We present MELLE, a novel continuous-valued tokens based language modeling approach for text to speech synthesis (TTS). MELLE autoregressively generates continuous mel-spectrogram frames directly from ...

├── src/ # Source code │ ├── data_utils.py # Data generation and loading utilities │ ├── models.py # Time series forecasting models │ ├── visualization.py # Visualization utilities │ ├── main.py # ...

Every time a language model like GPT-4, Claude or Mistral generates a sentence, it does something deceptively simple: It picks one word at a time. This word-by-word approach is what gives ...

This study examines how trade shocks that start in one large economy ripple through other countries and how long those effects stick around. Using quarterly bilateral-trade data for 2012-2023, the ...

Objective: This study aimed to develop depression incidence forecasting models and compare the performance of autoregressive integrated moving average (ARIMA) and vector-ARIMA (VARIMA) and temporal ...

One of the core problems with AI is the notoriously high power and computing demand, especially for tasks such as media generation. On mobile phones, when it comes to running natively, only a handful ...

Abstract: To handle complex non-linearity and latent dynamics in industrial processes, this paper proposes a kernel latent vector auto-regressive (K-LaVAR) algorithm for nonlinear dynamic process ...

ABSTRACT: To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models.

Autoregressive LLMs are complex neural networks that generate coherent and contextually relevant text through sequential prediction. These LLms excel at handling large datasets and are very strong at ...

Abstract: The vector autoregressive (VAR) model is extensively employed for modelling dynamic processes, yet its scalability is challenged by an overwhelming growth in parameters when dealing with ...