Search Results for author:
Found 12 papers, 5 papers with code
Improving and Benchmarking Offline Reinforcement Learning Algorithms
Recently, Offline Reinforcement Learning (RL) has achieved remarkable progress with the emergence of various algorithms and datasets.
Confidence-based Reliable Learning under Dual Noises
Deep neural networks (DNNs) have achieved remarkable success in a variety of computer vision tasks, where massive labeled images are routinely required for model optimization.
EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbones
It is also effective for self-supervised learning (e. g., MAE).
Boosting Offline Reinforcement Learning via Data Rebalancing
Therefore, we propose a simple yet effective method to boost offline RL algorithms based on the observation that resampling a dataset keeps the distribution support unchanged.
AdaFocusV3: On Unified Spatial-temporal Dynamic Video Recognition
Recent research has revealed that reducing the temporal and spatial redundancy are both effective approaches towards efficient video recognition, e. g., allocating the majority of computation to a task-relevant subset of frames or the most valuable image regions of each frame.
Value-Consistent Representation Learning for Data-Efficient Reinforcement Learning
Recently, visual representation learning has been shown to be effective and promising for boosting sample efficiency in RL.
Improving short-term bike sharing demand forecast through an irregular convolutional neural network
As an important task for the management of bike sharing systems, accurate forecast of travel demand could facilitate dispatch and relocation of bicycles to improve user satisfaction.
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition
Recent works have shown that the computational efficiency of video recognition can be significantly improved by reducing the spatial redundancy.
Zero-Shot Learning in Named-Entity Recognition with External Knowledge
We propose ZERO, a model that performs zero-shot and few-shot learning in NER to generalize to unseen domains by incorporating pre-existing knowledge in the form of semantic word embeddings.
Separating Content and Style for Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation aims to learn the mapping between two visual domains with unpaired samples.
A SLAM Map Restoration Algorithm Based on Submaps and an Undirected Connected Graph
To solve the missing map problem, which is an issue in many applications , after the tracking is lost, based on monocular visual SLAM, we present a method of reconstructing a complete global map of UAV datasets by sequentially merging the submaps via the corresponding undirected connected graph.
A simple blind-denoising filter inspired by electrically coupled photoreceptors in the retina
Photoreceptors in the retina are coupled by electrical synapses called "gap junctions".
