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08/25/2023
GEMTrans: A General, Echocardiography-based, Multi-Level Transformer Framework for Cardiovascular Diagnosis
Echocardiography (echo) is an ultrasound imaging modality that is widely...
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07/23/2023
EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms
The functional assessment of the left ventricle chamber of the heart req...
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06/03/2023
Memorization Capacity of Multi-Head Attention in Transformers
In this paper, we investigate the memorization capabilities of multi-hea...
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03/02/2023
Specformer: Spectral Graph Neural Networks Meet Transformers
Spectral graph neural networks (GNNs) learn graph representations via sp...
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02/02/2023
Self-Supervised Relation Alignment for Scene Graph Generation
The goal of scene graph generation is to predict a graph from an input i...
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11/28/2022
GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Models
Neural architectures can be naturally viewed as computational graphs. Mo...
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10/24/2022
VLC-BERT: Visual Question Answering with Contextualized Commonsense Knowledge
There has been a growing interest in solving Visual Question Answering (...
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10/19/2022
Gaussian-Bernoulli RBMs Without Tears
We revisit the challenging problem of training Gaussian-Bernoulli restri...
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10/07/2022
Scaling Forward Gradient With Local Losses
Forward gradient learning computes a noisy directional gradient and is a...
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08/30/2022
EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks
Ejection fraction (EF) is a key indicator of cardiac function, allowing ...
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07/20/2022
NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Clouds
We introduce a method for instance proposal generation for 3D point clou...
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06/25/2021
NP-DRAW: A Non-Parametric Structured Latent Variable Modelfor Image Generation
In this paper, we present a non-parametric structured latent variable mo...
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01/17/2021
LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting
Forecasting the future behaviors of dynamic actors is an important task ...
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01/16/2021
LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving
Self-driving vehicles need to anticipate a diverse set of future traffic...
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01/07/2021
Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting
In this paper, we address the important problem in self-driving of forec...
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12/14/2020
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks
In this paper, we derive generalization bounds for the two primary class...
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12/13/2020
GeoNet++: Iterative Geometric Neural Network with Edge-Aware Refinement for Joint Depth and Surface Normal Estimation
In this paper, we propose a geometric neural network with edge-aware ref...
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08/13/2020
DSDNet: Deep Structured self-Driving Network
In this paper, we propose the Deep Structured self-Driving Network (DSDN...
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08/13/2020
Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction
We present a novel method for testing the safety of self-driving vehicle...
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07/27/2020
Learning Lane Graph Representations for Motion Forecasting
We propose a motion forecasting model that exploits a novel structured m...
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07/23/2020
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
In order to plan a safe maneuver an autonomous vehicle must accurately p...
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06/21/2020
Fast and Accurate: Structure Coherence Component for Face Alignment
In this paper, we propose a fast and accurate coordinate regression meth...
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02/15/2020
Latent Variable Modelling with Hyperbolic Normalizing Flows
The choice of approximate posterior distributions plays a central role i...
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02/10/2020
Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation
Feedforward computations, such as evaluating a neural network or samplin...
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10/18/2019
Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data
In this paper, we tackle the problem of relational behavior forecasting ...
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10/17/2019
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
Self-driving vehicles plan around both static and dynamic objects, apply...
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10/02/2019
Efficient Graph Generation with Graph Recurrent Attention Networks
We propose a new family of efficient and expressive deep generative mode...
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09/27/2019
DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation
In this paper, we propose the differentiable mask-matching network (DMM-...
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07/30/2019
Deformable Filter Convolution for Point Cloud Reasoning
Point clouds are the native output of many real-world 3D sensors. To bor...
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06/22/2019
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
We introduce a new molecular dataset, named Alchemy, for developing mach...
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05/15/2019
DARNet: Deep Active Ray Network for Building Segmentation
In this paper, we propose a Deep Active Ray Network (DARNet) for automat...
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01/12/2019
UPSNet: A Unified Panoptic Segmentation Network
In this paper, we propose a unified panoptic segmentation network (UPSNe...
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01/06/2019
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
We propose the Lanczos network (LanczosNet), which uses the Lanczos algo...
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10/16/2018
Incremental Few-Shot Learning with Attention Attractor Networks
Machine learning classifiers are often trained to recognize a set of pre...
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09/08/2018
Neural Guided Constraint Logic Programming for Program Synthesis
Synthesizing programs using example input/outputs is a classic problem i...
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03/21/2018
Inference in Probabilistic Graphical Models by Graph Neural Networks
A useful computation when acting in a complex environment is to infer th...
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03/16/2018
Reviving and Improving Recurrent Back-Propagation
In this paper, we revisit the recurrent back-propagation (RBP) algorithm...
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03/16/2018
Learning deep structured active contours end-to-end
The world is covered with millions of buildings, and precisely knowing e...
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03/16/2018
Graph Partition Neural Networks for Semi-Supervised Classification
We present graph partition neural networks (GPNN), an extension of graph...
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03/06/2018
Understanding Short-Horizon Bias in Stochastic Meta-Optimization
Careful tuning of the learning rate, or even schedules thereof, can be c...
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08/14/2017
Situation Recognition with Graph Neural Networks
We address the problem of recognizing situations in images. Given an ima...
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04/10/2017
Detail-revealing Deep Video Super-resolution
Previous CNN-based video super-resolution approaches need to align multi...
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11/14/2016
