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Found 19 papers, 6 papers with code
VRKitchen2.0-IndoorKit: A Tutorial for Augmented Indoor Scene Building in Omniverse
With the recent progress of simulations by 3D modeling software and game engines, many researchers have focused on Embodied AI tasks in the virtual environment.
Effects of Augmented-Reality-Based Assisting Interfaces on Drivers' Object-wise Situational Awareness in Highly Autonomous Vehicles
To understand the effect of highlighting on drivers' SA for objects with different types and locations under various traffic densities, we conducted an in-person experiment with 20 participants on a driving simulator.
HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction
Based on this observation, in this paper, we propose a novel approach Hierarchical Intention Embedding Network (HIEN), which considers dependencies of attributes based on bottom-up tree aggregation in the constructed attribute graph.
Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach
To circumvent false negatives, we develop a principled approach to improve the reliability of negative instances and prove that the objective is an unbiased estimation of sampling from the true negative distribution.
DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following
Language-guided Embodied AI benchmarks requiring an agent to navigate an environment and manipulate objects typically allow one-way communication: the human user gives a natural language command to the agent, and the agent can only follow the command passively.
Cross-Task Knowledge Distillation in Multi-Task Recommendation
Multi-task learning (MTL) has been widely used in recommender systems, wherein predicting each type of user feedback on items (e. g, click, purchase) are treated as individual tasks and jointly trained with a unified model.
FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social Networks
Finding influential users in social networks is a fundamental problem with many possible useful applications.
Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration
Our experiments with human participants demonstrate that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground truth.
Inductive Collaborative Filtering via Relation Graph Learning
In this paper, we propose an inductive collaborative filtering framework that learns a hidden relational graph among users from the rating matrix.
Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks
Thus, in this paper, we propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations, where the robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications based on its online Bayesian inference of the user's mental state.
Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach
The first model follows conventional matrix factorization which factorizes a group of key users' rating matrix to obtain meta latents.
MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding
Online real-time bidding (RTB) is known as a complex auction game where ad platforms seek to consider various influential key performance indicators (KPIs), like revenue and return on investment (ROI).
A Hierarchical Optimizer for Recommendation System Based on Shortest Path Algorithm
Top-k Nearest Geosocial Keyword (T-kNGK) query on geosocial network is defined to give users k recommendations based on some keywords and designated spatial range, and can be realized by shortest path algorithms.
Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling
We target modeling latent dynamics in high-dimension marked event sequences without any prior knowledge about marker relations.
NETR-Tree: An Eifficient Framework for Social-Based Time-Aware Spatial Keyword Query
In the user layer, we exploit the network embedding strategy to measure the relationship effect in users' relationship network.
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems
Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods.
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VRKitchen: an Interactive 3D Virtual Environment for Task-oriented Learning
One of the main challenges of advancing task-oriented learning such as visual task planning and reinforcement learning is the lack of realistic and standardized environments for training and testing AI agents.
Accelerate RNN-based Training with Importance Sampling
Unfortunately, the calculation of the sampling probability distribution $P$ causes a major limitation of IS: it requires the input data to be well-structured, i. e., the feature vector is properly defined.
Learning Social Affordance Grammar from Videos: Transferring Human Interactions to Human-Robot Interactions
In this paper, we present a general framework for learning social affordance grammar as a spatiotemporal AND-OR graph (ST-AOG) from RGB-D videos of human interactions, and transfer the grammar to humanoids to enable a real-time motion inference for human-robot interaction (HRI).
