Search Results for author:
Found 7 papers, 4 papers with code
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity
We study the problem of online sequential decision-making given auxiliary demonstrations from experts who made their decisions based on unobserved contextual information.
On Inference Stability for Diffusion Models
To minimize that gap, we propose a novel \textit{sequence-aware} loss that aims to reduce the estimation gap to enhance the sampling quality.
Individual Tree Detection in Large-Scale Urban Environments using High-Resolution Multispectral Imagery
We introduce a novel deep learning method for detection of individual trees in urban environments using high-resolution multispectral aerial imagery.
Improvement in Machine Translation with Generative Adversarial Networks
In this paper, we explore machine translation improvement via Generative Adversarial Network (GAN) architecture.
Human Imperceptible Attacks and Applications to Improve Fairness
However, small perturbations which are imperceptible to humans may significantly degrade the performance of well-trained deep neural networks.
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm as well as the optimism principle.
Sub-character Neural Language Modelling in Japanese
In East Asian languages such as Japanese and Chinese, the semantics of a character are (somewhat) reflected in its sub-character elements.
