Search Results for author:
Found 14 papers, 6 papers with code
Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control
The ongoing transition to renewable energy is increasing the share of fluctuating power sources like wind and solar, raising power grid volatility and making grid operation increasingly complex and costly.
Power Grid Congestion Management via Topology Optimization with AlphaZero
Unfortunately, redispatching of fossil generators leads to excessive grid operation costs and higher emissions, which is in direct opposition to the decarbonization of the energy sector.
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
For such complex tasks, the recently proposed RUDDER uses reward redistribution to leverage steps in the Q-function that are associated with accomplishing sub-tasks.
General Reinforcement Learning
Multiple Sequence Alignment
+1
The Receptive Field as a Regularizer in Deep Convolutional Neural Networks for Acoustic Scene Classification
To this end, we analyse the receptive field (RF) of these CNNs and demonstrate the importance of the RF to the generalization capability of the models.
Learning Soft-Attention Models for Tempo-invariant Audio-Sheet Music Retrieval
Quantitative and qualitative results on synthesized piano data indicate that this attention increases the robustness of the retrieval system by focusing on different parts of the input representation based on the tempo of the audio.
Cross-Modal Music Retrieval and Applications: An Overview of Key Methodologies
There has been a rapid growth of digitally available music data, including audio recordings, digitized images of sheet music, album covers and liner notes, and video clips.
Attention as a Perspective for Learning Tempo-invariant Audio Queries
Current models for audio--sheet music retrieval via multimodal embedding space learning use convolutional neural networks with a fixed-size window for the input audio.
Learning to Listen, Read, and Follow: Score Following as a Reinforcement Learning Game
Score following is the process of tracking a musical performance (audio) with respect to a known symbolic representation (a score).
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning
To tackle this problem, we propose Deep Within-Class Covariance Analysis (DWCCA), a deep neural network layer that significantly reduces the within-class covariance of a DNN's representation, improving performance on unseen test data from a shifted distribution.
A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification
Finally, we propose a hybrid system for ASC using multi-channel i-vectors and CNNs by utilizing a score fusion technique.
On the Potential of Simple Framewise Approaches to Piano Transcription
In an attempt at exploring the limitations of simple approaches to the task of piano transcription (as usually defined in MIR), we conduct an in-depth analysis of neural network-based framewise transcription.
Towards End-to-End Audio-Sheet-Music Retrieval
This paper demonstrates the feasibility of learning to retrieve short snippets of sheet music (images) when given a short query excerpt of music (audio) -- and vice versa --, without any symbolic representation of music or scores.
Towards Score Following in Sheet Music Images
This paper addresses the matching of short music audio snippets to the corresponding pixel location in images of sheet music.
Deep Linear Discriminant Analysis
The central idea of this paper is to put LDA on top of a deep neural network.
