4 papers
Kd-tree Based Wasserstein Distance Approximation for High-Dimensional Data
Kanata Teshigawara, Keisho Oh, Ken Kobayashi +1
The Wasserstein distance is a discrepancy measure between probability distributions, defined by an optimal transport problem. It has been used for various tasks such as retrieving…
Content-Aware Ad Banner Layout Generation with Two-Stage Chain-of-Thought in Vision Language Models
Kei Yoshitake, Kento Hosono, Ken Kobayashi +1
In this paper, we propose a method for generating layouts for image-based advertisements by leveraging a Vision-Language Model (VLM). Conventional advertisement layout techniques h…
Hierarchical Time Series Forecasting with Robust Reconciliation
Shuhei Aikawa, Aru Suzuki, Kei Yoshitake +4
This paper focuses on forecasting hierarchical time-series data, where each higher-level observation equals the sum of its corresponding lower-level time series. In such contexts,…
Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification
Ryota Ueda, Takami Sato, Ken Kobayashi +1
Semidefinite programming (SDP) relaxation has emerged as a promising approach for neural network verification, offering tighter bounds than other convex relaxation methods for deep…