Search Results for author:
Found 8 papers, 5 papers with code
Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning
Algorithms that constrain the learned policy towards the given dataset perform well for datasets with high TQ or SACo.
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
Align-RUDDER outperforms competitors on complex artificial tasks with delayed reward and few demonstrations.
General Reinforcement Learning
Multiple Sequence Alignment
+1
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs.
Detecting cutaneous basal cell carcinomas in ultra-high resolution and weakly labelled histopathological images
The aim of this study is to evaluate whether it is possible to detect basal cell carcinomas in histological sections using attention-based deep learning models and to overcome the ultra-high resolution and the weak labels of whole slide images.
Patch Refinement -- Localized 3D Object Detection
We introduce Patch Refinement a two-stage model for accurate 3D object detection and localization from point cloud data.
Ranked #1 on
Object Detection
on KITTI Cars Easy
Human-level Protein Localization with Convolutional Neural Networks
We present the largest comparison of CNN architectures including GapNet-PL for protein localization in HTI images of human cells.
End-to-end learning of pharmacological assays from high-resolution microscopy images
Surprisingly, we could predict 29% of the 209 pharmacological assays at high predictive performance (AUC > 0. 9).
Speeding up Semantic Segmentation for Autonomous Driving
We propose a novel deep network architecture for image segmentation that keeps the high accuracy while being efficient enough for embedded devices.
