Search Results for author:
Found 8 papers, 1 papers with code
TopoMask: Instance-Mask-Based Formulation for the Road Topology Problem via Transformer-Based Architecture
In this study, we have introduced a new approach called TopoMask for predicting centerlines in road topology.
Ranked #1 on
3D Lane Detection
on OpenLane-V2 test
Reinforcement Learning for Robotic Manipulation using Simulated Locomotion Demonstrations
In order to exploit this idea, we introduce a framework whereby an object locomotion policy is initially obtained using a realistic physics simulator.
Multi-agent Deep Reinforcement Learning with Extremely Noisy Observations
An agent's policy depends on its own private observations as well as those explicitly shared by others through a communication medium.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization
In this paper, we propose a novel unsupervised clustering approach exploiting the hidden information that is indirectly introduced through a pseudo classification objective.
Ranked #1 on
Unsupervised Image Classification
on SVHN
(using extra training data)
GAR: An efficient and scalable Graph-based Activity Regularization for semi-supervised learning
Adjacency of the examples is inferred using the predictions of a neural network model which is first initialized by a supervised pretraining.
Clustering-based Source-aware Assessment of True Robustness for Learning Models
We introduce a novel validation framework to measure the true robustness of learning models for real-world applications by creating source-inclusive and source-exclusive partitions in a dataset via clustering.
Auto-clustering Output Layer: Automatic Learning of Latent Annotations in Neural Networks
As the proposed output layer modification duplicates the softmax nodes at the output layer for each class, GAR allows for competitive learning between these duplicates on a traditional error-correction learning framework to ultimately enable a neural network to learn the latent annotations in this partially supervised setup.
