Multi-scale resolution training has seen an increased adoption across
mu...
We propose Dataset Reinforcement, a strategy to improve a dataset once s...
Deep neural networks are the most commonly used function approximators i...
A growing body of research in continual learning focuses on the catastro...
A large body of research in continual learning is devoted to overcoming ...
A growing body of research in continual learning is devoted to overcomin...
Learning new tasks continuously without forgetting on a constantly chang...
Estimating individual and average treatment effects from observational d...
Continual (sequential) training and multitask (simultaneous) training ar...
We study continual learning in the large scale setting where tasks in th...
Neural networks have achieved remarkable success in many cognitive tasks...
This work focuses on off-policy evaluation (OPE) with function approxima...
Catastrophic forgetting affects the training of neural networks, limitin...
The challenge of developing powerful and general Reinforcement Learning ...
In recent years, neural networks have demonstrated an outstanding abilit...
Knowledge distillation introduced in the deep learning context is a meth...
Neural networks are achieving state of the art and sometimes super-human...
The ability to navigate from visual observations in unfamiliar environme...
Despite the fact that deep neural networks are powerful models and achie...
We study the problem of off-policy evaluation (OPE) in reinforcement lea...
The spread of invasive species to new areas threatens the stability of
e...
Point processes are becoming very popular in modeling asynchronous seque...
Poisson factorization is a probabilistic model of users and items for
re...
Online knowledge repositories typically rely on their users or dedicated...
Many users in online social networks are constantly trying to gain atten...
Large volume of networked streaming event data are becoming increasingly...
Learning Granger causality for general point processes is a very challen...
Information diffusion in online social networks is affected by the under...