
-
Universal Successor Features for Transfer Reinforcement Learning
Transfer in Reinforcement Learning (RL) refers to the idea of applying k...
read it
-
Maximizing Information Gain in Partially Observable Environments via Prediction Reward
Information gathering in a partially observable environment can be formu...
read it
-
CDT: Cascading Decision Trees for Explainable Reinforcement Learning
Deep Reinforcement Learning (DRL) has recently achieved significant adva...
read it
-
Maxmin Q-learning: Controlling the Estimation Bias of Q-learning
Q-learning suffers from overestimation bias, because it approximates the...
read it
-
Towards a practical measure of interference for reinforcement learning
Catastrophic interference is common in many network-based learning syste...
read it
-
Is Fast Adaptation All You Need?
Gradient-based meta-learning has proven to be highly effective at learni...
read it
-
Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent
In this paper, we present exploitability descent, a new algorithm to com...
read it
-
Planning with Expectation Models
Distribution and sample models are two popular model choices in model-ba...
read it
-
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...
read it
-
Shape Detection In 2D Ultrasound Images
Ultrasound images are one of the most widely used techniques in clinical...
read it
-
CNN-Based Real-Time Parameter Tuning for Optimizing Denoising Filter Performance
We propose a novel direction to improve the denoising quality of filteri...
read it
-
Improving Performance in Reinforcement Learning by Breaking Generalization in Neural Networks
Reinforcement learning systems require good representations to work well...
read it
-
DRMIME: Differentiable Mutual Information and Matrix Exponential for Multi-Resolution Image Registration
In this work, we present a novel unsupervised image registration algorit...
read it
-
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...
read it
-
Domain Aggregation Networks for Multi-Source Domain Adaptation
In many real-world applications, we want to exploit multiple source data...
read it
-
Shared Space Transfer Learning for analyzing multi-site fMRI data
Multi-voxel pattern analysis (MVPA) learns predictive models from task-b...
read it
-
What's a Good Prediction? Issues in Evaluating General Value Functions Through Error
Constructing and maintaining knowledge of the world is a central problem...
read it
-
Polarization Human Shape and Pose Dataset
Polarization images are known to be able to capture polarized reflected ...
read it
-
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...
read it
-
Stimulating Creativity with FunLines: A Case Study of Humor Generation in Headlines
Building datasets of creative text, such as humor, is quite challenging....
read it
-
Primitive Fitting Using Deep Boundary Aware Geometric Segmentation
To identify and fit geometric primitives (e.g., planes, spheres, cylinde...
read it
-
Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection
Automated cell detection and localization from microscopy images are sig...
read it
-
Self-Attentional Models Application in Task-Oriented Dialogue Generation Systems
Self-attentional models are a new paradigm for sequence modelling tasks ...
read it
-
Beyond Prioritized Replay: Sampling States in Model-Based RL via Simulated Priorities
Model-based reinforcement learning (MBRL) can significantly improve samp...
read it
-
Learning Causal Models Online
Predictive models – learned from observational data not covering the com...
read it
-
Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling
Knowledge distillation is an effective technique that transfers knowledg...
read it
-
Meta-Learning Representations for Continual Learning
A continual learning agent should be able to build on top of existing kn...
read it
-
Perception as prediction using general value functions in autonomous driving applications
We propose and demonstrate a framework called perception as prediction f...
read it
-
Action2Motion: Conditioned Generation of 3D Human Motions
Action recognition is a relatively established task, where givenan input...
read it
-
Online Off-policy Prediction
This paper investigates the problem of online prediction learning, where...
read it
-
River Ice Segmentation with Deep Learning
This paper deals with the problem of computing surface ice concentration...
read it
-
Contrastive Reasons Detection and Clustering from Online Polarized Debate
This work tackles the problem of unsupervised modeling and extraction of...
read it
-
Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation
Counterfactual reasoning is an important paradigm applicable in many fie...
read it
-
Ordinary Differential Equation and Complex Matrix Exponential for Multi-resolution Image Registration
Autograd-based software packages have recently renewed interest in image...
read it
-
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...
read it
-
Context-Dependent Upper-Confidence Bounds for Directed Exploration
Directed exploration strategies for reinforcement learning are critical ...
read it
-
Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks
The MAPF problem is the fundamental problem of planning paths for multip...
read it
-
On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman
How to best explore in domains with sparse, delayed, and deceptive rewar...
read it
-
Crowd Scene Analysis by Output Encoding
Crowd scene analysis receives growing attention due to its wide applicat...
read it
-
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...
read it
-
Count-Based Exploration with the Successor Representation
The problem of exploration in reinforcement learning is well-understood ...
read it
-
The Utility of Sparse Representations for Control in Reinforcement Learning
We investigate sparse representations for control in reinforcement learn...
read it
-
Automatic Algorithm Selection In Multi-agent Pathfinding
In a multi-agent pathfinding (MAPF) problem, agents need to navigate fro...
read it
-
BubbleRank: Safe Online Learning to Rerank
We study the problem of online learning to re-rank, where users provide ...
read it
-
High-confidence error estimates for learned value functions
Estimating the value function for a fixed policy is a fundamental proble...
read it
-
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...
read it
-
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study
Learning about many things can provide numerous benefits to a reinforcem...
read it
-
Simultaneous Prediction Intervals for Patient-Specific Survival Curves
Accurate models of patient survival probabilities provide important info...
read it
-
MarlRank: Multi-agent Reinforced Learning to Rank
When estimating the relevancy between a query and a document, ranking mo...
read it
-
A Novel Generative Neural Approach for InSAR Joint Phase Filtering and Coherence Estimation
Earth's physical properties like atmosphere, topography and ground insta...
read it