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Found 10 papers, 6 papers with code
Dancing in the Dark: A Benchmark towards General Low-light Video Enhancement
To address this issue, we design a camera system and collect a high-quality low-light video dataset with multiple exposures and cameras.
Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo Cameras
One of the biggest challenges in parallel tracking and mapping with a monocular camera is to keep the scale consistent when reconstructing the geometric primitives.
SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation
At first, we define the problem of Training machine learning Models over Multiple data sources with Privacy Preservation (TMMPP for short).
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions
A causal graph, which encodes the relationships among input variables, can aid in assigning feature importance.
AdaSGD: Bridging the gap between SGD and Adam
In the context of stochastic gradient descent(SGD) and adaptive moment estimation (Adam), researchers have recently proposed optimization techniques that transition from Adam to SGD with the goal of improving both convergence and generalization performance.
Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series
In settings with limited data, relaxed parameter sharing can lead to improved patient risk stratification performance.
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks
Recently, researchers have started applying convolutional neural networks (CNNs) with one-dimensional convolutions to clinical tasks involving time-series data.
The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA
During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with a difficult decision when defending against the Cavaliers: "Do you double and risk giving up easy shots, or stay at home and do the best you can?"
Learning Credible Models
In this work, we formally define credibility in the linear setting and focus on techniques for learning models that are both accurate and credible.
HICO: A Benchmark for Recognizing Human-Object Interactions in Images
We introduce a new benchmark "Humans Interacting with Common Objects" (HICO) for recognizing human-object interactions (HOI).
