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Found 13 papers, 3 papers with code
Efficient Truncated Linear Regression with Unknown Noise Variance
In this paper, we provide the first computationally and statistically efficient estimators for truncated linear regression when the noise variance is unknown, estimating both the linear model and the variance of the noise.
A variational autoencoder approach for choice set generation and implicit perception of alternatives in choice modeling
This paper derives the generalized extreme value (GEV) model with implicit availability/perception (IAP) of alternatives and proposes a variational autoencoder (VAE) approach for choice set generation and implicit perception of alternatives.
Encoding Frequency Constraints in Preventive Unit Commitment Using Deep Learning with Region-of-Interest Active Sampling
Frequency-constrained unit commitment (FCUC) is proposed to address this challenge.
CycleSegNet: Object Co-segmentation with Cycle Refinement and Region Correspondence
Image co-segmentation is an active computer vision task that aims to segment the common objects from a set of images.
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control
Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem.
Deep Active Learning for Solvability Prediction in Power Systems
Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism.
Experiments on route choice set generation using a large GPS trajectory set
A modified link penalty method, which accounts for preference of using higher hierarchical roads, provides a route set with 97% coverage (80% overlap threshold).
Vehicle Re-Identification Based on Complementary Features
Top performance in City-Scale Multi-Camera Vehicle Re-Identification demonstrated the advantage of our methods, and we got 5-th place in the vehicle Re-ID track of AIC2020.
Video Object Segmentation and Tracking: A Survey
Second, we provide a detailed discussion and overview of the technical characteristics of the different methods.
CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning
Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets.
Ranked #13 on
Few-Shot Semantic Segmentation
on PASCAL-5i (1-Shot)
Efficient Eye Typing with 9-direction Gaze Estimation
According to the results from our experiments, our CNN model is able to accurately estimate different people's gaze under various lighting conditions by different devices.
Efficient Dense Labeling of Human Activity Sequences from Wearables using Fully Convolutional Networks
We conduct extensive experiments and demonstrate that our proposed approach is able to outperform the state-of-the-arts in terms of classification and label misalignment measures on three challenging datasets: Opportunity, Hand Gesture, and our new dataset.
Part-Based Visual Tracking with Online Latent Structural Learning
Despite many advances made in the area, deformable targets and partial occlusions continue to represent key problems in visual tracking.
