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Found 11 papers, 0 papers with code
INS-Conv: Incremental Sparse Convolution for Online 3D Segmentation
Based on INS-Conv, an online joint 3D semantic and instance segmentation pipeline is proposed, reaching an inference speed of 15 FPS on GPU and 10 FPS on CPU.
Toward a Taxonomy of Trust for Probabilistic Machine Learning
Probabilistic machine learning increasingly informs critical decisions in medicine, economics, politics, and beyond.
Artificial Perceptual Learning: Image Categorization with Weak Supervision
In this paper, we propose a novel machine learning framework, artificial perceptual learning (APL), to tackle the problem of weakly supervised image categorization.
Weakly Supervised Learning Creates a Fusion of Modeling Cultures
The past two decades have witnessed the great success of the algorithmic modeling framework advocated by Breiman et al. (2001).
Network Hawkes Process Models for Exploring Latent Hierarchy in Social Animal Interactions
We compare all models using simulated and real data.
Applications Methodology
Online Community Detection for Event Streams on Networks
To circumvent this challenge, we propose a fast online variational inference algorithm for learning the community structure underlying dynamic event arrivals on a network using continuous-time point process latent network models.
Next Waves in Veridical Network Embedding
Embedding nodes of a large network into a metric (e. g., Euclidean) space has become an area of active research in statistical machine learning, which has found applications in natural and social sciences.
OccuSeg: Occupancy-aware 3D Instance Segmentation
3D instance segmentation, with a variety of applications in robotics and augmented reality, is in large demands these days.
Ranked #1 on
3D Instance Segmentation
on SceneNN
Stabilized Sparse Online Learning for Sparse Data
To mitigate this issue, we introduce a stabilized truncated stochastic gradient descent algorithm.
Boosted Sparse Non-linear Distance Metric Learning
In this paper, we propose a nonlinear sparse metric learning algorithm via boosting.
Topic-adjusted visibility metric for scientific articles
In this work, we address the problem of field variation and introduce an article level metric useful for evaluating individual articles' visibility.
