Search Results for author:
Found 9 papers, 3 papers with code
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.
