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Found 19 papers, 6 papers with code
Methodology for Capacity Credit Evaluation of Physical and Virtual Energy Storage in Decarbonized Power System
Energy storage (ES) and virtual energy storage (VES) are key components to realizing power system decarbonization.
Approximately optimal domain adaptation with Fisher's Linear Discriminant Analysis
We propose a class of models based on Fisher's Linear Discriminant (FLD) in the context of domain adaptation.
Efficient Reinforcement Learning Through Trajectory Generation
Off-policy and Offline RL methods have been proposed to reduce the number of interactions with the physical environment by learning control policies from historical data.
Deep Learning with Label Noise: A Hierarchical Approach
Our hierarchical approach improves upon regular deep neural networks in learning with label noise.
Mental State Classification Using Multi-graph Features
We consider the problem of extracting features from passive, multi-channel electroencephalogram (EEG) devices for downstream inference tasks related to high-level mental states such as stress and cognitive load.
A Frequency Domain Approach to Predict Power System Transients
System topology and fault information are encoded by taking a multi-dimensional Fourier transform, allowing us to leverage the fact that the trajectories are sparse both in time and spatial frequencies.
Leveraging semantically similar queries for ranking via combining representations
In modern ranking problems, different and disparate representations of the items to be ranked are often available.
Learning without gradient descent encoded by the dynamics of a neurobiological model
The success of state-of-the-art machine learning is essentially all based on different variations of gradient descent algorithms that minimize some version of a cost or loss function.
Inducing a hierarchy for multi-class classification problems
In applications where categorical labels follow a natural hierarchy, classification methods that exploit the label structure often outperform those that do not.
A partition-based similarity for classification distributions
Herein we define a measure of similarity between classification distributions that is both principled from the perspective of statistical pattern recognition and useful from the perspective of machine learning practitioners.
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
We examine two related, complementary inference tasks: the detection of anomalous graphs within a time series, and the detection of temporally anomalous vertices.
Methodology
Using Mobility for Electrical Load Forecasting During the COVID-19 Pandemic
In this work, we focus on the problem of load forecasting.
Distance-based Positive and Unlabeled Learning for Ranking
Learning to rank -- producing a ranked list of items specific to a query and with respect to a set of supervisory items -- is a problem of general interest.
Omnidirectional Transfer for Quasilinear Lifelong Learning
But striving to avoid forgetting sets the goal unnecessarily low: the goal of lifelong learning, whether biological or artificial, should be to improve performance on all tasks (including past and future) with any new data.
A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability
Multilingual topic models (MTMs) learn topics on documents in multiple languages.
Adapting Topic Models using Lexical Associations with Tree Priors
Models work best when they are optimized taking into account the evaluation criteria that people care about.
