Search Results for author:
Found 12 papers, 5 papers with code
Holodeck: Language Guided Generation of 3D Embodied AI Environments
3D simulated environments play a critical role in Embodied AI, but their creation requires expertise and extensive manual effort, restricting their diversity and scope.
Partial-View Object View Synthesis via Filtered Inversion
At inference, given one or more views of a novel real-world object, FINV first finds a set of latent codes for the object by inverting the generative model from multiple initial seeds.
Interaction Modeling with Multiplex Attention
Modeling multi-agent systems requires understanding how agents interact.
Equivariant Neural Network for Factor Graphs
Several indices used in a factor graph data structure can be permuted without changing the underlying probability distribution.
Physion: Evaluating Physical Prediction from Vision in Humans and Machines
While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments.
Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn Chatbot Responding with Intention
Our framework includes a guiding chatbot and an interlocutor model that plays the role of humans.
Organ At Risk Segmentation with Multiple Modality
To better leverage different modalities, we have collected a large dataset consists of 136 cases with CT and MR images which diagnosed with nasopharyngeal cancer.
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization
There are also some recent methods based on language models (e. g. graph2vec) but they tend to only consider certain substructures (e. g. subtrees) as graph representatives.
Ranked #24 on
Graph Classification
on IMDb-B
vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
Experimental results on multiple real-world graphs show that vGraph is very effective in both community detection and node representation learning, outperforming many competitive baselines in both tasks.
A Regulation Enforcement Solution for Multi-agent Reinforcement Learning
If artificially intelligent (AI) agents make decisions on behalf of human beings, we would hope they can also follow established regulations while interacting with humans or other AI agents.
A Memory-Network Based Solution for Multivariate Time-Series Forecasting
Inspired by Memory Network proposed for solving the question-answering task, we propose a deep learning based model named Memory Time-series network (MTNet) for time series forecasting.
ANS: Adaptive Network Scaling for Deep Rectifier Reinforcement Learning Models
This work provides a thorough study on how reward scaling can affect performance of deep reinforcement learning agents.
