Search Results for author:
Found 6 papers, 4 papers with code
Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity
Deep reinforcement learning methods exhibit impressive performance on a range of tasks but still struggle on hard exploration tasks in large environments with sparse rewards.
Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building
Agents build and use a local map to predict their observations; high surprisal leads to a "fragmentation event" that truncates the local map.
Model-agnostic Measure of Generalization Difficulty
The measure of a machine learning algorithm is the difficulty of the tasks it can perform, and sufficiently difficult tasks are critical drivers of strong machine learning models.
Exemplar-Based Open-Set Panoptic Segmentation Network
We extend panoptic segmentation to the open-world and introduce an open-set panoptic segmentation (OPS) task.
Weakly Supervised Instance Segmentation by Deep Community Learning
We present a weakly supervised instance segmentation algorithm based on deep community learning with multiple tasks.
Image-level Supervised Instance Segmentation
object-detection
+4
Learning from Noisy Demonstration Sets via Meta-Learned Suitability Assessor
A noisy and diverse demonstration set may hinder the performances of an agent aiming to acquire certain skills via imitation learning.
