglar Gulcehre, Sergio Gómez Colmenarejo, Ziyu Wang, Jakub Sygnowski, Thomas Paine, Konrad Zolna, Yutian Chen, Matthew Hoffman, Razvan Pascanu, Nando de Freitas

Due to bootstrapping, these errors get amplified during training and can lead to divergence, thereby crippling learning.

Offline RL

Addressing Extrapolation Error in Deep Offline Reinforcement Learning

no code implementations1 Jan 2021 Caglar Gulcehre, Sergio Gómez Colmenarejo, Ziyu Wang, Jakub Sygnowski, Thomas Paine, Konrad Zolna, Yutian Chen, Matthew Hoffman, Razvan Pascanu, Nando de Freitas

These errors can be compounded by bootstrapping when the function approximator overestimates, leading the value function to *grow unbounded*, thereby crippling learning.

Offline RL reinforcement-learning

Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings

1 code implementation14 Dec 2020 Qipeng Guo, Zhijing Jin, Ziyu Wang, Xipeng Qiu, Weinan Zhang, Jun Zhu, Zheng Zhang, David Wipf

Cycle-consistent training is widely used for jointly learning a forward and inverse mapping between two domains of interest without the cumbersome requirement of collecting matched pairs within each domain.

Knowledge Graphs Text Generation

RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning

1 code implementation NeurIPS 2020 Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Thomas Paine, Sergio Gómez, Konrad Zolna, Rishabh Agarwal, Josh S. Merel, Daniel J. Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matthew Hoffman, Nicolas Heess, Nando de Freitas

We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community.

Offline RL reinforcement-learning

Offline Learning from Demonstrations and Unlabeled Experience

no code implementations27 Nov 2020 Konrad Zolna, Alexander Novikov, Ksenia Konyushkova, Caglar Gulcehre, Ziyu Wang, Yusuf Aytar, Misha Denil, Nando de Freitas, Scott Reed

Behavior cloning (BC) is often practical for robot learning because it allows a policy to be trained offline without rewards, by supervised learning on expert demonstrations.

Continuous Control Imitation Learning +1

Further Analysis of Outlier Detection with Deep Generative Models

1 code implementation NeurIPS 2020 Ziyu Wang, Bin Dai, David Wipf, Jun Zhu

The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applications as well as our overall understanding of generative modeling.

Outlier Detection

POP909: A Pop-song Dataset for Music Arrangement Generation

1 code implementation17 Aug 2020 Ziyu Wang, Ke Chen, Junyan Jiang, Yiyi Zhang, Maoran Xu, Shuqi Dai, Xianbin Gu, Gus Xia

The main body of the dataset contains the vocal melody, the lead instrument melody, and the piano accompaniment for each song in MIDI format, which are aligned to the original audio files.

Music Generation

Learning Interpretable Representation for Controllable Polyphonic Music Generation

2 code implementations17 Aug 2020 Ziyu Wang, Dingsu Wang, Yixiao Zhang, Gus Xia

While deep generative models have become the leading methods for algorithmic composition, it remains a challenging problem to control the generation process because the latent variables of most deep-learning models lack good interpretability.

Disentanglement Music Generation +1

PIANOTREE VAE: Structured Representation Learning for Polyphonic Music

2 code implementations17 Aug 2020 Ziyu Wang, Yiyi Zhang, Yixiao Zhang, Junyan Jiang, Ruihan Yang, Junbo Zhao, Gus Xia

The dominant approach for music representation learning involves the deep unsupervised model family variational autoencoder (VAE).

Music Generation Representation Learning

Hyperparameter Selection for Offline Reinforcement Learning

no code implementations17 Jul 2020 Tom Le Paine, Cosmin Paduraru, Andrea Michi, Caglar Gulcehre, Konrad Zolna, Alexander Novikov, Ziyu Wang, Nando de Freitas

Therefore, in this work, we focus on \textit{offline hyperparameter selection}, i. e. methods for choosing the best policy from a set of many policies trained using different hyperparameters, given only logged data.

Offline RL reinforcement-learning

Critic Regularized Regression

3 code implementations NeurIPS 2020 Ziyu Wang, Alexander Novikov, Konrad Zolna, Jost Tobias Springenberg, Scott Reed, Bobak Shahriari, Noah Siegel, Josh Merel, Caglar Gulcehre, Nicolas Heess, Nando de Freitas

Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction.

Offline RL reinforcement-learning

RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning

2 code implementations24 Jun 2020 Caglar Gulcehre, Ziyu Wang, Alexander Novikov, Tom Le Paine, Sergio Gomez Colmenarejo, Konrad Zolna, Rishabh Agarwal, Josh Merel, Daniel Mankowitz, Cosmin Paduraru, Gabriel Dulac-Arnold, Jerry Li, Mohammad Norouzi, Matt Hoffman, Ofir Nachum, George Tucker, Nicolas Heess, Nando de Freitas

We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community.

Atari Games DQN Replay Dataset +2

Acme: A Research Framework for Distributed Reinforcement Learning

2 code implementations1 Jun 2020 Matt Hoffman, Bobak Shahriari, John Aslanides, Gabriel Barth-Maron, Feryal Behbahani, Tamara Norman, Abbas Abdolmaleki, Albin Cassirer, Fan Yang, Kate Baumli, Sarah Henderson, Alex Novikov, Sergio Gómez Colmenarejo, Serkan Cabi, Caglar Gulcehre, Tom Le Paine, Andrew Cowie, Ziyu Wang, Bilal Piot, Nando de Freitas

Ultimately, we show that the design decisions behind Acme lead to agents that can be scaled both up and down and that, for the most part, greater levels of parallelization result in agents with equivalent performance, just faster.

DQN Replay Dataset reinforcement-learning

A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models

1 code implementation pproximateinference AABI Symposium 2019 Ziyu Wang, Shuyu Cheng, Yueru Li, Jun Zhu, Bo Zhang

Score matching provides an effective approach to learning flexible unnormalized models, but its scalability is limited by the need to evaluate a second-order derivative.

Incentive Analysis of Bitcoin-NG, Revisited

no code implementations14 Jan 2020 Jianyu Niu, Ziyu Wang, Fangyu Gai, Chen Feng

First, we propose a new incentive analysis that takes the network capacity into account, showing that Bitcoin-NG can still maintain incentive compatibility against the microblock mining attack even under limited network capacity.

Cryptography and Security Distributed, Parallel, and Cluster Computing

The Usual Suspects? Reassessing Blame for VAE Posterior Collapse

no code implementations ICML 2020 Bin Dai, Ziyu Wang, David Wipf

In narrow asymptotic settings Gaussian VAE models of continuous data have been shown to possess global optima aligned with ground-truth distributions.

Task-Relevant Adversarial Imitation Learning

no code implementations2 Oct 2019 Konrad Zolna, Scott Reed, Alexander Novikov, Sergio Gomez Colmenarejo, David Budden, Serkan Cabi, Misha Denil, Nando de Freitas, Ziyu Wang

We show that a critical vulnerability in adversarial imitation is the tendency of discriminator networks to learn spurious associations between visual features and expert labels.

Imitation Learning

Making Efficient Use of Demonstrations to Solve Hard Exploration Problems

1 code implementation ICLR 2020 Tom Le Paine, Caglar Gulcehre, Bobak Shahriari, Misha Denil, Matt Hoffman, Hubert Soyer, Richard Tanburn, Steven Kapturowski, Neil Rabinowitz, Duncan Williams, Gabriel Barth-Maron, Ziyu Wang, Nando de Freitas, Worlds Team

This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions.

Deep Music Analogy Via Latent Representation Disentanglement

3 code implementations9 Jun 2019 Ruihan Yang, Dingsu Wang, Ziyu Wang, Tianyao Chen, Junyan Jiang, Gus Xia

Analogy-making is a key method for computer algorithms to generate both natural and creative music pieces.

Disentanglement

Visual Imitation with a Minimal Adversary

no code implementations ICLR 2019 Scott Reed, Yusuf Aytar, Ziyu Wang, Tom Paine, Aäron van den Oord, Tobias Pfaff, Sergio Gomez, Alexander Novikov, David Budden, Oriol Vinyals

The proposed agent can solve a challenging robot manipulation task of block stacking from only video demonstrations and sparse reward, in which the non-imitating agents fail to learn completely.

Imitation Learning

Function Space Particle Optimization for Bayesian Neural Networks

1 code implementation ICLR 2019 Ziyu Wang, Tongzheng Ren, Jun Zhu, Bo Zhang

While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and over-parameterized nature.

reinforcement-learning Variational Inference

A Framework for Automated Pop-song Melody Generation with Piano Accompaniment Arrangement

no code implementations28 Dec 2018 Ziyu Wang, Gus Xia

Second, the melody generation model generates the lead melody and other voices (melody lines) of the accompaniment using seasonal ARMA (Autoregressive Moving Average) processes.

Bayesian Optimization in AlphaGo

no code implementations17 Dec 2018 Yutian Chen, Aja Huang, Ziyu Wang, Ioannis Antonoglou, Julian Schrittwieser, David Silver, Nando de Freitas

During the development of AlphaGo, its many hyper-parameters were tuned with Bayesian optimization multiple times.

One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL

no code implementations ICLR 2019 Tom Le Paine, Sergio Gómez Colmenarejo, Ziyu Wang, Scott Reed, Yusuf Aytar, Tobias Pfaff, Matt W. Hoffman, Gabriel Barth-Maron, Serkan Cabi, David Budden, Nando de Freitas

MetaMimic can learn both (i) policies for high-fidelity one-shot imitation of diverse novel skills, and (ii) policies that enable the agent to solve tasks more efficiently than the demonstrators.

Playing hard exploration games by watching YouTube

1 code implementation NeurIPS 2018 Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, Nando de Freitas

One successful method of guiding exploration in these domains is to imitate trajectories provided by a human demonstrator.

Montezuma's Revenge

Robust Imitation of Diverse Behaviors

no code implementations NeurIPS 2017 Ziyu Wang, Josh Merel, Scott Reed, Greg Wayne, Nando de Freitas, Nicolas Heess

Compared to purely supervised methods, Generative Adversarial Imitation Learning (GAIL) can learn more robust controllers from fewer demonstrations, but is inherently mode-seeking and more difficult to train.

Imitation Learning

Learning human behaviors from motion capture by adversarial imitation

1 code implementation7 Jul 2017 Josh Merel, Yuval Tassa, Dhruva TB, Sriram Srinivasan, Jay Lemmon, Ziyu Wang, Greg Wayne, Nicolas Heess

Rapid progress in deep reinforcement learning has made it increasingly feasible to train controllers for high-dimensional humanoid bodies.

Imitation Learning reinforcement-learning

Emergence of Locomotion Behaviours in Rich Environments

6 code implementations7 Jul 2017 Nicolas Heess, Dhruva TB, Srinivasan Sriram, Jay Lemmon, Josh Merel, Greg Wayne, Yuval Tassa, Tom Erez, Ziyu Wang, S. M. Ali Eslami, Martin Riedmiller, David Silver

The reinforcement learning paradigm allows, in principle, for complex behaviours to be learned directly from simple reward signals.

reinforcement-learning

Parallel Multiscale Autoregressive Density Estimation

no code implementations ICML 2017 Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, Nando de Freitas

Our new PixelCNN model achieves competitive density estimation and orders of magnitude speedup - O(log N) sampling instead of O(N) - enabling the practical generation of 512x512 images.

Conditional Image Generation Density Estimation +2

Sample Efficient Actor-Critic with Experience Replay

8 code implementations3 Nov 2016 Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos, Koray Kavukcuoglu, Nando de Freitas

This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems.

Continuous Control reinforcement-learning

Deep Fried Convnets

1 code implementation ICCV 2015 Zichao Yang, Marcin Moczulski, Misha Denil, Nando de Freitas, Alex Smola, Le Song, Ziyu Wang

The fully connected layers of a deep convolutional neural network typically contain over 90% of the network parameters, and consume the majority of the memory required to store the network parameters.

Image Classification

Bayesian Optimisation for Machine Translation

no code implementations22 Dec 2014 Yishu Miao, Ziyu Wang, Phil Blunsom

This paper presents novel Bayesian optimisation algorithms for minimum error rate training of statistical machine translation systems.

Bayesian Optimisation Machine Translation +1

Heteroscedastic Treed Bayesian Optimisation

no code implementations27 Oct 2014 John-Alexander M. Assael, Ziyu Wang, Bobak Shahriari, Nando de Freitas

At the core of this approach is a Gaussian process prior that captures our belief about the distribution over functions.

Bayesian Optimisation

Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters

1 code implementation30 Jun 2014 Ziyu Wang, Nando de Freitas

Bayesian optimisation has gained great popularity as a tool for optimising the parameters of machine learning algorithms and models.

Bayesian Optimisation Gaussian Processes

An Entropy Search Portfolio for Bayesian Optimization

no code implementations18 Jun 2014 Bobak Shahriari, Ziyu Wang, Matthew W. Hoffman, Alexandre Bouchard-Côté, Nando de Freitas

How- ever, the performance of a Bayesian optimization method very much depends on its exploration strategy, i. e. the choice of acquisition function, and it is not clear a priori which choice will result in superior performance.

Bayesian Multi-Scale Optimistic Optimization

no code implementations27 Feb 2014 Ziyu Wang, Babak Shakibi, Lin Jin, Nando de Freitas

In this paper, we introduce a new technique for efficient global optimization that combines Gaussian process confidence bounds and treed simultaneous optimistic optimization to eliminate the need for auxiliary optimization of acquisition functions.

Gaussian Processes

Bayesian Optimization in a Billion Dimensions via Random Embeddings

1 code implementation9 Jan 2013 Ziyu Wang, Frank Hutter, Masrour Zoghi, David Matheson, Nando de Freitas

Bayesian optimization techniques have been successfully applied to robotics, planning, sensor placement, recommendation, advertising, intelligent user interfaces and automatic algorithm configuration.

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