11 citations · 26 across the 7 of their papers we have counts for
7 papers
Masked Generative Modeling with Enhanced Sampling Scheme
Daesoo Lee, Erlend Aune, Sara Malacarne
This paper presents a novel sampling scheme for masked non-autoregressive generative modeling. We identify the limitations of TimeVQVAE, MaskGIT, and Token-Critic in their sampling…
Vector Quantized Time Series Generation with a Bidirectional Prior Model
Daesoo Lee, Sara Malacarne, Erlend Aune
Time series generation (TSG) studies have mainly focused on the use of Generative Adversarial Networks (GANs) combined with recurrent neural network (RNN) variants. However, the fu…
Persistence Initialization: A novel adaptation of the Transformer architecture for Time Series Forecasting
Espen Haugsdal, Erlend Aune, Massimiliano Ruocco
Time series forecasting is an important problem, with many real world applications. Ensembles of deep neural networks have recently achieved impressive forecasting accuracy, but su…
VNIbCReg: VICReg with Neighboring-Invariance and better-Covariance Evaluated on Non-stationary Seismic Signal Time Series
Daesoo Lee, Erlend Aune, Nadège Langet +1
One of the latest self-supervised learning (SSL) methods, VICReg, showed a great performance both in the linear evaluation and the fine-tuning evaluation. However, VICReg is propos…
Augmented Memory Networks for Streaming-Based Active One-Shot Learning
Andreas Kvistad, Massimiliano Ruocco, Eliezer de Souza da Silva +1
One of the major challenges in training deep architectures for predictive tasks is the scarcity and cost of labeled training data. Active Learning (AL) is one way of addressing thi…
Time is of the Essence: a Joint Hierarchical RNN and Point Process Model for Time and Item Predictions
Bjørnar Vassøy, Massimiliano Ruocco, Eliezer de Souza da Silva +1
In recent years session-based recommendation has emerged as an increasingly applicable type of recommendation. As sessions consist of sequences of events, this type of recommendati…