Search Results for author:
Found 12 papers, 2 papers with code
Learning Attentive Meta-Transfer
Meta-transfer learning seeks to improve the efficiency of learning a new task via both meta-learning and transfer-learning in a setting with a stream of evolving tasks.
Dr. Strategy: Model-Based Generalist Agents with Strategic Dreaming
The proposed agent realizes a version of divide-and-conquer-like strategy in dreaming.
Spatially-Aware Transformer for Embodied Agents
Adopting this approach, we demonstrate that memory utilization efficiency can be improved, leading to enhanced accuracy in various place-centric downstream tasks.
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning
In this paper, we investigate the effectiveness of OCR pre-training for image-based reinforcement learning via empirical experiments.
TransDreamer: Reinforcement Learning with Transformer World Models
We then share this world model with a transformer-based policy network and obtain stability in training a transformer-based RL agent.
Model-based Reinforcement Learning
reinforcement-learning
+1
Generative Video Transformer: Can Objects be the Words?
We compare our model with previous RNN-based approaches as well as other possible video transformer baselines.
Robustifying Sequential Neural Processes
When tasks change over time, meta-transfer learning seeks to improve the efficiency of learning a new task via both meta-learning and transfer-learning.
Attentive Sequential Neural Processes
In this paper, we propose the Attentive Sequential Neural Processes (ASNP) that resolve the underfitting in SNP by introducing a novel imaginary context as a latent variable and by applying attention over the imaginary context.
Sequential Neural Processes
In this paper, we propose Sequential Neural Processes (SNP) which incorporates a temporal state-transition model of stochastic processes and thus extends its modeling capabilities to dynamic stochastic processes.
One-Shot Learning for Text-to-SQL Generation
We show that our model outperforms state-of-the-art approaches for various text-to-SQL datasets in two aspects: 1) the SQL generation accuracy for the trained templates, and 2) the adaptability to the unseen SQL templates based on a single example without any additional training.
Bayesian Model-Agnostic Meta-Learning
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem.
Deep Learning on Key Performance Indicators for Predictive Maintenance in SAP HANA
The experimental results with real-world data confirm the effectiveness of the system and models.
