Search Results for author:
Found 6 papers, 1 papers with code
Selectively Informative Description can Reduce Undesired Embedding Entanglements in Text-to-Image Personalization
In text-to-image personalization, a timely and crucial challenge is the tendency of generated images overfitting to the biases present in the reference images.
Harmonizing Visual and Textual Embeddings for Zero-Shot Text-to-Image Customization
In a surge of text-to-image (T2I) models and their customization methods that generate new images of a user-provided subject, current works focus on alleviating the costs incurred by a lengthy per-subject optimization.
Enhancing Contrastive Learning with Efficient Combinatorial Positive Pairing
In the past few years, contrastive learning has played a central role for the success of visual unsupervised representation learning.
An Adaptive Dual-level Reinforcement Learning Approach for Optimal Trade Execution
The purpose of this research is to devise a tactic that can closely track the daily cumulative volume-weighted average price (VWAP) using reinforcement learning.
Evaluating Feature Attribution Methods for Electrocardiogram
The performance of cardiac arrhythmia detection with electrocardiograms(ECGs) has been considerably improved since the introduction of deep learning models.
On the radius of spatial analyticity for the Klein-Gordon-Schrödinger system
In this paper, we study the persistence of spatial analyticity for the solutions to the Klein-Gordon-Schr\"{o}dinger system, which describes a physical system of a nucleon field interacting with a neutral meson field, with analytic initial data.
Analysis of PDEs
