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Found 15 papers, 7 papers with code
Providing theoretical learning guarantees to Deep Learning Networks
Deep Learning (DL) is one of the most common subjects when Machine Learning and Data Science approaches are considered.
Computing the Shattering Coefficient of Supervised Learning Algorithms
The Statistical Learning Theory (SLT) provides the theoretical guarantees for supervised machine learning based on the Empirical Risk Minimization Principle (ERMP).
Como funciona o Deep Learning
Deep Learning methods are currently the state-of-the-art in many problems which can be tackled via machine learning, in particular classification problems.
Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis
The models are evaluated considering both the reconstruction error of the images and the feature spaces in terms of their discriminative power.
Generalization of feature embeddings transferred from different video anomaly detection domains
By proposing novel cross-domain generalization measures, we study how source features can generalize for different target video domains, as well as analyze unsupervised transfer learning.
Speech2Phone: A Novel and Efficient Method for Training Speaker Recognition Models
We compare the three best architectures trained using our method to select the best one, which is the one with a shallow architecture.
TTS-Portuguese Corpus: a corpus for speech synthesis in Brazilian Portuguese
Speech provides a natural way for human-computer interaction.
SC-GlowTTS: an Efficient Zero-Shot Multi-Speaker Text-To-Speech Model
In this paper, we propose SC-GlowTTS: an efficient zero-shot multi-speaker text-to-speech model that improves similarity for speakers unseen during training.
Training Deep Networks from Zero to Hero: avoiding pitfalls and going beyond
Training deep neural networks may be challenging in real world data.
YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyone
YourTTS brings the power of a multilingual approach to the task of zero-shot multi-speaker TTS.
ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversion
We explore cross-lingual multi-speaker speech synthesis and cross-lingual voice conversion applied to data augmentation for automatic speech recognition (ASR) systems in low/medium-resource scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Improving Data Quality with Training Dynamics of Gradient Boosting Decision Trees
Real world datasets contain incorrectly labeled instances that hamper the performance of the model and, in particular, the ability to generalize out of distribution.
Sketch-an-Anchor: Sub-epoch Fast Model Adaptation for Zero-shot Sketch-based Image Retrieval
Sketch-an-Anchor is a novel method to train state-of-the-art Zero-shot Sketch-based Image Retrieval (ZSSBIR) models in under an epoch.
Dendrogram distance: an evaluation metric for generative networks using hierarchical clustering
We present a novel metric for generative modeling evaluation, focusing primarily on generative networks.
Decoupling Decision-Making in Fraud Prevention through Classifier Calibration for Business Logic Action
Machine learning models typically focus on specific targets like creating classifiers, often based on known population feature distributions in a business context.
