Search Results for author: Renjie Liao

Found 57 papers, 34 papers with code

GEMTrans: A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosis

1 code implementation25 Aug 2023 Masoud Mokhtari, Neda Ahmadi, Teresa S. M. Tsang, Purang Abolmaesumi, Renjie Liao

Due to inter-observer variability in echo-based diagnosis, which arises from the variability in echo image acquisition and the interpretation of echo images based on clinical experience, vision-based machine learning (ML) methods have gained popularity to act as secondary layers of verification.

EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms

1 code implementation23 Jul 2023 Masoud Mokhtari, Mobina Mahdavi, Hooman Vaseli, Christina Luong, Purang Abolmaesumi, Teresa S. M. Tsang, Renjie Liao

To address this challenge, we introduce an echocardiogram-based, hierarchical graph neural network (GNN) for left ventricle landmark detection (EchoGLAD).

Graph Representation Learning

SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation

1 code implementation4 Jul 2023 Qi Yan, Zhengyang Liang, Yang song, Renjie Liao, Lele Wang

Diffusion models based on permutation-equivariant networks can learn permutation-invariant distributions for graph data.

Denoising Graph Generation

Memorization Capacity of Multi-Head Attention in Transformers

1 code implementation3 Jun 2023 Sadegh Mahdavi, Renjie Liao, Christos Thrampoulidis

Transformers have become the go-to architecture for language and vision tasks, yet their theoretical properties, especially memorization capacity, remain elusive.

Image Classification Memorization

Specformer: Spectral Graph Neural Networks Meet Transformers

1 code implementation2 Mar 2023 Deyu Bo, Chuan Shi, Lele Wang, Renjie Liao

To tackle these issues, we introduce Specformer, which effectively encodes the set of all eigenvalues and performs self-attention in the spectral domain, leading to a learnable set-to-set spectral filter.

Self-Supervised Relation Alignment for Scene Graph Generation

no code implementations2 Feb 2023 Bicheng Xu, Renjie Liao, Leonid Sigal

In the auxiliary branch, relational input features are partially masked prior to message passing and predicate prediction.

Graph Generation Scene Graph Generation

GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Models

no code implementations28 Nov 2022 Muchen Li, Jeffrey Yunfan Liu, Leonid Sigal, Renjie Liao

Moreover, our graph generator leads to a learnable probabilistic search method that is more flexible and efficient than the commonly used RNN generator and random search methods.

Neural Architecture Search

Learning Latent Part-Whole Hierarchies for Point Clouds

no code implementations14 Nov 2022 Xiang Gao, Wei Hu, Renjie Liao

The decoder takes the latent variable and the feature from the encoder as an input and predicts the per-point part distribution at the top level.

Point Cloud Segmentation Segmentation

Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks

1 code implementation1 Nov 2022 Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao

In this paper, we study the OOD generalization of neural algorithmic reasoning tasks, where the goal is to learn an algorithm (e. g., sorting, breadth-first search, and depth-first search) from input-output pairs using deep neural networks.

Data Augmentation Out-of-Distribution Generalization

VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge

1 code implementation24 Oct 2022 Sahithya Ravi, Aditya Chinchure, Leonid Sigal, Renjie Liao, Vered Shwartz

In contrast to previous methods which inject knowledge from static knowledge bases, we investigate the incorporation of contextualized knowledge using Commonsense Transformer (COMET), an existing knowledge model trained on human-curated knowledge bases.

Ranked #5 on Visual Question Answering (VQA) on A-OKVQA (DA VQA Score metric)

Question Answering Visual Question Answering

Gaussian-Bernoulli RBMs Without Tears

1 code implementation19 Oct 2022 Renjie Liao, Simon Kornblith, Mengye Ren, David J. Fleet, Geoffrey Hinton

We revisit the challenging problem of training Gaussian-Bernoulli restricted Boltzmann machines (GRBMs), introducing two innovations.

Scaling Forward Gradient With Local Losses

1 code implementation7 Oct 2022 Mengye Ren, Simon Kornblith, Renjie Liao, Geoffrey Hinton

Forward gradient learning computes a noisy directional gradient and is a biologically plausible alternative to backprop for learning deep neural networks.

EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks

1 code implementation30 Aug 2022 Masoud Mokhtari, Teresa Tsang, Purang Abolmaesumi, Renjie Liao

In this work, we introduce EchoGNN, a model based on graph neural networks (GNNs) to estimate EF from echo videos.

NP-DRAW: A Non-Parametric Structured Latent Variable Model for Image Generation

1 code implementation25 Jun 2021 Xiaohui Zeng, Raquel Urtasun, Richard Zemel, Sanja Fidler, Renjie Liao

1) We propose a non-parametric prior distribution over the appearance of image parts so that the latent variable ``what-to-draw'' per step becomes a categorical random variable.

Image Generation

LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving

no code implementations ICCV 2021 Alexander Cui, Sergio Casas, Abbas Sadat, Renjie Liao, Raquel Urtasun

In this paper, we present LookOut, a novel autonomy system that perceives the environment, predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of the SDV by optimizing a set of contingency plans over these future realizations.

Future prediction Motion Forecasting

Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting

no code implementations7 Jan 2021 Katie Luo, Sergio Casas, Renjie Liao, Xinchen Yan, Yuwen Xiong, Wenyuan Zeng, Raquel Urtasun

On two large-scale real-world datasets, nuScenes and ATG4D, we showcase that our scene-occupancy predictions are more accurate and better calibrated than those from state-of-the-art motion forecasting methods, while also matching their performance in pedestrian motion forecasting metrics.

Motion Forecasting

A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks

no code implementations ICLR 2021 Renjie Liao, Raquel Urtasun, Richard Zemel

In this paper, we derive generalization bounds for the two primary classes of graph neural networks (GNNs), namely graph convolutional networks (GCNs) and message passing GNNs (MPGNNs), via a PAC-Bayesian approach.

Generalization Bounds

GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation

2 code implementations13 Dec 2020 Xiaojuan Qi, Zhengzhe Liu, Renjie Liao, Philip H. S. Torr, Raquel Urtasun, Jiaya Jia

Note that GeoNet++ is generic and can be used in other depth/normal prediction frameworks to improve the quality of 3D reconstruction and pixel-wise accuracy of depth and surface normals.

3D Reconstruction Depth Estimation +2

DSDNet: Deep Structured self-Driving Network

no code implementations ECCV 2020 Wenyuan Zeng, Shenlong Wang, Renjie Liao, Yun Chen, Bin Yang, Raquel Urtasun

In this paper, we propose the Deep Structured self-Driving Network (DSDNet), which performs object detection, motion prediction, and motion planning with a single neural network.

Motion Planning motion prediction +2

Learning Lane Graph Representations for Motion Forecasting

1 code implementation ECCV 2020 Ming Liang, Bin Yang, Rui Hu, Yun Chen, Renjie Liao, Song Feng, Raquel Urtasun

We propose a motion forecasting model that exploits a novel structured map representation as well as actor-map interactions.

Motion Forecasting Trajectory Prediction

Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

no code implementations ECCV 2020 Sergio Casas, Cole Gulino, Simon Suo, Katie Luo, Renjie Liao, Raquel Urtasun

In order to plan a safe maneuver an autonomous vehicle must accurately perceive its environment, and understand the interactions among traffic participants.

Motion Forecasting Motion Planning

Latent Variable Modelling with Hyperbolic Normalizing Flows

1 code implementation ICML 2020 Avishek Joey Bose, Ariella Smofsky, Renjie Liao, Prakash Panangaden, William L. Hamilton

One effective solution is the use of normalizing flows \cut{defined on Euclidean spaces} to construct flexible posterior distributions.

Density Estimation Variational Inference

Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving

1 code implementation10 Feb 2020 Yang Song, Chenlin Meng, Renjie Liao, Stefano Ermon

Feedforward computation, such as evaluating a neural network or sampling from an autoregressive model, is ubiquitous in machine learning.


no code implementations25 Sep 2019 Xin Zhang, Weixiao Huang, Renjie Liao, Yanhua Li

Imitation learning aims to inversely learn a policy from expert demonstrations, which has been extensively studied in the literature for both single-agent setting with Markov decision process (MDP) model, and multi-agent setting with Markov game (MG) model.

Imitation Learning

LanczosNet: Multi-Scale Deep Graph Convolutional Networks

1 code implementation ICLR 2019 Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard S. Zemel

We propose the Lanczos network (LanczosNet), which uses the Lanczos algorithm to construct low rank approximations of the graph Laplacian for graph convolution.

Node Classification

Incremental Few-Shot Learning with Attention Attractor Networks

1 code implementation NeurIPS 2019 Mengye Ren, Renjie Liao, Ethan Fetaya, Richard S. Zemel

This paper addresses this problem, incremental few-shot learning, where a regular classification network has already been trained to recognize a set of base classes, and several extra novel classes are being considered, each with only a few labeled examples.

Few-Shot Learning General Classification

Inference in Probabilistic Graphical Models by Graph Neural Networks

1 code implementation21 Mar 2018 KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow

Message-passing algorithms, such as belief propagation, are a natural way to disseminate evidence amongst correlated variables while exploiting the graph structure, but these algorithms can struggle when the conditional dependency graphs contain loops.

Decision Making

Reviving and Improving Recurrent Back-Propagation

1 code implementation ICML 2018 Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel

We examine all RBP variants along with BPTT and TBPTT in three different application domains: associative memory with continuous Hopfield networks, document classification in citation networks using graph neural networks and hyperparameter optimization for fully connected networks.

Document Classification Hyperparameter Optimization

Learning deep structured active contours end-to-end

2 code implementations CVPR 2018 Diego Marcos, Devis Tuia, Benjamin Kellenberger, Lisa Zhang, Min Bai, Renjie Liao, Raquel Urtasun

The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications.

Instance Segmentation Segmentation +1

Understanding Short-Horizon Bias in Stochastic Meta-Optimization

1 code implementation ICLR 2018 Yuhuai Wu, Mengye Ren, Renjie Liao, Roger Grosse

Careful tuning of the learning rate, or even schedules thereof, can be crucial to effective neural net training.

3D Graph Neural Networks for RGBD Semantic Segmentation

2 code implementations ICCV 2017 Xiaojuan Qi, Renjie Liao, Jiaya Jia, Sanja Fidler, Raquel Urtasun

Each node in the graph corresponds to a set of points and is associated with a hidden representation vector initialized with an appearance feature extracted by a unary CNN from 2D images.

RGBD Semantic Segmentation Semantic Segmentation

Video Super-Resolution via Deep Draft-Ensemble Learning

no code implementations ICCV 2015 Renjie Liao, Xin Tao, Ruiyu Li, Ziyang Ma, Jiaya Jia

We propose a new direction for fast video super-resolution (VideoSR) via a SR draft ensemble, which is defined as the set of high-resolution patch candidates before final image deconvolution.

Ensemble Learning Image Deconvolution +1

Learning to Generate Images with Perceptual Similarity Metrics

1 code implementation19 Nov 2015 Jake Snell, Karl Ridgeway, Renjie Liao, Brett D. Roads, Michael C. Mozer, Richard S. Zemel

We propose instead to use a loss function that is better calibrated to human perceptual judgments of image quality: the multiscale structural-similarity score (MS-SSIM).

Image Classification Image Generation +3

Bounded-Distortion Metric Learning

no code implementations10 May 2015 Renjie Liao, Jianping Shi, Ziyang Ma, Jun Zhu, Jiaya Jia

Metric learning aims to embed one metric space into another to benefit tasks like classification and clustering.

Clustering General Classification +1

Learning Important Spatial Pooling Regions for Scene Classification

no code implementations CVPR 2014 Di Lin, Cewu Lu, Renjie Liao, Jiaya Jia

We address the false response influence problem when learning and applying discriminative parts to construct the mid-level representation in scene classification.

Classification General Classification +1

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