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Found 27 papers, 9 papers with code
Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer
We propose Deep Longitudinal Targeted Minimum Loss-based Estimation (Deep LTMLE), a novel approach to estimate the counterfactual mean of outcome under dynamic treatment policies in longitudinal problem settings.
ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems
Machine learning (ML) methods are widely used in industrial applications, which usually require a large amount of training data.
LSKNet: A Foundation Lightweight Backbone for Remote Sensing
While a considerable amount of research has been dedicated to remote sensing classification, object detection and semantic segmentation, most of these studies have overlooked the valuable prior knowledge embedded within remote sensing scenarios.
Not Just Change the Labels, Learn the Features: Watermarking Deep Neural Networks with Multi-View Data
Unfortunately, existing methodologies based on trigger sets are still susceptible to functionality-stealing attacks, potentially enabling adversaries to steal the functionality of the source model without a reliable means of verifying ownership.
SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection
To the best of our knowledge, SARDet-100K is the first COCO-level large-scale multi-class SAR object detection dataset ever created.
Ranked #1 on
2D Object Detection
on SARDet-100K
(using extra training data)
Understanding Representations Pretrained with Auxiliary Losses for Embodied Agent Planning
Surprisingly, we find that imitation learning on these exploration trajectories out-performs all other auxiliary losses even despite the exploration trajectories being dissimilar from the downstream tasks.
Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Additive Manufacturing
The incorporation of advanced sensors and machine learning techniques has enabled modern manufacturing enterprises to perform data-driven in-situ quality monitoring based on the sensor data collected in manufacturing processes.
Predicting Gradient is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture
Besides, we employ local masks and multi-scale features to accommodate the various small targets in remote sensing.
SpectralGPT: Spectral Remote Sensing Foundation Model
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner.
Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e. g., single cities or regions).
Unified Statistical Channel Modeling and performance analysis of Vertical Underwater Wireless Optical Communication Links considering Turbulence-Induced Fading
The reliability of a vertical underwater wireless optical communication (UWOC) network is seriously impacted by turbulence-induced fading due to fluctuations in the water temperature and salinity, which vary with depth.
Attention-stacked Generative Adversarial Network (AS-GAN)-empowered Sensor Data Augmentation for Online Monitoring of Manufacturing System
To generate the high-quality minority samples, it is vital to learn the underlying distribution of the abnormal states data.
Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?
Diffusion models have recently achieved astonishing performance in generating high-fidelity photo-realistic images.
Genetic Analysis of Prostate Cancer with Computer Science Methods
Metastatic prostate cancer is one of the most common cancers in men.
Robust Mode Connectivity-Oriented Adversarial Defense: Enhancing Neural Network Robustness Against Diversified $\ell_p$ Attacks
Adversarial robustness is a key concept in measuring the ability of neural networks to defend against adversarial attacks during the inference phase.
Large Selective Kernel Network for Remote Sensing Object Detection
To the best of our knowledge, this is the first time that large and selective kernel mechanisms have been explored in the field of remote sensing object detection.
Ranked #1 on
Semantic Segmentation
on UAVid
Knowledge Distillation-based Information Sharing for Online Process Monitoring in Decentralized Manufacturing System
In a decentralized manufacturing system, the involved units may fabricate same or similar products and deploy their own machine learning model for online process monitoring.
Stimulus Verification Is a Universal and Effective Sampler in Multi-Modal Human Trajectory Prediction
To comprehensively cover the uncertainty of the future, the common practice of multi-modal human trajectory prediction is to first generate a set/distribution of candidate future trajectories and then sample required numbers of trajectories from them as final predictions.
Multimodal Remote Sensing Image Registration Based on Adaptive Multi-scale PIIFD
Therefore, this paper proposes a multimodal remote sensing image registration method based on adaptive multi-scale PIIFD(AM-PIIFD).
Systematic Generalization and Emergent Structures in Transformers Trained on Structured Tasks
Transformer networks have seen great success in natural language processing and machine vision, where task objectives such as next word prediction and image classification benefit from nuanced context sensitivity across high-dimensional inputs.
Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow
Although traditional optimization techniques, such as stochastic and robust optimization approaches, could be leveraged to address the OPF problem, in the face of renewable energy uncertainty, i. e., the dynamic coefficients in the optimization model, their effectiveness in dealing with large-scale problems remains limited.
A Generative Adversarial Network-based Selective Ensemble Characteristic-to-Expression Synthesis (SE-CTES) Approach and Its Applications in Healthcare
Investigating the causal relationships between characteristics and expressions plays a critical role in healthcare analytics.
UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment Metrics
Achieving subjective and objective quality assessment of underwater images is of high significance in underwater visual perception and image/video processing.
Human Trajectory Prediction With Momentary Observation
Human trajectory prediction task aims to analyze human future movements given their past status, which is a crucial step for many autonomous systems such as self-driving cars and social robots.
Pseudo-Model-Free Hedging for Variable Annuities via Deep Reinforcement Learning
This paper proposes a two-phase deep reinforcement learning approach, for hedging variable annuity contracts with both GMMB and GMDB riders, which can address model miscalibration in Black-Scholes financial and constant force of mortality actuarial market environments.
Three Steps to Multimodal Trajectory Prediction: Modality Clustering, Classification and Synthesis
Multimodal prediction results are essential for trajectory prediction task as there is no single correct answer for the future.
NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control
Our meta policy tries to manipulate the next optimal state and actual action is produced by the inverse dynamics model.
