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Found 6 papers, 3 papers with code
Sample Efficient Deep Reinforcement Learning via Uncertainty Estimation
In model-free deep reinforcement learning (RL) algorithms, using noisy value estimates to supervise policy evaluation and optimization is detrimental to the sample efficiency.
$f$-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception
While modern deep neural networks are performant perception modules, performance (accuracy) alone is insufficient, particularly for safety-critical robotic applications such as self-driving vehicles.
AutoLay: Benchmarking amodal layout estimation for autonomous driving
Given an image or a video captured from a monocular camera, amodal layout estimation is the task of predicting semantics and occupancy in bird's eye view.
MonoLayout: Amodal scene layout from a single image
We dub this problem amodal scene layout estimation, which involves "hallucinating" scene layout for even parts of the world that are occluded in the image.
Multi-Document Summarization using Distributed Bag-of-Words Model
As the number of documents on the web is growing exponentially, multi-document summarization is becoming more and more important since it can provide the main ideas in a document set in short time.
BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification
Deep learning based landcover classification algorithms have recently been proposed in literature.
Ranked #12 on
Hyperspectral Image Classification
on Indian Pines
(Overall Accuracy metric)
