Search Results for author:
Found 27 papers, 10 papers with code
Cleanba: A Reproducible and Efficient Distributed Reinforcement Learning Platform
Distributed Deep Reinforcement Learning (DRL) aims to leverage more computational resources to train autonomous agents with less training time.
MEMORY-VQ: Compression for Tractable Internet-Scale Memory
Retrieval augmentation is a powerful but expensive method to make language models more knowledgeable about the world.
mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences
We present our work on developing a multilingual, efficient text-to-text transformer that is suitable for handling long inputs.
CoLT5: Faster Long-Range Transformers with Conditional Computation
Many natural language processing tasks benefit from long inputs, but processing long documents with Transformers is expensive -- not only due to quadratic attention complexity but also from applying feedforward and projection layers to every token.
Ranked #1 on
Long-range modeling
on SCROLLS
Improving Fairness in Adaptive Social Exergames via Shapley Bandits
Our results indicate that our Shapley Bandits effectively mediates the Greedy Bandit Problem and achieves better user retention and motivation across the participants.
A2C is a special case of PPO
Advantage Actor-critic (A2C) and Proximal Policy Optimization (PPO) are popular deep reinforcement learning algorithms used for game AI in recent years.
Identifying On-road Scenarios Predictive of ADHD usingDriving Simulator Time Series Data
In this paper we introduce a novel algorithm called Iterative Section Reduction (ISR) to automatically identify sub-intervals of spatiotemporal time series that are predictive of a target classification task.
Iterative Decoding for Compositional Generalization in Transformers
Deep learning models generalize well to in-distribution data but struggle to generalize compositionally, i. e., to combine a set of learned primitives to solve more complex tasks.
Making Transformers Solve Compositional Tasks
Several studies have reported the inability of Transformer models to generalize compositionally, a key type of generalization in many NLP tasks such as semantic parsing.
Improving Compositional Generalization in Classification Tasks via Structure Annotations
Compositional generalization is the ability to generalize systematically to a new data distribution by combining known components.
Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning
In recent years, researchers have achieved great success in applying Deep Reinforcement Learning (DRL) algorithms to Real-time Strategy (RTS) games, creating strong autonomous agents that could defeat professional players in StarCraft~II.
Mondegreen: A Post-Processing Solution to Speech Recognition Error Correction for Voice Search Queries
As more and more online search queries come from voice, automatic speech recognition becomes a key component to deliver relevant search results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
The Personalization Paradox: the Conflict between Accurate User Models and Personalized Adaptive Systems
Personalized adaptation technology has been adopted in a wide range of digital applications such as health, training and education, e-commerce and entertainment.
Human-Computer Interaction
Player-Centered AI for Automatic Game Personalization: Open Problems
Computer games represent an ideal research domain for the next generation of personalized digital applications.
Regression Oracles and Exploration Strategies for Short-Horizon Multi-Armed Bandits
This paper explores multi-armed bandit (MAB) strategies in very short horizon scenarios, i. e., when the bandit strategy is only allowed very few interactions with the environment.
Player Modeling via Multi-Armed Bandits
This paper focuses on building personalized player models solely from player behavior in the context of adaptive games.
Personalization Paradox in Behavior Change Apps: Lessons from a Social Comparison-Based Personalized App for Physical Activity
In addition to design implications for social comparison features in social apps, this paper identified the personalization paradox, the conflict between user modeling and adaptation, as a key design challenge of personalized applications for behavior change.
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games
Training agents using Reinforcement Learning in games with sparse rewards is a challenging problem, since large amounts of exploration are required to retrieve even the first reward.
A Closer Look at Invalid Action Masking in Policy Gradient Algorithms
In recent years, Deep Reinforcement Learning (DRL) algorithms have achieved state-of-the-art performance in many challenging strategy games.
An Overview of Distance and Similarity Functions for Structured Data
The notions of distance and similarity play a key role in many machine learning approaches, and artificial intelligence (AI) in general, since they can serve as an organizing principle by which individuals classify objects, form concepts and make generalizations.
Comparing Observation and Action Representations for Deep Reinforcement Learning in $μ$RTS
This paper presents a preliminary study comparing different observation and action space representations for Deep Reinforcement Learning (DRL) in the context of Real-time Strategy (RTS) games.
Tracing Player Knowledge in a Parallel Programming Educational Game
This paper focuses on "tracing player knowledge" in educational games.
Experience Management in Multi-player Games
Experience Management studies AI systems that automatically adapt interactive experiences such as games to tailor to specific players and to fulfill design goals.
Combinatorial Multi-armed Bandits for Real-Time Strategy Games
Games with large branching factors pose a significant challenge for game tree search algorithms.
The VGLC: The Video Game Level Corpus
Levels are a key component of many different video games, and a large body of work has been produced on how to procedurally generate game levels.
Combat Models for RTS Games
This paper presents three forward models for two-player attrition games, which we call "combat models", and show how they can be used to simulate combat in RTS games.
RHOG: A Refinement-Operator Library for Directed Labeled Graphs
This document provides the foundations behind the functionality provided by the $\rho$G library (https://github. com/santiontanon/RHOG), focusing on the basic operations the library provides: subsumption, refinement of directed labeled graphs, and distance/similarity assessment between directed labeled graphs.
