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Found 25 papers, 7 papers with code
Pre-trained language models evaluating themselves - A comparative study
Evaluating generated text received new attention with the introduction of model-based metrics in recent years.
The Growth, Geography, and Implications of Trade in Digital Products
Despite global efforts to harmonize international trade statistics, our understanding of digital trade and its implications remains elusive.
A tailored Handwritten-Text-Recognition System for Medieval Latin
This dictionary entails record cards referring to lemmas in medieval Latin, a low-resource language.
Multimodal Deep Learning
This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually.
L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging
In this work, we show that while encoding the logic of a whole sleep cycle is crucial to improve sleep staging performance, the sequential modelling approach in existing state-of-the-art deep learning models are inefficient for that purpose.
The Role of Immigrants, Emigrants, and Locals in the Historical Formation of European Knowledge Agglomerations
grows with both, the presence of immigrants with knowledge on that activity and immigrants with knowledge in related activities.
Multidimensional Economic Complexity and Inclusive Green Growth
To achieve inclusive green growth, countries need to consider a multiplicity of economic, social, and environmental factors.
Polyphonic audio event detection: multi-label or multi-class multi-task classification problem?
The network is composed of a backbone subnet and multiple task-specific subnets.
SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification
It is based on the transformer backbone and offers interpretability of the model's decisions at both the epoch and sequence level.
Multi-view Audio and Music Classification
The learned embedding in the subnetworks are then concatenated to form the multi-view embedding for classification similar to a simple concatenation network.
Self-Attention Generative Adversarial Network for Speech Enhancement
Existing generative adversarial networks (GANs) for speech enhancement solely rely on the convolution operation, which may obscure temporal dependencies across the sequence input.
On Multitask Loss Function for Audio Event Detection and Localization
Audio event localization and detection (SELD) have been commonly tackled using multitask models.
XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging
This work proposes a sequence-to-sequence sleep staging model, XSleepNet, that is capable of learning a joint representation from both raw signals and time-frequency images.
Ranked #1 on
Sleep Stage Detection
on PhysioNet Challenge 2018
Personalized Automatic Sleep Staging with Single-Night Data: a Pilot Study with KL-Divergence Regularization
We employ the pretrained SeqSleepNet (i. e. the subject independent model) as a starting point and finetune it with the single-night personalization data to derive the personalized model.
Improving GANs for Speech Enhancement
The former constrains the generators to learn a common mapping that is iteratively applied at all enhancement stages and results in a small model footprint.
Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning
We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains: the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database.
Ranked #1 on
Multimodal Sleep Stage Detection
on Surrey-PSG
Automatic Sleep Stage Classification
Multimodal Sleep Stage Detection
+2
Deep Transfer Learning for Single-Channel Automatic Sleep Staging with Channel Mismatch
This work presents a deep transfer learning approach to overcome the channel mismatch problem and transfer knowledge from a large dataset to a small cohort to study automatic sleep staging with single-channel input.
Spatio-Temporal Attention Pooling for Audio Scene Classification
Acoustic scenes are rich and redundant in their content.
Unifying Isolated and Overlapping Audio Event Detection with Multi-Label Multi-Task Convolutional Recurrent Neural Networks
We propose a multi-label multi-task framework based on a convolutional recurrent neural network to unify detection of isolated and overlapping audio events.
Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene?
Moreover, as model fusion with deep network ensemble is prevalent in audio scene classification, we further study whether, and if so, when model fusion is necessary for this task.
Enabling Early Audio Event Detection with Neural Networks
The proposed system consists of a novel inference step coupled with dual parallel tailored-loss deep neural networks (DNNs).
Audio Scene Classification with Deep Recurrent Neural Networks
We introduce in this work an efficient approach for audio scene classification using deep recurrent neural networks.
CaR-FOREST: Joint Classification-Regression Decision Forests for Overlapping Audio Event Detection
The regression phase is then carried out to let the positive audio segments vote for the event onsets and offsets, and therefore model the temporal structure of audio events.
CNN-LTE: a Class of 1-X Pooling Convolutional Neural Networks on Label Tree Embeddings for Audio Scene Recognition
This category taxonomy is then used in the feature extraction step in which an audio scene instance is represented by a label tree embedding image.
Label Tree Embeddings for Acoustic Scene Classification
We present in this paper an efficient approach for acoustic scene classification by exploring the structure of class labels.
