Mar 21, 2025 · A hybrid AI approach known as hybrid autoregressive transformer can generate realistic images with the same or better quality than state-of-the-art diffusion models, but that runs about nine …
In financial risk management, Value at Risk (VaR) is widely used to estimate potential portfolio losses. VaR's limitation is its inability to account for the magnitude of losses beyond a certain threshold. Expected Shortfall (ES) addresses this by pr...
We study a multivariate autoregressive stochastic volatility model for the first 3 principal components (level, slope, curvature) of 10 series of zero-coupon Treasury bond rates with maturities from 1 to 10 years. We fit this model using monthly data...
A framework for the evaluation of autoregressive code generation language models. (⭐ 1019)
A Non-Autoregressive Transformer based Text-to-Speech, supporting a family of SOTA transformers with supervised and unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultimate TTS (⭐ 328)
Autoregressive Language Models (LLMs) trained on Next-Token Prediction (NTP) often suffer from Topic Drift where the generation wanders away from the initial prompt due to a reliance on local associations rather than global planning. While scaling mo...
Introdução: “Como as democracias morrem” é uma publicação que explora o caminho que leva democracias a se tornarem autoritárias, analisando os mecanismos e comportamentos que …
Scholarship on generative pretraining (GPT) remains acutely Anglocentric, leaving serious gaps in our understanding of the whole class of autoregressive models. For example, we have little knowledge about the potential of these models and their socie...
Mar 21, 2025 · A hybrid AI approach known as hybrid autoregressive transformer can generate realistic images with the same or better quality than state-of-the-art diffusion models, but that runs about nine …
(in Catalan). Retrieved 28 February 2025. "Navarro Miralles, Luis J." datos.bne.es (in Spanish). Retrieved 28 February 2025. "Todos los libros del autor
La BAD a également consolidé sa place de première institution de financement du développement en Afrique, de centre du savoir et de la recherche ainsi que sa vocation à être la voix autorisée en â¦
El Real Madrid sometió con autoridad al Valencia (70-81) y certificó por la vía rápida (3-0) su trigésimo octavo título de liga en un partido en el que demostró todas las virtudes que le han ll...
Large language models exhibit systematic vulnerabilities to adversarial attacks despite extensive safety alignment. We provide a mechanistic analysis revealing that position-dependent gradient weakening during autoregressive training creates signal d...
Diffusion large language models (dLLMs) have emerged as a promising alternative to autoregressive models, offering flexible generation orders and strong performance on complex reasoning tasks. However, instruction-tuned dLLMs exhibit a critical vulne...
Mar 21, 2025 · A hybrid AI approach known as hybrid autoregressive transformer can generate realistic images with the same or better quality than state-of-the-art diffusion models, but that runs about nine …
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This resource paper addresses the challenge of evaluating Information Retrieval (IR) systems in the era of autoregressive Large Language Models (LLMs). Traditional methods relying on passage-level judgments are no longer effective due to the diversit...
How do language models learn to make predictions during pre-training? To study this, we extract learning curves from five autoregressive English language model pre-training runs, for 1M unseen tokens in context. We observe that the language models ge...
Morse foliated open books were introduced by the autors (along with abstract and embedded versions) as a tool for studying contact manifolds with boundary, and this article illustrates the advantages of the Morse perspective. We use this to extend th...
We consider impulse response inference in a locally misspecified vector autoregression (VAR) model. The conventional local projection (LP) confidence interval has correct coverage even when the misspecification is so large that it can be detected wit...
Blog Gadu-Gadu - blog polskiego komunikatora internetowego GG (dawniej Gadu-Gadu). Możesz tworzyć tego bloga razem z nami. Dołącz do grona autorów! Gadu-Gadu na każdy temat. …
Diffusion LLMs have emerged as a promising alternative to conventional autoregressive LLMs, offering significant potential for improved runtime efficiency. However, existing diffusion models lack the ability to provably enforce user-specified formal...
Neuro-symbolic AI bridges the gap between purely symbolic and neural approaches to learning. This often requires maximizing the likelihood of a symbolic constraint w.r.t the neural network's output distribution. Such output distributions are typicall...
This is an implementation of Time Series forecasting methods namely VAR(Vector Auto Regressive) model and various other RNN models like Non-Linear AutoRegressive with eXogenous inputs) (4 Layers), Elman RNN, Jordan RNN. (⭐ 6)
Obra en que se recogen informaciones correspondientes a muy diversos campos del saber y de las actividades humanas. 3. f. enciclopedismo (‖ doctrina de los autores de la Enciclopedia). Sin.: …
自从transformer 出现之后，NLP领域出现了很多基于transformer的改进，例如non-autoregressive transform…
Methods based on diffusion backbones have recently revolutionized novel view synthesis (NVS). However, those models require pretrained 2D diffusion checkpoints (e.g., Stable Diffusion) as the basis for geometrical priors. Since such checkpoints requi...
In this comment on "Solving Statistical Mechanics Using Variational Autoregressive Networks" by Wu et al., we propose a subtle yet powerful modification of their approach. We show that the inherent sampling error of their method can be corrected by u...
Frase do Dia: “Sua vida é a história que você escreve todos os dias.” – Autor desconhecido ? JRMUNEWS, Ano 3, Nº 874 ? ? Notícias do Brasil e do Mundo ? Sábado, 10 de Julho...
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Real-time, intelligent, and natural speech interaction is an essential part of the next-generation human-computer interaction. Recent advancements have showcased the potential of building intelligent spoken chatbots based on large language models (LL...
I present an approach for modeling areal spatial covariance by considering the stationary distribution of a spatio-temporal Markov random walk. In the areal data case, this stationary distribution corresponds to an intrinsic simultaneous autoregressi...
The pricing of financial derivatives, which requires massive calculations and close-to-real-time operations under many trading and arbitrage scenarios, were largely infeasible in the past. However, with the advancement of modern computing, the effici...
We consider a class of vector autoregressive models with banded coefficient matrices. The setting represents a type of sparse structure for high-dimensional time series, though the implied autocovariance matrices are not banded. The structure is also...
In this study interest centers on regional differences in the response of housing prices to monetary policy shocks in the US. We address this issue by analyzing monthly home price data for metropolitan regions using a factor-augmented vector autoregr...
We present the R-package mgm for the estimation of k-order Mixed Graphical Models (MGMs) and mixed Vector Autoregressive (mVAR) models in high-dimensional data. These are a useful extensions of graphical models for only one variable type, since data...
In this paper, we propose a text-to-speech (TTS)-driven data augmentation method for improving the quality of a non-autoregressive (AR) TTS system. Recently proposed non-AR models, such as FastSpeech 2, have successfully achieved fast speech synthesi...
We introduce Inworld TTS-1, a set of two Transformer-based autoregressive text-to-speech (TTS) models. Our largest model, TTS-1-Max, has 8.8B parameters and is designed for utmost quality and expressiveness in demanding applications. TTS-1 is our mos...
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