snovais

advanced-pandas-experiments

analise_snis_datasus

api-express

Um repositório simples utilizado no curso denode.

app-tasks

App de tarefas desenvolvido em react-native acompanhando curso da Cod3r

curso-cplusplus

algumas implementações

gan-mnist-tensorflow

poo-cplusplus

pre_processing_data_weather

rnr-imdb

Rede Neural Recorrente (LSTM) para previsão de reviews no IMDB

weather_forecast

Previsão de temperatura, precipitação e velocidade do vento para uma cidade com ConvGRU

snovais/weather_forecast

Previsão de temperatura, precipitação e velocidade do vento para uma cidade com ConvGRU

snovais/poo-cplusplus

snovais/advanced-pandas-experiments

snovais/analise_snis_datasus

snovais/pre_processing_data_weather

snovais/rnr-imdb

Rede Neural Recorrente (LSTM) para previsão de reviews no IMDB

snovais/arima

snovais/ASV-anti-spoofing-with-EABN

snovais/c-language

snovais/como_a_rede_neural_aprende

snovais/cplusplus_stl

snovais/CREMA-D

Crowd Sourced Emotional Multimodal Actors Dataset (CREMA-D)

snovais/design-patterns-python

snovais/feedforward_torch

snovais/intro-bert

snovais/intro_to_spark

snovais/introducao_pytorch

snovais/introduction_keras

snovais/KerasBeats

Github Repository for the KerasBeats package. Easily import and use the NBeats NN architecture in Keras

snovais/open_cv_hello_world_cpp

snovais/reconhecimento_de_audios

snovais/redes-neurais-com-numpy-para-regress-o

snovais/redes_neurais_recorrentes

snovais/segm_detectron2

snovais/snovais

snovais/tf-aprendizado-transferencia-classificacao-gato-cachorro

snovais/timehetnet

Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set. Leveraging learning experience with similar datasets is a well-established technique for classification problems called few-shot classification. However, existing approaches cannot be applied to time-series forecasting because i) multivariate time-series datasets have different channels and ii) forecasting is principally different from classification. In this paper we formalize the problem of few-shot forecasting of time-series with heterogeneous channels for the first time. Extending recent work on heterogeneous attributes in vector data, we develop a model composed of permutation-invariant deep set-blocks which incorporate a temporal embedding. We assemble the first meta-dataset of 40 multivariate time-series datasets and show through experiments that our model provides a good generalization, outperforming baselines carried over from simpler scenarios that either fail to learn across tasks or miss temporal information.

snovais/todo

snovais/UniCL_copy

Copy of The official implementation for "Unified Contrastive Learning in Image-Text-Label Space. CVPR 2022"

snovais/yolo_basic