Results 211 to 220 of about 75,217 (233)

Practical Generation of Video Textures using the Auto-Regressive Process

Procedings of the British Machine Vision Conference 2002, 2002
Recently, there have been several attempts at creating `video textures', that is, synthesising new (potentially infinitely long) video clips based on existing ones. One way to do this is to transform each frame of the video into an eigenspace using Principal Components Analysis so that the original sequence can be viewed as a signature through this low-
Campbell, NW, Dalton, C, Gibson, D, Thomas, B   +3 more
openaire   +1 more source

ALARM: A logistic auto-regressive model for binary processes on networks

2013 IEEE Global Conference on Signal and Information Processing, 2013
We introduce the ALARM model, a logistic autoregressive model for discrete-time binary processes on networks, and describe a technique for learning the graph structure underlying the model from observations. Using only a small number of parameters, the proposed ALARM can describe a wide range of dynamic behavior on graphs, such as the contact process ...
Ameya Agaskar, Yue M. Lu
openaire   +1 more source

MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling

Computer Vision and Pattern Recognition
Recent advancements in multi-modal large language models have propelled the development of joint probabilistic models capable of both image understanding and generation.
Jian Yang, Dacheng Yin, Yizhou Zhou, Fengyun Rao, Wei Zhai, Yang Cao, Zhengjun Zha   +6 more
semanticscholar   +1 more source

Fast and High-Quality Auto-Regressive Speech Synthesis via Speculative Decoding

IEEE International Conference on Acoustics, Speech, and Signal Processing
The auto-regressive (AR) architecture, exemplified by models such as GPT, is extensively utilized in modern Text-to-Speech (TTS) systems. However, it often leads to considerable inference delays, primarily due to the challenges associated with next-token
Bohan Li, Hankun Wang, Situo Zhang, Yiwei Guo, Kai Yu   +4 more
semanticscholar   +1 more source

Memory-Augmented Auto-Regressive Network for Frame Recurrent Inter Prediction

International Symposium on Circuits and Systems, 2020
Inter prediction is quite important for the modern codecs to remove temporal redundancy. In this paper, we make endeavors in generating artificial reference frames with previous reconstructed frames for inter prediction, to offer a better choice when the
Yuzhang Hu, Sifeng Xia, Wenhan Yang, Jiaying Liu   +3 more
semanticscholar   +1 more source

GLM based auto-regressive process to model Covid-19 pandemic in Turkey

Statistical Communications in Infectious Diseases, 2021
Abstract Objectives: Our objective is to propose a robust approach to model daily new cases and daily new deaths due to covid-19 infection in Turkey. Methods: We consider the generalized linear model (GLM) approach for the autoregressive process (AR) with log link for modelling.
openaire   +2 more sources

BAD: Bidirectional Auto-Regressive Diffusion for Text-to-Motion Generation

IEEE International Conference on Acoustics, Speech, and Signal Processing
Autoregressive models excel in modeling sequential dependencies by enforcing causal constraints, yet they struggle to capture complex bidirectional patterns due to their unidirectional nature. In contrast, mask-based models leverage bidirectional context,
Seyed Rohollah Hosseyni, Ali Ahmad Rahmani, Seyed Jamal Seyed-Mohammadi, Sanaz Seyedin, Arash Mohammadi   +4 more
semanticscholar   +1 more source

A weighted auto regressive LSTM based approach for chemical processes modeling

Neurocomputing, 2019
Abstract Data-driven methods have been regarded as effective methods for modeling in chemical processes. However, with the increasing complexity of chemical processes in spatial domain and time domain, how to extract meaningful features and build corresponding models are keys for accurate modeling tasks. To retain temporal features of original inputs,
Xu Zhang, Yuanyuan Zou, Shaoyuan Li, Shenghu Xu   +3 more
openaire   +1 more source

An Efficient Dynamic Auto-Regressive CCA for Time Series Imputation With Irregular Sampling

IEEE Transactions on Automation Science and Engineering
System dynamics are inevitable in industrial processes due to factors such as ambient disturbances and controller tuning. Accurate modeling of these dynamics are of key importance for subsequent process analysis and anomaly detection, and dynamic latent ...
Bo Xu, Qinqin Zhu
semanticscholar   +1 more source

Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching

International Conference on Learning Representations
Autoregressive (AR) models have achieved state-of-the-art performance in text and image generation but suffer from slow generation due to the token-by-token process.
En-hao Liu, Xuefei Ning, Yu Wang, Zinan Lin   +3 more
semanticscholar   +1 more source