
AttentionRNN: A Structured Spatial Attention Mechanism
Visual attention mechanisms have proven to be integrally important const...
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AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks
Recurrent neural networks have gained widespread use in modeling sequent...
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A Preliminary Study of Disentanglement With Insights on the Inadequacy of Metrics
Disentangled encoding is an important step towards a better representati...
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Building Ethics into Artificial Intelligence
As artificial intelligence (AI) systems become increasingly ubiquitous, ...
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Imitation Learning of Factored Multiagent Reactive Models
We apply recent advances in deep generative modeling to the task of imit...
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The role of surrogate models in the development of digital twins of dynamic systems
Digital twin technology has significant promise, relevance and potential...
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Painless Stochastic Gradient: Interpolation, LineSearch, and Convergence Rates
Recent works have shown that stochastic gradient descent (SGD) achieves ...
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Neural Sequential Phrase Grounding (SeqGROUND)
We propose an endtoend approach for phrase grounding in images. Unlike...
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Valid Causal Inference with (Some) Invalid Instruments
Instrumental variable methods provide a powerful approach to estimating ...
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Smarter Parking: Using AI to Identify Parking Inefficiencies in Vancouver
Onstreet parking is convenient, but has many disadvantages: onstreet s...
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Distilling and Transferring Knowledge via cGANgenerated Samples for Image Classification and Regression
Knowledge distillation (KD) has been actively studied for image classifi...
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Learning Sports Camera Selection from Internet Videos
This work addresses camera selection, the task of predicting which camer...
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Instancelevel Facial Attributes Transfer with GeometryAware Flow
We address the problem of instancelevel facial attribute transfer witho...
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CHAIN: Conceptharmonized Hierarchical Inference Interpretation of Deep Convolutional Neural Networks
With the great success of networks, it witnesses the increasing demand f...
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Coping With Simulators That Don't Always Return
Deterministic models are approximations of reality that are easy to inte...
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Learning Joint ArticulatoryAcoustic Representations with Normalizing Flows
The articulatory geometric configurations of the vocal tract and the aco...
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PCLs: Geometryaware Neural Reconstruction of 3D Pose with Perspective Crop Layers
Local processing is an essential feature of CNNs and other neural networ...
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Efficient Deep Gaussian Process Models for VariableSized Input
Deep Gaussian processes (DGP) have appealing Bayesian properties, can ha...
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Joint User Pairing and Association for Multicell NOMA: A Pointer Networkbased Approach
In this paper, we investigate the joint user pairing and association pro...
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Ultra2Speech – A Deep Learning Framework for Formant Frequency Estimation and Tracking from Ultrasound Tongue Images
Thousands of individuals need surgical removal of their larynx due to cr...
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Hierarchical Marketing Mix Models with Sign Constraints
Marketing mix models (MMMs) are statistical models for measuring the eff...
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Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel framework that enables efficient probabilistic infere...
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Does Your Model Know the Digit 6 Is Not a Cat? A Less Biased Evaluation of "Outlier" Detectors
In the real world, a learning system could receive an input that looks n...
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LayoutVAE: Stochastic Scene Layout Generation from a Label Set
Recently there is an increasing interest in scene generation within the ...
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Inference Trees: Adaptive Inference with Exploration
We introduce inference trees (ITs), a new class of inference methods tha...
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Where are the Blobs: Counting by Localization with Point Supervision
Object counting is an important task in computer vision due to its growi...
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An Introduction to Probabilistic Programming
This document is designed to be a firstyear graduatelevel introduction...
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Instance Segmentation with Point Supervision
Instance segmentation methods often require costly perpixel labels. We ...
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Assisting the Adversary to Improve GAN Training
We propose a method for improved training of generative adversarial netw...
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MADDA: Unsupervised Domain Adaptation with Deep Metric Learning
Unsupervised domain adaptation techniques have been successful for a wid...
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Predicting Confusion from EyeTracking Data with Recurrent Neural Networks
Encouraged by the success of deep learning in a variety of domains, we i...
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Improved FewShot Visual Classification
Fewshot learning is a fundamental task in computer vision that carries ...
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Deep Sentence Embedding Using Long ShortTerm Memory Networks: Analysis and Application to Information Retrieval
This paper develops a model that addresses sentence embedding, a hot top...
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On Integrating Information Visualization Techniques into Data Mining: A Review
The exploding growth of digital data in the information era and its imme...
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MultiChannel CNNbased Object Detection for Enhanced Situation Awareness
Object Detection is critical for automatic military operations. However,...
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Exploiting temporal information for 3D pose estimation
In this work, we address the problem of 3D human pose estimation from a ...
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Recent Advances in Zeroshot Recognition
With the recent renaissance of deep convolution neural networks, encoura...
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Learning across scales  A multiscale method for Convolution Neural Networks
In this work we establish the relation between optimal control and train...
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Backtracking Regression Forests for Accurate Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer v...
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Generalized Haar Filter based Deep Networks for RealTime Object Detection in Traffic Scene
Visionbased object detection is one of the fundamental functions in num...
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Domain Recursion for Lifted Inference with Existential Quantifiers
In recent work, we proved that the domain recursion inference rule makes...
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HotRodding the Browser Engine: Automatic Configuration of JavaScript Compilers
Modern software systems in many application areas offer to the user a mu...
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Reversible Architectures for Arbitrarily Deep Residual Neural Networks
Recently, deep residual networks have been successfully applied in many ...
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Deep Optimization for Spectrum Repacking
Over 13 months in 201617 the FCC conducted an "incentive auction" to re...
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ReducedPrecision Strategies for Bounded Memory in Deep Neural Nets
This work investigates how using reduced precision data in Convolutional...
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Ensembles of Models and Metrics for Robust Ranking of Homologous Proteins
An ensemble of models (EM), where each model is constructed on a diverse...
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New Liftable Classes for FirstOrder Probabilistic Inference
Statistical relational models provide compact encodings of probabilistic...
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A Learning Algorithm for Relational Logistic Regression: Preliminary Results
Relational logistic regression (RLR) is a representation of conditional ...
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Why is Compiling Lifted Inference into a LowLevel Language so Effective?
Firstorder knowledge compilation techniques have proven efficient for l...
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WeaklySupervised Spatial Context Networks
We explore the power of spatial context as a selfsupervisory signal for...
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25306 The University of British Columbia is a public research university with campuses in Vancouver and Kelowna, British Columbia.