University of Alberta

∙ Edmonton , Canada ∙ 0 followers

The University of Alberta in Edmonton is one of Canada's top teaching and research universities, with an international reputation for excellence across the humanities, sciences, creative arts, business, engineering, and health sciences. Home to 38,000 students and 15,000 faculty and staff, the university has an annual budget of $1.8 billion and attracts more than $480 million in sponsored research revenue. The U of A offers close to 400 rigorous undergraduate, graduate, and professional programs in 18 faculties on five campuses, including one rural and one francophone campus. The university has more than 275,000 alumni worldwide. The university and its people remain dedicated to the promise made in 1908 by founding president Henry Marshall Tory that knowledge shall be used for "uplifting the whole people."

Featured Researchers

Yue Wang
138 publications
Fan Yang
114 publications
Barnabas Poczos
99 publications
Csaba Szepesvari
93 publications
Peng Xu
77 publications
Shimon Whiteson
72 publications
Wei Yang
61 publications
Sen Wang
61 publications
Dale Schuurmans
51 publications
David Silver
49 publications
Martha White
48 publications

Universal Successor Features for Transfer Reinforcement Learning

Transfer in Reinforcement Learning (RL) refers to the idea of applying k...
01/05/2020 ∙ by Chen Ma, et al. ∙ 46 ∙ share

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Maximizing Information Gain in Partially Observable Environments via Prediction Reward

Information gathering in a partially observable environment can be formu...
05/11/2020 ∙ by Yash Satsangi, et al. ∙ 32 ∙ share

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CDT: Cascading Decision Trees for Explainable Reinforcement Learning

Deep Reinforcement Learning (DRL) has recently achieved significant adva...
11/15/2020 ∙ by Zihan Ding, et al. ∙ 30 ∙ share

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Maxmin Q-learning: Controlling the Estimation Bias of Q-learning

Q-learning suffers from overestimation bias, because it approximates the...
02/16/2020 ∙ by Qingfeng Lan, et al. ∙ 29 ∙ share

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Towards a practical measure of interference for reinforcement learning

Catastrophic interference is common in many network-based learning syste...
07/07/2020 ∙ by Vincent Liu, et al. ∙ 28 ∙ share

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An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem

Vehicle routing problem (VRP) is a typical discrete combinatorial optimi...
07/15/2021 ∙ by Bingjie Li, et al. ∙ 28 ∙ share

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Is Fast Adaptation All You Need?

Gradient-based meta-learning has proven to be highly effective at learni...
10/03/2019 ∙ by Khurram Javed, et al. ∙ 24 ∙ share

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Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent

In this paper, we present exploitability descent, a new algorithm to com...
03/13/2019 ∙ by Edward Lockhart, et al. ∙ 20 ∙ share

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Planning with Expectation Models

Distribution and sample models are two popular model choices in model-ba...
04/02/2019 ∙ by Yi Wan, et al. ∙ 20 ∙ share

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Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)

The optimal policy of a reinforcement learning problem is often disconti...
02/07/2020 ∙ by Zhimin Hou, et al. ∙ 19 ∙ share

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Shape Detection In 2D Ultrasound Images

Ultrasound images are one of the most widely used techniques in clinical...
11/22/2019 ∙ by Ruturaj Gole, et al. ∙ 17 ∙ share

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CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter Performance

We propose a novel direction to improve the denoising quality of filteri...
01/20/2020 ∙ by Subhayan Mukherjee, et al. ∙ 17 ∙ share

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Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks

Reinforcement learning systems require good representations to work well...
03/16/2020 ∙ by Sina Ghiassian, et al. ∙ 17 ∙ share

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DRMIME: Differentiable Mutual Information and Matrix Exponential for Multi-Resolution Image Registration

In this work, we present a novel unsupervised image registration algorit...
01/27/2020 ∙ by Abhishek Nan, et al. ∙ 16 ∙ share

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U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection

In this paper, we design a simple yet powerful deep network architecture...
05/18/2020 ∙ by Xuebin Qin, et al. ∙ 16 ∙ share

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Domain Aggregation Networks for Multi-Source Domain Adaptation

In many real-world applications, we want to exploit multiple source data...
09/11/2019 ∙ by Junfeng Wen, et al. ∙ 15 ∙ share

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Shared Space Transfer Learning for analyzing multi-site fMRI data

Multi-voxel pattern analysis (MVPA) learns predictive models from task-b...
10/24/2020 ∙ by Muhammad Yousefnezhad, et al. ∙ 15 ∙ share

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What's a Good Prediction? Issues in Evaluating General Value Functions Through Error

Constructing and maintaining knowledge of the world is a central problem...
01/23/2020 ∙ by Alex Kearney, et al. ∙ 14 ∙ share

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Polarization Human Shape and Pose Dataset

Polarization images are known to be able to capture polarized reflected ...
04/30/2020 ∙ by Shihao Zou, et al. ∙ 14 ∙ share

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Exploring TD error as a heuristic for σ selection in Q(σ, λ)

In the landscape of TD algorithms, the Q(σ, λ) algorithm is an algorithm...
12/21/2019 ∙ by Abhishek Nan, et al. ∙ 13 ∙ share

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Stimulating Creativity with FunLines: A Case Study of Humor Generation in Headlines

Building datasets of creative text, such as humor, is quite challenging....
02/05/2020 ∙ by Nabil Hossain, et al. ∙ 13 ∙ share

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Primitive Fitting Using Deep Boundary Aware Geometric Segmentation

To identify and fit geometric primitives (e.g., planes, spheres, cylinde...
10/03/2018 ∙ by Duanshun Li, et al. ∙ 12 ∙ share

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Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection

Automated cell detection and localization from microscopy images are sig...
10/07/2018 ∙ by Yao Xue, et al. ∙ 12 ∙ share

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Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems

Self-attentional models are a new paradigm for sequence modelling tasks ...
09/11/2019 ∙ by Mansour Saffar, et al. ∙ 12 ∙ share

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Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities

Model-based reinforcement learning (MBRL) can significantly improve samp...
07/19/2020 ∙ by Jincheng Mei, et al. ∙ 12 ∙ share

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Learning Causal Models Online

Predictive models – learned from observational data not covering the com...
06/12/2020 ∙ by Khurram Javed, et al. ∙ 11 ∙ share

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Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling

Knowledge distillation is an effective technique that transfers knowledg...
12/03/2018 ∙ by Minghan Li, et al. ∙ 10 ∙ share

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Meta-Learning Representations for Continual Learning

A continual learning agent should be able to build on top of existing kn...
05/29/2019 ∙ by Khurram Javed, et al. ∙ 10 ∙ share

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Perception as prediction using general value functions in autonomous driving applications

We propose and demonstrate a framework called perception as prediction f...
01/24/2020 ∙ by Daniel Graves, et al. ∙ 9 ∙ share

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Action2Motion: Conditioned Generation of 3D Human Motions

Action recognition is a relatively established task, where givenan input...
07/30/2020 ∙ by Chuan Guo, et al. ∙ 9 ∙ share

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Online Off-policy Prediction

This paper investigates the problem of online prediction learning, where...
11/06/2018 ∙ by Sina Ghiassian, et al. ∙ 8 ∙ share

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River Ice Segmentation with Deep Learning

This paper deals with the problem of computing surface ice concentration...
01/14/2019 ∙ by Abhineet Singh, et al. ∙ 8 ∙ share

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Contrastive Reasons Detection and Clustering from Online Polarized Debate

This work tackles the problem of unsupervised modeling and extraction of...
08/01/2019 ∙ by Amine Trabelsi, et al. ∙ 8 ∙ share

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Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation

Counterfactual reasoning is an important paradigm applicable in many fie...
12/19/2019 ∙ by Zichen Zhang, et al. ∙ 8 ∙ share

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Ordinary Differential Equation and Complex Matrix Exponential for Multi-resolution Image Registration

Autograd-based software packages have recently renewed interest in image...
07/27/2020 ∙ by Abhishek Nan, et al. ∙ 8 ∙ share

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A Globally Normalized Neural Model for Semantic Parsing

In this paper, we propose a globally normalized model for context-free g...
06/07/2021 ∙ by Chenyang Huang, et al. ∙ 7 ∙ share

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Actor-Expert: A Framework for using Action-Value Methods in Continuous Action Spaces

Value-based approaches can be difficult to use in continuous action spac...
10/22/2018 ∙ by Sungsu Lim, et al. ∙ 6 ∙ share

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Context-Dependent Upper-Confidence Bounds for Directed Exploration

Directed exploration strategies for reinforcement learning are critical ...
11/15/2018 ∙ by Raksha Kumaraswamy, et al. ∙ 6 ∙ share

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Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

The MAPF problem is the fundamental problem of planning paths for multip...
06/19/2019 ∙ by Roni Stern, et al. ∙ 5 ∙ share

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On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman

How to best explore in domains with sparse, delayed, and deceptive rewar...
07/26/2019 ∙ by Chao Gao, et al. ∙ 5 ∙ share

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Crowd Scene Analysis by Output Encoding

Crowd scene analysis receives growing attention due to its wide applicat...
01/27/2020 ∙ by Yao Xue, et al. ∙ 5 ∙ share

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Organizing Experience: A Deeper Look at Replay Mechanisms for Sample-based Planning in Continuous State Domains

Model-based strategies for control are critical to obtain sample efficie...
06/12/2018 ∙ by Yangchen Pan, et al. ∙ 4 ∙ share

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Count-Based Exploration with the Successor Representation

The problem of exploration in reinforcement learning is well-understood ...
07/31/2018 ∙ by Marlos C. Machado, et al. ∙ 4 ∙ share

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The Utility of Sparse Representations for Control in Reinforcement Learning

We investigate sparse representations for control in reinforcement learn...
11/15/2018 ∙ by Vincent Liu, et al. ∙ 4 ∙ share

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Automatic Algorithm Selection In Multi-agent Pathfinding

In a multi-agent pathfinding (MAPF) problem, agents need to navigate fro...
06/10/2019 ∙ by Devon Sigurdson, et al. ∙ 4 ∙ share

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BubbleRank: Safe Online Learning to Rerank

We study the problem of online learning to re-rank, where users provide ...
06/15/2018 ∙ by Branislav Kveton, et al. ∙ 2 ∙ share

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High-confidence error estimates for learned value functions

Estimating the value function for a fixed policy is a fundamental proble...
08/28/2018 ∙ by Touqir Sajed, et al. ∙ 2 ∙ share

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General Dynamic Neural Networks for explainable PID parameter tuning in control engineering: An extensive comparison

Automation, the ability to run processes without human supervision, is o...
05/30/2019 ∙ by Johannes Günther, et al. ∙ 2 ∙ share

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Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study

Learning about many things can provide numerous benefits to a reinforcem...
06/19/2019 ∙ by Cam Linke, et al. ∙ 2 ∙ share

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Simultaneous Prediction Intervals for Patient-Specific Survival Curves

Accurate models of patient survival probabilities provide important info...
06/25/2019 ∙ by Samuel Sokota, et al. ∙ 2 ∙ share

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