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Found 26 papers, 14 papers with code
Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"
In the first step, we train different sub-predictors on different types of available low-fidelity information to extract beneficial knowledge as low-fidelity experts.
Distill the Image to Nowhere: Inversion Knowledge Distillation for Multimodal Machine Translation
Thus, in this work, we introduce IKD-MMT, a novel MMT framework to support the image-free inference phase via an inversion knowledge distillation scheme.
SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning
Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth.
ProMix: Combating Label Noise via Maximizing Clean Sample Utility
Combining with the small-loss selection, our method is able to achieve a precision of 99. 27 and a recall of 98. 22 in detecting clean samples on the CIFAR-10N dataset.
Ranked #1 on
Learning with noisy labels
on CIFAR-10N-Worst
Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis
The aspect-based sentiment analysis (ABSA) is a fine-grained task that aims to determine the sentiment polarity towards targeted aspect terms occurring in the sentence.
Ranked #4 on
Aspect-Based Sentiment Analysis (ABSA)
on SemEval 2014 Task 4 Sub Task 2
(using extra training data)
Aspect-Based Sentiment Analysis (ABSA)
Multi-Task Learning
+1
PiCO+: Contrastive Label Disambiguation for Robust Partial Label Learning
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity.
Robust Data-Driven Linear Power Flow Model with Probability Constrained Worst-Case Errors
To limit the probability of unacceptable worst-case linearization errors that might yield risks for power system operations, this letter proposes a robust data-driven linear power flow (RD-LPF) model.
Deps-SAN: Neural Machine Translation with Dependency-Scaled Self-Attention Network
However, succeeding researches pointed out that limited by the uncontrolled nature of attention computation, the NMT model requires an external syntax to capture the deep syntactic awareness.
Contrastive Label Disambiguation for Partial Label Learning
Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity.
VeniBot: Towards Autonomous Venipuncture with Semi-supervised Vein Segmentation from Ultrasound Images
In the modern medical care, venipuncture is an indispensable procedure for both diagnosis and treatment.
VeniBot: Towards Autonomous Venipuncture with Automatic Puncture Area and Angle Regression from NIR Images
For the automation, we focus on the positioning part and propose a Dual-In-Dual-Out network based on two-step learning and two-task learning, which can achieve fully automatic regression of the suitable puncture area and angle from near-infrared(NIR) images.
A robust extended Kalman filter for power system dynamic state estimation using PMU measurements
Simulations carried out on the IEEE 39-bus test system reveal that our robust extended Kalman filter exhibits good tracking capabilities under Gaussian process and observation noise while suppressing observation outliers, even in position of leverage.
Discovering Robust Convolutional Architecture at Targeted Capacity: A Multi-Shot Approach
In this paper, considering scenarios with capacity budget, we aim to discover adversarially robust architecture at targeted capacities.
Data-Driven Assisted Chance-Constrained Energy and Reserve Scheduling with Wind Curtailment
Case studies performed on the PJM 5-bus and IEEE 118-bus systems demonstrate that the proposed method is capable of accurately accounting the influence of wind curtailment dispatch in CCO.
Systems and Control Systems and Control
PIANOTREE VAE: Structured Representation Learning for Polyphonic Music
The dominant approach for music representation learning involves the deep unsupervised model family variational autoencoder (VAE).
Model-Free Voltage Regulation of Unbalanced Distribution Network Based on Surrogate Model and Deep Reinforcement Learning
Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage controls, but this is difficult to obtain in practice.
Distributed Voltage Regulation of Active Distribution System Based on Enhanced Multi-agent Deep Reinforcement Learning
This paper proposes a data-driven distributed voltage control approach based on the spectrum clustering and the enhanced multi-agent deep reinforcement learning (MADRL) algorithm.
Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach
This paper proposes a data-driven approach for optimal power flow (OPF) based on the stacked extreme learning machine (SELM) framework.
BIG-bench Machine Learning
Physics-informed machine learning
Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation
Power system dynamic state estimation (DSE) remains an active research area.
Prediction Under Uncertainty with Error Encoding Networks
In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty.
Prediction Under Uncertainty with Error-Encoding Networks
In this work we introduce a new framework for performing temporal predictions in the presence of uncertainty.
Disentangling factors of variation in deep representation using adversarial training
The only available source of supervision during the training process comes from our ability to distinguish among different observations belonging to the same category.
Disentangling factors of variation in deep representations using adversarial training
During training, the only available source of supervision comes from our ability to distinguish among different observations belonging to the same class.
Energy-based Generative Adversarial Network
We introduce the "Energy-based Generative Adversarial Network" model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions.
Character-level Convolutional Networks for Text Classification
This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification.
Ranked #16 on
Sentiment Analysis
on Yelp Fine-grained classification
Stacked What-Where Auto-encoders
The objective function includes reconstruction terms that induce the hidden states in the Deconvnet to be similar to those of the Convnet.
