Diffusion models learn to reverse the progressive noising of a data
dist...
As natural language interfaces enable users to express increasingly comp...
Traditional recommender systems leverage users' item preference history ...
Text detoxification is a conditional text generation task aiming to remo...
Conversational recommender systems (CRS) enhance the expressivity and
pe...
Traffic prediction is a spatiotemporal predictive task that plays an
ess...
Perimeter control maintains high traffic efficiency within protected reg...
Can a Large Language Model (LLM) solve simple abstract reasoning problem...
Clustering is a powerful and extensively used data science tool. While
c...
Users may demand recommendations with highly personalized requirements
i...
In safe MDP planning, a cost function based on the current state and act...
Recent years have witnessed substantial growth in adaptive traffic signa...
We present pyRDDLGym, a Python framework for auto-generation of OpenAI G...
Text-based games present a unique class of sequential decision making pr...
The Abstraction and Reasoning Corpus (ARC) aims at benchmarking the
perf...
Recent advances in deep learning have enabled optimization of deep react...
Weakly supervised semantic segmentation (WSSS) with only image-level
sup...
Conversational Recommendation Systems (CRSs) have recently started to
le...
Contrastive learning has led to substantial improvements in the quality ...
Spatiotemporal prediction of event data is a challenging task with a lon...
This document provides a brief introduction to learned automated plannin...
Planning provides a framework for optimizing sequential decisions in com...
Recent years have seen the introduction of a range of methods for post-h...
Online class-incremental continual learning (CL) studies the problem of
...
Online continual learning for image classification studies the problem o...
Most existing One-Class Collaborative Filtering (OC-CF) algorithms estim...
Continual learning is a branch of deep learning that seeks to strike a
b...
One-class collaborative filtering (OC-CF) is a common class of recommend...
Continual learning is a branch of deep learning that seeks to strike a
b...
Resolving the exploration-exploitation trade-off remains a fundamental
p...
Learning from demonstrations (LfD) improves the exploration efficiency o...
Reinforcement learning methods that consider the context, or current sta...
Twitter has grown to become an important platform to access immediate
in...
Optimal planning with respect to learned neural network (NN) models in
c...
In many real-world planning problems with factored, mixed discrete and
c...
In this paper, we leverage the efficiency of Binarized Neural Networks (...
In this paper, we leverage the efficiency of Binarized Neural Networks (...
Previous highly scalable one-class collaborative filtering methods such ...
Many photography websites such as Flickr, 500px, Unsplash, and Adobe Beh...
Variational Autoencoders (VAEs) are a popular generative model, but one ...
Lifted probabilistic inference (Poole, 2003) and symbolic dynamic progra...
We introduce a new approximate solution technique for first-order Markov...
Many real-world decision-theoretic planning problems can be naturally mo...