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Found 15 papers, 8 papers with code
Enhancing Sample Generation of Diffusion Models using Noise Level Correction
Building on this insight, we propose a novel method to enhance sample generation by aligning the estimated noise level with the true distance of noisy samples to the manifold.
Revisiting the Initial Steps in Adaptive Gradient Descent Optimization
Adaptive gradient optimization methods, such as Adam, are prevalent in training deep neural networks across diverse machine learning tasks due to their ability to achieve faster convergence.
Estimating Neural Network Robustness via Lipschitz Constant and Architecture Sensitivity
Ensuring neural network robustness is essential for the safe and reliable operation of robotic learning systems, especially in perception and decision-making tasks within real-world environments.
Proactive Human-Robot Co-Assembly: Leveraging Human Intention Prediction and Robust Safe Control
Second, it is difficult to ensure interactive safety due to uncertainty in human behaviors.
General Place Recognition Survey: Towards the Real-world Autonomy Age
A summary of this work and our datasets and evaluation API is publicly available to the robotics community at: https://github. com/MetaSLAM/GPRS.
Loop Closure Detection
Simultaneous Localization and Mapping
+1
BioSLAM: A Bio-inspired Lifelong Memory System for General Place Recognition
We present BioSLAM, a lifelong SLAM framework for learning various new appearances incrementally and maintaining accurate place recognition for previously visited areas.
An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms
Deep reinforcement learning has the potential to address various scientific problems.
An Optics Controlling Environment and Reinforcement Learning Benchmarks
Deep reinforcement learning has the potential to address various scientific problems.
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction
the target property, such that the limited labels can be effectively propagated among similar molecules.
Escaping the Big Data Paradigm with Compact Transformers
Our models are flexible in terms of model size, and can have as little as 0. 28M parameters while achieving competitive results.
Ranked #1 on
Image Classification
on Flowers-102
(using extra training data)
Fine-Grained Image Classification
Superpixel Image Classification
Adaptive Consistency Regularization for Semi-Supervised Transfer Learning
To better exploit the value of both pre-trained weights and unlabeled target examples, we introduce adaptive consistency regularization that consists of two complementary components: Adaptive Knowledge Consistency (AKC) on the examples between the source and target model, and Adaptive Representation Consistency (ARC) on the target model between labeled and unlabeled examples.
Robust Online Model Adaptation by Extended Kalman Filter with Exponential Moving Average and Dynamic Multi-Epoch Strategy
The challenge motivates the adoption of online adaptation algorithms to update prediction models in real-time to improve the prediction performance.
Adaptable Human Intention and Trajectory Prediction for Human-Robot Collaboration
The effectiveness and flexibility of the proposed method has been validated in experiments.
Robotics
Efficient Method for Categorize Animals in the Wild
Thanks to advanced regularization strategies and ensemble learning, we got top 7/336 places in the final leaderboard.
Automatic Generation of Personalized Comment Based on User Profile
Comments on social media are very diverse, in terms of content, style and vocabulary, which make generating comments much more challenging than other existing natural language generation~(NLG) tasks.
