Search Results for author:
Found 33 papers, 12 papers with code
ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting
To capitalize on both of these strengths, we propose ATFNet, an innovative framework that combines a time domain module and a frequency domain module to concurrently capture local and global dependencies in time series data.
GROUNDHOG: Grounding Large Language Models to Holistic Segmentation
Most multimodal large language models (MLLMs) learn language-to-object grounding through causal language modeling where grounded objects are captured by bounding boxes as sequences of location tokens.
Ranked #1 on
Generalized Referring Expression Segmentation
on gRefCOCO
(using extra training data)
Causal Language Modeling
Generalized Referring Expression Segmentation
+2
Mixture of Link Predictors
Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning.
Pareto-based Multi-Objective Recommender System with Forgetting Curve
Considering recency effect in memories, we propose a forgetting model based on Ebbinghaus Forgetting Curve to cope with negative feedback.
Planning as In-Painting: A Diffusion-Based Embodied Task Planning Framework for Environments under Uncertainty
In this paper, we aim to tackle this problem with a unified framework consisting of an end-to-end trainable method and a planning algorithm.
DSCom: A Data-Driven Self-Adaptive Community-Based Framework for Influence Maximization in Social Networks
Several previous works have addressed this topic in a statistical way and provided efficient algorithms with theoretical guarantee.
Mastering Robot Manipulation with Multimodal Prompts through Pretraining and Multi-task Fine-tuning
Inspired by their success in language tasks, existing research has leveraged LLMs in embodied instruction following and task planning.
Temporal Interest Network for User Response Prediction
To fill this gap, we propose a Temporal Interest Network (TIN) to capture the semantic-temporal correlation simultaneously between behaviors and the target.
Alexa, play with robot: Introducing the First Alexa Prize SimBot Challenge on Embodied AI
The Alexa Prize program has empowered numerous university students to explore, experiment, and showcase their talents in building conversational agents through challenges like the SocialBot Grand Challenge and the TaskBot Challenge.
LEMMA: Learning Language-Conditioned Multi-Robot Manipulation
We introduce a benchmark for LanguagE-Conditioned Multi-robot MAnipulation (LEMMA) focused on task allocation and long-horizon object manipulation based on human language instructions in a tabletop setting.
Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public Cloud
Multi-tenancy in public clouds may lead to co-location interference on shared resources, which possibly results in performance degradation of cloud applications.
ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes
To tackle these challenges, we present ARNOLD, a benchmark that evaluates language-grounded task learning with continuous states in realistic 3D scenes.
Alexa Arena: A User-Centric Interactive Platform for Embodied AI
We introduce Alexa Arena, a user-centric simulation platform for Embodied AI (EAI) research.
AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling
In this paper, we propose a novel model named AutoAttention, which includes all item/user/context side fields as the query, and assigns a learnable weight for each field pair between behavior fields and query fields.
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.
Bayesian Inference
Explainable Artificial Intelligence (XAI)
+1
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 a complex auction game among which advertisers struggle to bid for ad impressions when a user request occurs.
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.
Ranked #1 on
Recommendation Systems
on WeChat
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).
