A method speeds up generation in convolutional autoregressive models.
problem Slow generation in convolutional autoregressive models.
method Cache hidden states to avoid redundant computation.
result Up to 21x and 183x speedups in generation for Wavenet and PixelCNN++ models.
MaCow improves flow-based models for image density estimation.
problem Flow-based models struggle with density estimation compared to autoregressive models.
method Introduced masked convolutional generative flow (MaCow) using masked convolution.
result Significant improvements in density estimation on image benchmarks.
LMConv improves autoregressive models for image generation and completion.
problem Limited generation order in autoregressive models restricts their applicability.
method Introduces LMConv, a modified 2D convolution that allows arbitrary masks to be applied to weights.
result LMConv achieves improved performance on image density estimation and coherent completions.
New model predicts univariate and multivariate time series with improved accuracy.
problem Complex patterns in univariate and multivariate time series forecasting.
method Uses autoregressive convolutional recurrent neural network with feature extraction and recurrent encoder.
result Outperforms existing architectures in multivariate time series datasets.
PixelSNAIL improves autoregressive models for high-dimensional data like images.
problem Improving autoregressive models for high-dimensional data like images.
method Combines causal convolutions with self-attention to model long-range dependencies.
result Achieves state-of-the-art log-likelihood results on CIFAR-10 and ImageNet.
ARMA nets expand receptive fields for dense prediction tasks.
problem Global information in dense prediction problems is challenging for traditional convolutional layers.
method ARMA layers with adjustable autoregressive coefficients replace traditional convolutions.
result ARMA networks improve dense prediction tasks including video prediction and semantic segmentation.
Deep neural network solves complex groundwater contaminant source identification.
problem Identifying groundwater contaminant sources in highly heterogeneous media.
method Deep autoregressive neural network surrogate for forward model, ILUES for inversion.
result Deep autoregressive neural network provides accurate approximation for high-dimensional model.
SHVC improves image compression with fewer parameters.
problem Challenges in VAE compression, especially with bits-back coding.
method Introduces autoregressive sub-pixel convolution and autoregressive initial bits.
result Achieves state-of-the-art compression performance with fewer model parameters.
Improved generative models using flexible convolutions.
problem Generating high-quality images efficiently.
method Generalized 1 x 1 convolutions to d x d convolutions, chaining autoregressive and periodic convolutions.
result Flexible d x d convolutions significantly improve generative flow models' performance.
Flow++ improves flow-based models by dequantizing with variational methods and using expressive architectures.
problem Flow-based models have poor density estimation compared to autoregressive models.
method Variational dequantization, expressive affine flows, and improved architecture design.
result Flow++ is now the state-of-the-art non-autoregressive model for unconditional density estimation.
Method uses autoregressive models to interpret neural network representations.
problem Understanding and quantifying information preserved in neural network layers.
method Trains autoregressive models to invert model representations and estimate mutual information.
result Mutual information between inputs and network layers decreases over training.
New deep learning method solves TSP faster and more efficiently.
problem Approximately solving the Travelling Salesman Problem on 2D Euclidean graphs.
method Uses Graph Convolutional Networks for efficient TSP graph representations and non-autoregressive beam search.
result Significantly reduces optimality gap for large problem instances.
Convolutional Neural Processes improve data efficiency in neural processes.
problem Improving data efficiency in neural processes for small datasets.
method Convolutional Neural Processes (ConvNPs) improve data efficiency by leveraging translation equivariance and convolutional neural networks.
result ConvNPs enhance the performance of neural processes in small-data problems.
Parallel decoding speeds up deep autoregressive models.
problem Sequential generation limits deep autoregressive models' speed.
method Blockwise parallel decoding scheme for multiple time steps.
result Up to 7x speedup in wall-clock time with no loss in quality.
Time series analysis is a key component of machine learning, with applications in various fields.
problem Time series analysis in machine learning
method Basic concepts, classical statistical models, modern machine learning approaches
result Machine learning techniques for time series analysis
Convolutional network converts speaker voices without text.
problem Speaker conversion without text-based methods.
method Fully convolutional wav-to-wav network with ASR pre-training.
result Successfully converts TTS robot's voice to narrated audiobook voices.
Convolutional neural networks improve time series forecasting efficiency.
problem Efficiently forecasting multivariate time series with historical data.
method Adapted WaveNet architecture with dilated convolutions and parallel filters.
result Convolutional network outperforms autoregressive and LSTM models.
New model captures long-range patterns in sequences efficiently.
problem Efficiently capturing long-range patterns in sequential data.
method Inspired by wavelet multiresolution analysis, introduces MultiresLayer with multiresolution convolution.
result State-of-the-art performance on sequence classification and autoregressive density estimation tasks.
Modeling spatial extremes with non-Gaussian fields using SAR models and CNNs.
problem Challenges in modeling spatial data with heavy-tailed distributions and missing cells.
method Spatial autoregressive models with Generalized Extreme Value innovations, combined with CNN for fast parameter estimation.
result Effective modeling of spatial extremes in non-Gaussian fields, demonstrated on precipitation data.
NVAE improves VAE performance on large image datasets.
problem Improving variational autoencoder performance for large image datasets.
method Deep hierarchical VAE with depth-wise separable convolutions and batch normalization, residual parameterization of Normal distributions, and spectral regularization.
result NVAE achieves state-of-the-art results on MNIST, CIFAR-10, CelebA 64, and CelebA HQ datasets.
Improves autoregressive models for better density estimation.
problem Limited flexibility of autoregressive models.
method Stacked autoregressive models with random number modeling.
result Achieves state-of-the-art performance in density estimation.
Method converts sparse systems to dense ones for statistical mechanics problems.
problem Statistical mechanics on sparse graphs
method Extracts a Feedback Vertex Set, learns variational distribution, estimates free energy.
result More accurate and faster than existing methods for sparse systems.
Efficiently improves non-autoregressive sequence models for better translation performance.
problem Heavy inference latency and inconsistent output sentences in non-autoregressive models.
method Incorporates a structured inference module with an efficient CRF approximation and dynamic transition technique.
result Significantly better translation performance (BLEU score 26.80) compared to previous non-autoregressive models.
Paper combines latent state space with CRF for improved autoregressive text generation.
problem Autoregressive models expose hidden state trajectory to biases.
method Combines latent state space model with CRF observation model.
result Improved performance on unconditional sentence generation compared to RNN and GAN baselines.
This work proposes an efficient autoregressive model for text generation.
problem The challenge of generating high-quality text with autoregressive models.
method Introduces a cascaded decoding approach using Markov transformers to achieve sub-linear parallel time generation.
result Shows competitive accuracy/speed tradeoff compared to existing methods on five machine translation datasets.
Alternative sampling method for autoregressive models using Langevin dynamics.
problem Efficiently sampling from autoregressive models.
method Initialize sequences with white noise and follow Langevin dynamics on global log-likelihood.
result Parallelizes and generalizes sampling process for autoregressive models.
Bayesian method for multivariate autoregressive models with exogenous inputs.
problem Estimating uncertainties in autoregressive models with exogenous inputs.
method Recursive Bayesian estimation via message passing in a factor graph.
result Produces full posterior distributions for autoregressive coefficients and noise precision.
GAMs combine autoregressive and log-linear components for data-efficient sequence learning.
problem Poor performance of standard autoregressive models under small-data conditions.
method Introduce Global Autoregressive Models (GAMs) combining autoregressive and log-linear components, trained in two steps.
result GAMs show a strong perplexity reduction over standard models in language modelling.
Optimal attack against autoregressive models by manipulating environment states.
problem Manipulating autoregressive forecasts to track a target trajectory.
method Linear Quadratic Regulator (LQR) for linear models, Model Predictive Control (MPC) for nonlinear models.
result Optimal attack formulations for both white-box and black-box settings.
Paper proposes AXE loss for non-autoregressive machine translation, improving performance.
problem Challenges in training non-autoregressive models due to lack of autoregressive factors and cross entropy loss penalties.
method Proposes aligned cross entropy (AXE) loss function using a differentiable dynamic program for better word order alignment.
result AXE-based training improves performance on major WMT benchmarks and sets a new state of the art for non-autoregressive models.
Non-autoregressive model speeds up sequence generation tasks.
problem Efficiency in sequence generation tasks.
method Iterative refinement based on latent variable models and denoising autoencoders.
result Significant speedup in decoding with comparable quality.
SMART training improves mask-predict translations.
problem Closing the performance gap between semi-autoregressive and autoregressive models.
method SMART training method for conditional masked language models.
result SMART-trained models produce higher-quality translations.
Parallelizes autoregressive generation using VSSM.
problem Autoregressive models' inability to parallelize generation.
method Variational SSM (VSSM) with parallelizable sampling and decoding.
result Parallel generation possible with VSSM.
DLF combines autoregressive and flow-based methods for efficient and high-performance image generation.
problem Limited density estimation performance and parallelizability of flow-based and autoregressive models.
method Dynamic Linear Flow (DLF) with partially autoregressive structure.
result DLF achieves state-of-the-art performance on ImageNet 32x32 and 64x64 images.
NAF combines neural networks with autoregressive models for better density estimation.
problem Improving density estimation and speech synthesis speed.
method Generalizes autoregressive models using neural networks for invertible transformations.
result NAF is a universal approximator for continuous probability distributions and outperforms IAF.
Hierarchical autoregressive models improve image quality by learning abstract representations.
problem Local structure bias in autoregressive models leads to lack of large-scale coherence in generated images.
method Propose two methods to learn discrete representations of images that abstract away local detail, and train autoregressive priors on these representations.
result Hierarchical autoregressive models produce high-fidelity reconstructions and realistic images with large-scale coherence.
Linear attention in Transformers can be interpreted as dynamic VAR models.
problem Misalignment between Transformers and autoregressive forecasting objectives.
method Interpreting linear attention as VAR, rearranging MLP, attention, and flow.
result SAMoVAR improves performance, interpretability, and efficiency.
Efficiently combines autoregressive and set-based models for joint distributions.
problem Joint distributions over multiple predictions from set-based models.
method Causal autoregressive buffer that caches context and captures dependencies.
result Up to 20x faster joint sampling and density evaluation, up to 7x lower memory usage.
Paper proposes a self-supervised method to denoise autoregressive signals with heavy-tailed noise.
problem Denoising autoregressive signals corrupted by heavy-tailed noise.
method Self-supervised learning approach without requiring full noise distribution knowledge.
result Strong denoising performance compared to baseline methods, especially for impulsive noise.
HARNet improves volatility forecasting using deep neural networks.
problem Lack of deep learning in volatility forecasting.
method HARNet based on dilated convolutional layers, explicitly initialized to match HAR model.
result HARNet significantly improves forecasting accuracy compared to HAR models.
Autoregressive models struggle with hard-to-compute distributions, alternatives like energy-based and latent-variable models solve this.
problem Autoregressive models struggle with distributions whose next-symbol probability is hard to compute.
method Alternatives include energy-based models and latent-variable autoregressive models.
result Alternatives to autoregressive models can escape limitations of hard-to-compute distributions.
NAOMI improves imputation accuracy for long-range sequences.
problem Missing value imputation in spatiotemporal data.
method Non-autoregressive deep generative model exploiting multiresolution structure.
result Significant improvement in imputation accuracy (60% reduction in average prediction error).
A new training method improves autoregressive data completion efficiency.
problem Efficiently completing missing data in autoregressive models.
method Proposed an alternative training procedure (OA++) that reduces overfitting and leverages prior knowledge.
result OA++ achieves better performance with fewer computations and less overfitting.
Study improves dividend discount model using VAR process.
problem Improving dividend discount models for better predictions.
method Introduced a Gordon growth model based on Vector Autoregressive Process (VAR).
result Two Propositions related to the new model.
Bayesian method estimates Kronecker graphical models from autoregressive processes.
problem Estimating Kronecker graphical models from autoregressive Gaussian processes.
method Bayesian approach to estimate Kronecker graphical models.
result Effectiveness demonstrated through numerical experiments and real-world data application.
Discrete flows extend normalizing flows to discrete data, improving various applications.
problem Applying normalizing flows to discrete data distributions.
method Developed discrete autoregressive and bipartite flows, showing their effectiveness on various discrete data tasks.
result Discrete autoregressive flows outperform autoregressive baselines on synthetic discrete distributions and Potts models.
The paper proposes autoregressive models for better offline RL.
problem Offline RL policy evaluation and optimization challenges.
method Autoregressive dynamics models for sequential state and reward prediction.
result Autoregressive models outperform standard methods in log-likelihood and RL tasks.
A new clustering method for vector time series using autoregressive dynamics.
problem Clustering of vector time series based on their dynamics is challenging.
method System identification approach using mixture autoregressive models.
result Developed a computationally manageable algorithm k-LMVAR for clustering vector time series.