Satoshi Koide
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LaViC: Adapting Large Vision-Language Models to Visually-Aware Conversational Recommendation Open
Conversational recommender systems engage users in dialogues to refine their needs and provide more personalized suggestions. Although textual information suffices for many domains, visually driven categories such as fashion or home decor …
Impact of Tone-Aware Explanations in Recommender Systems Open
In recommender systems, explanations are essential for supporting users’ decision-making processes. While many studies have focused on explanation content or user interface, the expression of textual explanations has been largely overlooke…
Toward Tone-Aware Explanations in Recommender Systems Open
In recommender systems, the presentation of explanations plays a crucial role in supporting users' decision-making processes. Although numerous existing studies have focused on the effects (e.g., transparency) of explanation content, expla…
Impact of Tone-Aware Explanations in Recommender Systems Open
In recommender systems, the presentation of explanations plays a crucial role in supporting users' decision-making processes. Although numerous existing studies have focused on the effects (transparency or persuasiveness) of explanation co…
Multi-Dimensional Fused Gromov Wasserstein Discrepancy for Edge-Attributed Graphs Open
Graph dissimilarities provide a powerful and ubiquitous approach for applying machine learning algorithms to edge-attributed graphs. However, conventional optimal transport-based dissimilarities cannot handle edge-attributes. In this paper…
A Case Study on Recommender Systems in Online Conferences: Behavioral Analysis through A/B Testing Open
Owing to the COVID-19 pandemic, many academic conferences are now being held online. Our study focuses on online video conferences, where participants can watch pre-recorded embedded videos on a conference website. In online video conferen…
One-Shot Domain Incremental Learning Open
Domain incremental learning (DIL) has been discussed in previous studies on deep neural network models for classification. In DIL, we assume that samples on new domains are observed over time. The models must classify inputs on all domains…
High-Dimensional Slider-Based Preferential Bayesian Optimization With Mixed Local and Global Acquisition Strategies Open
Preferential Bayesian optimization (PBO) is a framework for human-in-the-loop optimization to maximize black-box human preference functions such as seeking perceptually good visual designs. It is advantageous when consistently providing a …
Deep generative model super-resolves spatially correlated multiregional climate data Open
Super-resolving the coarse outputs of global climate simulations, termed downscaling, is crucial in making political and social decisions on systems requiring long-term climate change projections. Existing fast super-resolution techniques,…
Stability Analysis of Logit Dynamics with Committed Minority and Internal/External Conformity Biases Open
In this paper, we analyze a novel continuous-time dynamical binary choice model that unifies several logit dynamics in the presence of a committed minority and internal/external conformity biases. Each logit dynamics represents a populatio…
Reward for Exploration Based on View Synthesis Open
Research on embodied-AI has flourished in recent years to make AI accessible to real-world information. Visual exploration is a very fundamental task in embodied-AI applications such as object-goal navigation, embodied questioning and answ…
Enhancement of Robot Navigation Systems via Human Pointing Instruction Open
The determination of destinations and paths is a critical aspect of robot navigation. In recent years, research has been conducted on methods to provide robots with realistic destinations for use in practical situations. One such general m…
Collision-free Navigation with Unknown and Heterogeneous Social Preferences Open
In this study, we address cooperative navigation using multiple agents with social preferences such as egoism and altruism. Previous studies have proposed collision-free navigation with social preferences under the assumption that the soci…
Deep generative model super-resolves spatially correlated multiregional climate data Open
Super-resolving the coarse outputs of global climate simulations, termed downscaling, is crucial in making political and social decisions on systems requiring long-term climate change projections. Existing fast super-resolution techniques,…
Quantum topology optimization of ground structures using noisy intermediate-scale quantum devices Open
To arrive at some viable product design, product development processes frequently use numerical simulations and mathematical programming techniques. Topology optimization, in particular, is one of the most promising techniques for generati…
Partial Wasserstein Covering Open
We consider a general task called partial Wasserstein covering with the goal of providing information on what patterns are not being taken into account in a dataset (e.g., dataset used during development) compared to another (e.g., dataset…
Entropy Tucker model: Mining latent mobility patterns with simultaneous estimation of travel impedance parameters Open
With the rapid increase in the availability of passive data in the field of transportation, combining machine learning with transportation models has emerged as an important research topic in recent years. This study proposes an entropy Tu…
Variational quantum algorithm based on the minimum potential energy for solving the Poisson equation Open
Computer-aided engineering techniques are indispensable in modern engineering developments. In particular, partial differential equations are commonly used to simulate the dynamics of physical phenomena, but very large systems are often in…
Computationally Efficient Quantum Expectation with Extended Bell Measurements Open
Evaluating an expectation value of an arbitrary observable $A\in{\mathbb C}^{2^n\times 2^n}$ through naïve Pauli measurements requires a large number of terms to be evaluated. We approach this issue using a method based on Bell measurement…
Cooperative Path Planning for Heterogeneous Agents Open
Cooperation among different vehicles is a promising concept for route planning of Mobility as a Service (MaaS). For instance, vehicle platooning on highways decreases fuel consumption because it reduces the air resistance and several truck…
Learning low-dimensional manifolds under the L0-norm constraint for unsupervised outlier detection Open
Unsupervised outlier detection without the need for clean data has attracted great attention because it is suitable for real-world problems as a result of its low data collection costs. Reconstruction-based methods are popular approaches f…
Partial Wasserstein Covering Open
We consider a general task called partial Wasserstein covering with the goal of providing information on what patterns are not being taken into account in a dataset (e.g., dataset used during development) compared with another dataset(e.g.…
Variational Monocular Depth Estimation for Reliability Prediction Open
Self-supervised learning for monocular depth estimation is widely investigated as an alternative to supervised learning approach, that requires a lot of ground truths. Previous works have successfully improved the accuracy of depth estimat…
Neural Time Warping For Multiple Sequence Alignment Open
Multiple sequences alignment (MSA) is a traditional and challenging task for time-series analyses. The MSA problem is formulated as a discrete optimization problem and is typically solved by dynamic programming. However, the computational …
View article: Fast Subtrajectory Similarity Search in Road Networks under Weighted Edit Distance Constraints
Fast Subtrajectory Similarity Search in Road Networks under Weighted Edit Distance Constraints Open
In this paper, we address a similarity search problem for spatial trajectories in road networks. In particular, we focus on the subtrajectory similarity search problem, which involves finding in a database the subtrajectories similar to a …
PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation Open
We propose a novel objective for penalizing geometric inconsistencies to improve the depth and pose estimation performance of monocular camera images. Our objective is designed using the Wasserstein distance between two point clouds, estim…
NERO: Hierarchical-approximated Rebalancing Optimization for Mobility on Demand Open
Mobility-on-Demand (MoD) services, such as taxi-like services, are promising applications. Rebalancing the vehicle locations against customer requests is a key challenge in the services because imbalance between the two worsens service qua…
NERO: Nested Rebalancing Optimization for Mobility on Demand Open
Mobility-on-Demand (MoD) services, such as taxi-like services, are promising applications. Rebalancing the vehicle locations against customer requests is a key challenge in the services because imbalance between the two worsens service qua…