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Found 20 papers, 11 papers with code
DaG LLM ver 1.0: Pioneering Instruction-Tuned Language Modeling for Korean NLP
This paper presents the DaG LLM (David and Goliath Large Language Model), a language model specialized for Korean and fine-tuned through Instruction Tuning across 41 tasks within 13 distinct categories.
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Music Demixing Track
We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding1.
Sound Demixing Challenge 2023 Music Demixing Track Technical Report: TFC-TDF-UNet v3
In this report, we present our award-winning solutions for the Music Demixing Track of Sound Demixing Challenge 2023.
Ranked #4 on
Music Source Separation
on MUSDB18
Debiasing Neighbor Aggregation for Graph Neural Network in Recommender Systems
Graph neural networks (GNNs) have achieved remarkable success in recommender systems by representing users and items based on their historical interactions.
Meta-Learning for Online Update of Recommender Systems
It is featured with an adaptive learning rate for each parameter-interaction pair for inducing a recommender to quickly learn users' up-to-date interest.
Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data
A deep neural network (DNN) has achieved great success in many machine learning tasks by virtue of its high expressive power.
KUIELab-MDX-Net: A Two-Stream Neural Network for Music Demixing
This paper proposes a two-stream neural network for music demixing, called KUIELab-MDX-Net, which shows a good balance of performance and required resources.
Ranked #7 on
Music Source Separation
on MUSDB18
Deep Learning Based Resource Assignment for Wireless Networks
This paper studies a deep learning approach for binary assignment problems in wireless networks, which identifies binary variables for permutation matrices.
Music Demixing Challenge 2021
The main differences compared with the past challenges are 1) the competition is designed to more easily allow machine learning practitioners from other disciplines to participate, 2) evaluation is done on a hidden test set created by music professionals dedicated exclusively to the challenge to assure the transparency of the challenge, i. e., the test set is not accessible from anyone except the challenge organizers, and 3) the dataset provides a wider range of music genres and involved a greater number of mixing engineers.
AMSS-Net: Audio Manipulation on User-Specified Sources with Textual Queries
This paper proposes a neural network that performs audio transformations to user-specified sources (e. g., vocals) of a given audio track according to a given description while preserving other sources not mentioned in the description.
Robust Learning by Self-Transition for Handling Noisy Labels
In the seeding phase, the network is updated using all the samples to collect a seed of clean samples.
LaSAFT: Latent Source Attentive Frequency Transformation for Conditioned Source Separation
Recent deep-learning approaches have shown that Frequency Transformation (FT) blocks can significantly improve spectrogram-based single-source separation models by capturing frequency patterns.
Ranked #19 on
Music Source Separation
on MUSDB18
Learning from Noisy Labels with Deep Neural Networks: A Survey
Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data.
Investigating U-Nets with various Intermediate Blocks for Spectrogram-based Singing Voice Separation
Singing Voice Separation (SVS) tries to separate singing voice from a given mixed musical signal.
How does Early Stopping Help Generalization against Label Noise?
In this paper, we claim that such overfitting can be avoided by "early stopping" training a deep neural network before the noisy labels are severely memorized.
Carpe Diem, Seize the Samples Uncertain "At the Moment" for Adaptive Batch Selection
Compared with existing batch selection methods, the results showed that Recency Bias reduced the test error by up to 20. 97% in a fixed wall-clock training time.
Prestopping: How Does Early Stopping Help Generalization Against Label Noise?
In this paper, we claim that such overfitting can be avoided by "early stopping" training a deep neural network before the noisy labels are severely memorized.
SELFIE: Refurbishing Unclean Samples for Robust Deep Learning
Owing to the extremely high expressive power of deep neural networks, their side effect is to totally memorize training data even when the labels are extremely noisy.
Ranked #10 on
Learning with noisy labels
on ANIMAL
Ada-Boundary: Accelerating the DNN Training via Adaptive Boundary Batch Selection
Neural networks can converge faster with help from a smarter batch selection strategy.
Training IBM Watson using Automatically Generated Question-Answer Pairs
Under the assumption that using such an automatically generated dataset could relieve the burden of manual question-answer generation, we tried to use this dataset to train an instance of Watson and checked the training efficiency and accuracy.
