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Found 10 papers, 3 papers with code
Learned Feature Importance Scores for Automated Feature Engineering
Feature engineering has demonstrated substantial utility for many machine learning workflows, such as in the small data regime or when distribution shifts are severe.
ASPEST: Bridging the Gap Between Active Learning and Selective Prediction
In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain while increasing accuracy and coverage.
Data-Efficient and Interpretable Tabular Anomaly Detection
In addition, the proposed framework, DIAD, can incorporate a small amount of labeled data to further boost anomaly detection performances in semi-supervised settings.
Decoupling Local and Global Representations of Time Series
Learning representations that capture the factors contributing to this variability enables a better understanding of the data via its underlying generative process and improves performance on downstream machine learning tasks.
Distance-Based Learning from Errors for Confidence Calibration
To circumvent this by inferring the distance for every test sample, we propose to train a confidence model jointly with the classification model.
On Concept-Based Explanations in Deep Neural Networks
Next, we propose a concept discovery method that considers two additional constraints to encourage the interpretability of the discovered concepts.
EPNAS: Efficient Progressive Neural Architecture Search
In this paper, we propose Efficient Progressive Neural Architecture Search (EPNAS), a neural architecture search (NAS) that efficiently handles large search space through a novel progressive search policy with performance prediction based on REINFORCE~\cite{Williams. 1992. PG}.
HybridNet: A Hybrid Neural Architecture to Speed-up Autoregressive Models
This paper introduces HybridNet, a hybrid neural network to speed-up autoregressive models for raw audio waveform generation.
Deep Voice 2: Multi-Speaker Neural Text-to-Speech
We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1.
Supervised classification-based stock prediction and portfolio optimization
In this work, we apply machine learning techniques to address automated stock picking, while using a larger number of financial parameters for individual companies than the previous studies.
