Search Results for author:
Found 25 papers, 10 papers with code
Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph Fusion
To address this issue, this paper proposes a graph learning-based node ranking method (MGL2Rank) that integrates the rich characteristics of the road network.
TraffNet: Learning Causality of Traffic Generation for Road Network Digital Twins
Therefore, their performance is unreliable for JIT decision making.
Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths
An interesting byproduct of this formulation is that DecDTW outputs the optimal warping path between two time series as opposed to a soft approximation, recoverable from Soft-DTW.
Cognitive Semantic Communication Systems Driven by Knowledge Graph: Principle, Implementation, and Performance Evaluation
Extensive simulation results conducted on a public dataset demonstrate that our proposed single-user and multi-user cognitive semantic communication systems are superior to benchmark communication systems in terms of the data compression rate and communication reliability.
Futuristic Variations and Analysis in Fundus Images Corresponding to Biological Traits
Our proposed DL models, named FAG-Net and FGC-Net, correspondingly estimate biological traits (age and gender) and generates fundus images.
Residual Skill Policies: Learning an Adaptable Skill-based Action Space for Reinforcement Learning for Robotics
Furthermore, the downstream RL agent is limited to learning structurally similar tasks to those used to construct the skill space.
Neuro-Symbolic Learning: Principles and Applications in Ophthalmology
Neural networks have been rapidly expanding in recent years, with novel strategies and applications.
3D Random Occlusion and Multi-Layer Projection for Deep Multi-Camera Pedestrian Localization
Although deep-learning based methods for monocular pedestrian detection have made great progress, they are still vulnerable to heavy occlusions.
Improving Worst Case Visual Localization Coverage via Place-specific Sub-selection in Multi-camera Systems
6-DoF visual localization systems utilize principled approaches rooted in 3D geometry to perform accurate camera pose estimation of images to a map.
Improving Road Segmentation in Challenging Domains Using Similar Place Priors
These approaches share one or more of three significant limitations: a reliance on large amounts of annotated training data that can be costly to obtain, both anticipation of and training data from the type of environmental conditions expected at inference time, and/or imagery captured from a previous visit to the location.
Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference
Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions.
FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction
Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data.
Sharing Behavior in Ride-hailing Trips: A Machine Learning Inference Approach
Using ensemble machine learning models, we find that the travel impedance variables (trip cost, distance, and duration) collectively contribute to 95% and 91% of the predictive power in determining whether a trip is requested to share and whether it is successfully shared, respectively.
Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise
We show that our method successfully alleviates the damage of both label noise and confirmation bias.
Ranked #2 on
Image Classification
on mini WebVision 1.0
Interactive Model with Structural Loss for Language-based Abductive Reasoning
Therefore, we propose to group instead of ranking the hypotheses and design a structural loss called ``joint softmax focal loss'' in this paper.
Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness
Probabilistic state-estimation approaches offer a principled foundation for designing localization systems, because they naturally integrate sequences of imperfect motion and exteroceptive sensor data.
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction.
Probabilistic Visual Place Recognition for Hierarchical Localization
In this letter, we propose two methods which adapt image retrieval techniques used for visual place recognition to the Bayesian state estimation formulation for localization.
Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition
Visual Place Recognition is a challenging task for robotics and autonomous systems, which must deal with the twin problems of appearance and viewpoint change in an always changing world.
Ranked #1 on
Visual Localization
on RobotCar Seasons v2
Triaging moderate COVID-19 and other viral pneumonias from routine blood tests
The COVID-19 is sweeping the world with deadly consequences.
EPINE: Enhanced Proximity Information Network Embedding
Unsupervised homogeneous network embedding (NE) represents every vertex of networks into a low-dimensional vector and meanwhile preserves the network information.
Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound
Our attention module utilizes the attention mechanism to selectively leverage the multilevel features integrated from different layers to refine the features at each individual layer, suppressing the non-prostate noise at shallow layers of the CNN and increasing more prostate details into features at deep layers.
Variance reduction properties of the reparameterization trick
From this, we show that the marginal variances of the reparameterization gradient estimator are smaller than those of the score function gradient estimator.
Discovery of Important Crossroads in Road Network using Massive Taxi Trajectories
However, none of existing approaches addresses the problem of identifying network-wide important crossroads in real road network.
Adaptive Mesh Representation and Restoration of Biomedical Images
The triangulation of images has become an active research area in recent years for its compressive representation and ease of image processing and visualization.
