Search Results for author:
Found 33 papers, 13 papers with code
Entity Alignment with Unlabeled Dangling Cases
We investigate the entity alignment problem with unlabeled dangling cases, meaning that there are entities in the source or target graph having no counterparts in the other, and those entities remain unlabeled.
Meta Operator for Complex Query Answering on Knowledge Graphs
To answer complex queries under incomplete knowledge, learning-based Complex Query Answering (CQA) models are proposed to directly learn from the query-answer samples to avoid the direct traversal of incomplete graph data.
BEHAVIOR-1K: A Human-Centered, Embodied AI Benchmark with 1,000 Everyday Activities and Realistic Simulation
We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics.
LM2D: Lyrics- and Music-Driven Dance Synthesis
First, we propose LM2D, a novel probabilistic architecture that incorporates a multimodal diffusion model with consistency distillation, designed to create dance conditioned on both music and lyrics in one diffusion generation step.
Soft Reasoning on Uncertain Knowledge Graphs
To bridge this gap, we study the setting of soft queries on uncertain knowledge, which is motivated by the establishment of soft constraint programming.
Goal-Conditioned Offline Reinforcement Learning via Metric Learning
Experimentally, we show how our method consistently outperforms other offline RL baselines in learning from sub-optimal offline datasets.
Scalable Motion Style Transfer with Constrained Diffusion Generation
Current training of motion style transfer systems relies on consistency losses across style domains to preserve contents, hindering its scalable application to a large number of domains and private data.
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale
In industry system, such as ranking system in Meta, it is unclear whether NAS algorithms from the literature can outperform production baselines because of: (1) scale - Meta ranking systems serve billions of users, (2) strong baselines - the baselines are production models optimized by hundreds to thousands of world-class engineers for years since the rise of deep learning, (3) dynamic baselines - engineers may have established new and stronger baselines during NAS search, and (4) efficiency - the search pipeline must yield results quickly in alignment with the productionization life cycle.
AutoML for Large Capacity Modeling of Meta's Ranking Systems
The large scale of models and tight production schedule requires AutoML to outperform human baselines by only using a small number of model evaluation trials (around 100).
Multi-State Brain Network Discovery
We refer to such brain networks as multi-state, and this mixture can help us understand human behavior.
EXMODD: An EXplanatory Multimodal Open-Domain Dialogue dataset
The need for high-quality data has been a key issue hindering the research of dialogue tasks.
Music- and Lyrics-driven Dance Synthesis
To complement it, we introduce JustLMD, a new multimodal dataset of 3D dance motion with music and lyrics.
Learning Geometric Representations of Objects via Interaction
We address the problem of learning representations from observations of a scene involving an agent and an external object the agent interacts with.
$\text{EFO}_{k}$-CQA: Towards Knowledge Graph Complex Query Answering beyond Set Operation
Learning-based methods are essential because they are capable of generalizing over unobserved knowledge.
Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport
In contrast to existing scoring functions motivated by local comparison or global transport, this work investigates the local and global trade-off with unbalanced optimal transport theory.
Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors
Moreover, we develop a new dataset containing ten new types of queries with features that have never been considered and therefore can provide a thorough investigation of complex queries.
Controllable Motion Synthesis and Reconstruction with Autoregressive Diffusion Models
Data-driven and controllable human motion synthesis and prediction are active research areas with various applications in interactive media and social robotics.
Behavioral graph fraud detection in E-commerce
Our experiments show that embedding features learned from similarity based behavioral graph have achieved significant performance increase to the baseline fraud detection model in various business scenarios.
Dance Style Transfer with Cross-modal Transformer
We present CycleDance, a dance style transfer system to transform an existing motion clip in one dance style to a motion clip in another dance style while attempting to preserve motion context of the dance.
Back to the Manifold: Recovering from Out-of-Distribution States
However, a major challenge is a distributional shift between the states in the training dataset and the ones visited by the learned policy at the test time.
On the Subspace Structure of Gradient-Based Meta-Learning
In this work we provide an analysis of the distribution of the post-adaptation parameters of Gradient-Based Meta-Learning (GBML) methods.
Geometric Multimodal Contrastive Representation Learning
Learning representations of multimodal data that are both informative and robust to missing modalities at test time remains a challenging problem due to the inherent heterogeneity of data obtained from different channels.
How to Sense the World: Leveraging Hierarchy in Multimodal Perception for Robust Reinforcement Learning Agents
This work addresses the problem of sensing the world: how to learn a multimodal representation of a reinforcement learning agent's environment that allows the execution of tasks under incomplete perceptual conditions.
Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs
Besides, our work, for the first time, provides a benchmark to evaluate and analyze the impact of different operators and normal forms by using (a) 7 choices of the operator systems and (b) 9 forms of complex queries.
On the Impact of Spurious Correlation for Out-of-distribution Detection
Modern neural networks can assign high confidence to inputs drawn from outside the training distribution, posing threats to models in real-world deployments.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Graph-based Normalizing Flow for Human Motion Generation and Reconstruction
Data-driven approaches for modeling human skeletal motion have found various applications in interactive media and social robotics.
Graph-based Task-specific Prediction Models for Interactions between Deformable and Rigid Objects
In an ablation study, we show the benefits of the two-stage model for single time step prediction and the effectiveness of the mixed-horizon model for long-term prediction tasks.
Robotics
Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap
We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces, focusing on manipulation of deformable objects.
Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks
Existing works mainly focus on unimodal distributions, where it is usually assumed that the observed activities aregenerated from asingleGaussian distribution (i. e., one graph). However, this assumption is too strong for many real-worldapplications.
Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection
To utilize the behavior sequence data, we treat click stream data as event sequence, use time attention based Bi-LSTM to learn the sequence embedding in an unsupervised fashion, and combine them with intuitive features generated by risk experts to form a hybrid feature representation.
Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation
We present a framework for visual action planning of complex manipulation tasks with high-dimensional state spaces such as manipulation of deformable objects.
Probabilistic Model Learning and Long-term Prediction for Contact-rich Manipulation Tasks
Learning dynamics models is an essential component of model-based reinforcement learning.
Robotics
StableNet: Semi-Online, Multi-Scale Deep Video Stabilization
Video stabilization algorithms are of greater importance nowadays with the prevalence of hand-held devices which unavoidably produce videos with undesirable shaky motions.
