Search Results for author:
Found 11 papers, 4 papers with code
Video Demoireing with Relation-Based Temporal Consistency
Moire patterns, appearing as color distortions, severely degrade image and video qualities when filming a screen with digital cameras.
Abandoning the Bayer-Filter to See in the Dark
Next, a fully convolutional network is proposed to achieve the low-light image enhancement by fusing colored raw data with synthesized monochrome raw data.
Reinforced Meta-path Selection for Recommendation on Heterogeneous Information Networks
We further integrate HRec with RMS and derive our recommendation solution, RMS-HRec, that automatically utilizes the effective meta-paths.
Interpretable performance analysis towards offline reinforcement learning: A dataset perspective
We make it open-source for fair and comprehensive competitions between offline RL algorithms with complete datasets and checkpoints being provided.
Watch Only Once: An End-to-End Video Action Detection Framework
WOO takes a unified video backbone to simultaneously extract features for actor location and action classification.
Scalable Graph Neural Networks for Heterogeneous Graphs
Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data.
Ranked #4 on
Node Property Prediction
on ogbn-mag
Facebook AI's WAT19 Myanmar-English Translation Task Submission
This paper describes Facebook AI's submission to the WAT 2019 Myanmar-English translation task.
Revisiting Self-Training for Neural Sequence Generation
In this work, we first empirically show that self-training is able to decently improve the supervised baseline on neural sequence generation tasks.
The Source-Target Domain Mismatch Problem in Machine Translation
While we live in an increasingly interconnected world, different places still exhibit strikingly different cultures and many events we experience in our every day life pertain only to the specific place we live in.
PyTorch-BigGraph: A Large-scale Graph Embedding System
Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks.
Ranked #1 on
Link Prediction
on YouTube
(Macro F1 metric)
Deformable Classifiers
In this paper, we design a framework for training deformable classifiers, where latent transformation variables are introduced, and a transformation of the object image to a reference instantiation is computed in terms of the classifier output, separately for each class.
