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General Relativistic NonNeutral White Dwarf Stars ; We generalize the recent Newtonian twocomponent charged fluid models for white dwarf stars of Krivoruchenko, Nadyozhin and Yudin and of Hund and Kiessling to the context of general relativity. We compare the equations and numerical solutions of these models. We exten... |
BV quantization of a generic degenerate quadratic lagrangian ; Generalizing the YangMills gauge theory, we provide the BV quantization of a field model with a generic almostregular quadratic Lagrangian by use of the fact that the configuration space of such a field model is split into the gaugeinvariant and gaugefixin... |
On Friedmann's universes ; There is a perfect concordance between Friedmann's cosmological models and the correspondent purely gravitational interactions and negligible pressure Newtonian models. This renders quite intuitive the fact that in general relativity no motion of bodies generates gravitational waves. |
Blow up of solutions to generalized KellerSegel model ; The existence and nonexistence of global in time solutions is studied for a class of equations generalizing the chemotaxis model of Keller and Segel. These equations involve L'evy diffusion operators and general potential type nonlinear terms. |
An Abased cofibrantly generated model category ; We develop a cofibrantly generated model category structure in the category of topological spaces in which weak equivalences are Aweak equivalences and such that the generalized CWAcomplexes are cofibrant objects. With this structure the exponential law turns out to be ... |
Recurrence and nonergodicity in generalized windtree models ; In this paper, we consider generalized windtree models and Zdcovers over compact translation surfaces. Under suitable hypothesis, we prove recurrence of the linear flow in a generic direction and nonergodicity of Lebesgue measure. |
XGGM Graph Generative Modeling for OutofDistribution Generalization in Visual Question Answering ; Encouraging progress has been made towards Visual Question Answering VQA in recent years, but it is still challenging to enable VQA models to adaptively generalize to outofdistribution OOD samples. Intuitively, recomposi... |
Generative Audio Synthesis with a Parametric Model ; Use a parametric representation of audio to train a generative model in the interest of obtaining more flexible control over the generated sound. |
Generalized permutations related to the degenerate Eulerian numbers ; In this work we propose a combinatorial model that generalizes the standard definition of permutation. Our model generalizes the degenerate Eulerian polynomials and numbers of Carlitz from 1979 and provides missing combinatorial proofs for some rela... |
Parallel Synthesis for Autoregressive Speech Generation ; Autoregressive models have achieved outstanding performance in neural speech synthesis tasks. Though they can generate highly natural human speech, the iterative generation inevitably makes the synthesis time proportional to the utterance's length, leading to l... |
MEGA Multilingual Evaluation of Generative AI ; Generative AI models have impressive performance on many Natural Language Processing tasks such as language understanding, reasoning and language generation. One of the most important questions that is being asked by the AI community today is about the capabilities and l... |
On Attribution of Deepfakes ; Progress in generative modelling, especially generative adversarial networks, have made it possible to efficiently synthesize and alter media at scale. Malicious individuals now rely on these machinegenerated media, or deepfakes, to manipulate social discourse. In order to ensure media au... |
TextFree ProsodyAware Generative Spoken Language Modeling ; Speech pretraining has primarily demonstrated efficacy on classification tasks, while its capability of generating novel speech, similar to how GPT2 can generate coherent paragraphs, has barely been explored. Generative Spoken Language Modeling GSLM citeLakho... |
Procedural Generalization by Planning with SelfSupervised World Models ; One of the key promises of modelbased reinforcement learning is the ability to generalize using an internal model of the world to make predictions in novel environments and tasks. However, the generalization ability of modelbased agents is not we... |
Operationalizing Specifications, In Addition to Test Sets for Evaluating Constrained Generative Models ; In this work, we present some recommendations on the evaluation of stateoftheart generative models for constrained generation tasks. The progress on generative models has been rapid in recent years. These largescal... |
Observational constraints on generalized Chaplygin gas model ; The generalized Chaplygin gas model with parameter space alpha1 is studied in this paper. Some reasonable physical constraints are added to justify the use of the larger parameter space. The Type Ia supernova data and age data of some clusters are then use... |
Generation of new classes of integrable quantum and statistical models ; A scheme based on a unifying qdeformed algebra and associated with a generalized Lax operator is proposed for generating integrable quantum and statistical models. As important applications we derive known as well as novel quantum models and obta... |
Grand Unification with Three Generations in Free Fermionic String Models ; We examine the problem of constructing three generation free fermionic string models with grand unified gauge groups. We attempt the construction of Gtimes G models, where G is a grand unified group realized at level 1. This structure allows th... |
A Generalized Higgs Model ; The Higgs model is generalized so that in addition to the radial Higgs field there are fields which correspond to the themasy and entropy. The model is further generalized to include state and sign parameters. A reduction to the standard Higgs model is given and how to break symmetry using ... |
Multivariate Generalized Gaussian Process Models ; We propose a family of multivariate Gaussian process models for correlated outputs, based on assuming that the likelihood function takes the generic form of the multivariate exponential family distribution EFD. We denote this model as a multivariate generalized Gaussi... |
Mixmaster model is associated to Borcherds algebra ; The problem of integrability of the mixmaster model as a dynamical system with finite degrees of freedom is investigated. The model belongs to the class of pseudoEuclidean generalized Toda chains. It is presented as a quasihomogeneous system after transformations of... |
On the decomposition of Generalized Additive Independence models ; The GAI Generalized Additive Independence model proposed by Fishburn is a generalization of the additive utility model, which need not satisfy mutual preferential independence. Its great generality makes however its application and study difficult. We ... |
An Architecture for Deep, Hierarchical Generative Models ; We present an architecture which lets us train deep, directed generative models with many layers of latent variables. We include deterministic paths between all latent variables and the generated output, and provide a richer set of connections between computat... |
Learning to generate onesentence biographies from Wikidata ; We investigate the generation of onesentence Wikipedia biographies from facts derived from Wikidata slotvalue pairs. We train a recurrent neural network sequencetosequence model with attention to select facts and generate textual summaries. Our model incorpo... |
A general solution to the preferential selection model ; We provide a general analytic solution to Herbert Simon's 1955 model for timeevolving novelty functions. This has farreaching consequences Simon's is a precursor model for Barabasi's 1999 preferential attachment model for growing social networks, and our general... |
Conditional Constrained Graph Variational Autoencoders for Molecule Design ; In recent years, deep generative models for graphs have been used to generate new molecules. These models have produced good results, leading to several proposals in the literature. However, these models may have troubles learning some of the... |
Searching for Search Errors in Neural Morphological Inflection ; Neural sequencetosequence models are currently the predominant choice for language generation tasks. Yet, on wordlevel tasks, exact inference of these models reveals the empty string is often the global optimum. Prior works have speculated this phenomeno... |
Topic Sensitive Neural Headline Generation ; Neural models have recently been used in text summarization including headline generation. The model can be trained using a set of documentheadline pairs. However, the model does not explicitly consider topical similarities and differences of documents. We suggest to catego... |
Latent space generative model for bipartite networks ; Generative network models are extremely useful for understanding the mechanisms that operate in network formation and are widely used across several areas of knowledge. However, when it comes to bipartite networks a class of network frequently encountered in soci... |
Dualtrack Music Generation using Deep Learning ; Music generation is always interesting in a sense that there is no formalized recipe. In this work, we propose a novel dualtrack architecture for generating classical piano music, which is able to model the interdependency of lefthand and righthand piano music. Particul... |
Modern French Poetry Generation with RoBERTa and GPT2 ; We present a novel neural model for modern poetry generation in French. The model consists of two pretrained neural models that are finetuned for the poem generation task. The encoder of the model is a RoBERTa based one while the decoder is based on GPT2. This wa... |
On type III generalized half logistic distribution ; It is well known that generalized models is attracting the attention of researchers in recent times because of their flexibilities. Particularly, the logistic model has been generalized and applied by many authors while the half logistic distribution has not recieve... |
Chinese Poetry Generation with Flexible Styles ; Research has shown that sequencetosequence neural models, particularly those with the attention mechanism, can successfully generate classical Chinese poems. However, neural models are not capable of generating poems that match specific styles, such as the impulsive sty... |
Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks ; We investigate under and overfitting in Generative Adversarial Networks GANs, using discriminators unseen by the generator to measure generalization. We find that the model capacity of the discriminator has a significant effect on the... |
Judge a Sentence by Its Content to Generate Grammatical Errors ; Data sparsity is a wellknown problem for grammatical error correction GEC. Generating synthetic training data is one widely proposed solution to this problem, and has allowed models to achieve stateoftheart SOTA performance in recent years. However, thes... |
Automatic Locally Robust Estimation with Generated Regressors ; Many economic and causal parameters of interest depend on generated regressors, including structural parameters in models with endogenous variables estimated by control functions and in models with sample selection. Inference with generated regressors is ... |
A General Equivalence Theorem for Crossover Designs under Generalized Linear Models ; With the help of Generalized Estimating Equations, we identify locally Doptimal crossover designs for generalized linear models. We adopt the variance of parameters of interest as the objective function, which is minimized using cons... |
Improve Language Modelling for Code Completion through Statement Level Language Model based on Statement Embedding Generated by BiLSTM ; Language models such as RNN, LSTM or other variants have been widely used as generative models in natural language processing. In last few years, taking source code as natural langua... |
Framework for Converting Mechanistic Network Models to Probabilistic Models ; There are two prominent paradigms to the modeling of networks in the first, referred to as the mechanistic approach, one specifies a set of domainspecific mechanistic rules that are used to grow or evolve the network over time; in the second... |
Combinatorial and accessible weak model categories ; In a previous work, we have introduced a weakening of Quillen model categories called weak model categories. They still allow all the usual constructions of model category theory, but are easier to construct and are in some sense better behaved. In this paper we con... |
MultiObjective De Novo Drug Design with Conditional Graph Generative Model ; Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design. However, previous research has focused mainly on generating SMILES strings instead of molecular graphs. Although current graph gen... |
Transferable Universal Adversarial Perturbations Using Generative Models ; Deep neural networks tend to be vulnerable to adversarial perturbations, which by adding to a natural image can fool a respective model with high confidence. Recently, the existence of imageagnostic perturbations, also known as universal advers... |
Prospects for Declarative Mathematical Modeling of Complex Biological Systems ; Declarative modeling uses symbolic expressions to represent models. With such expressions one can formalize highlevel mathematical computations on models that would be difficult or impossible to perform directly on a lowerlevel simulation ... |
Canonical Formalism for a 2nDimensional Model with Topological Mass Generation ; The fourdimensional model with topological mass generation that was found by Dvali, Jackiw and Pi has recently been generalized to any even number of dimensions 2ndimensions in a nontrivial manner in which a Stueckelbergtype mass term is ... |
Generalized PolandScheraga denaturation model and twodimensional renewal processes ; The PolandScheraga model describes the denaturation transition of two complementary in particular, equally long strands of DNA, and it has enjoyed a remarkable success both for quantitative modeling purposes and at a more theoretica... |
GEN Model An Alternative Approach to Deep Neural Network Models ; In this paper, we introduce an alternative approach, namely GEN Genetic Evolution Network Model, to the deep learning models. Instead of building one single deep model, GEN adopts a geneticevolutionary learning strategy to build a group of unit models g... |
A New Generative Statistical Model for Graphs The Latent Order Logistic LOLOG Model ; Full probability models are critical for the statistical modeling of complex networks, and yet there are few general, flexible and widely applicable generative methods. We propose a new family of probability models motivated by the i... |
NonHermitian generalizations of extended SuSchriefferHeeger models ; NonHermitian generalizations of the SuSchriefferHeeger SSH models with higher periods of the hopping coefficients, called the SSH3 and SSH4 models, are analyzed. The conventional construction of the winding number fails for the Hermitian SSH3 model, ... |
A Survey of Diffusion Based Image Generation Models Issues and Their Solutions ; Recently, there has been significant progress in the development of large models. Following the success of ChatGPT, numerous language models have been introduced, demonstrating remarkable performance. Similar advancements have also been o... |
Using reference models in variable selection ; Variable selection, or more generally, model reduction is an important aspect of the statistical workflow aiming to provide insights from data. In this paper, we discuss and demonstrate the benefits of using a reference model in variable selection. A reference model acts ... |
Vector Learning for Cross Domain Representations ; Recently, generative adversarial networks have gained a lot of popularity for image generation tasks. However, such models are associated with complex learning mechanisms and demand very large relevant datasets. This work borrows concepts from image and video captioni... |
The Effects of Invertibility on the Representational Complexity of Encoders in Variational Autoencoders ; Training and using modern neuralnetwork based latentvariable generative models like Variational Autoencoders often require simultaneously training a generative direction along with an inferentialencoding direction... |
Paraphrase Generation with Latent Bag of Words ; Paraphrase generation is a longstanding important problem in natural language processing. In addition, recent progress in deep generative models has shown promising results on discrete latent variables for text generation. Inspired by variational autoencoders with d... |
MEGATRONCNTRL Controllable Story Generation with External Knowledge Using LargeScale Language Models ; Existing pretrained large language models have shown unparalleled generative capabilities. However, they are not controllable. In this paper, we propose MEGATRONCNTRL, a novel framework that uses largescale language ... |
ProphetNetAds A Looking Ahead Strategy for Generative Retrieval Models in Sponsored Search Engine ; In a sponsored search engine, generative retrieval models are recently proposed to mine relevant advertisement keywords for users' input queries. Generative retrieval models generate outputs token by token on a path of ... |
Generative Capacity of Probabilistic Protein Sequence Models ; Potts models and variational autoencoders VAEs have recently gained popularity as generative protein sequence models GPSMs to explore fitness landscapes and predict the effect of mutations. Despite encouraging results, quantitative characterization and com... |
Superresolution of spin configurations based on flowbased generative models ; We present a superresolution method for spin systems using a flowbased generative model that is a deep generative model with reversible neural network architecture. Starting from spin configurations on a twodimensional square lattice, our mo... |
Controllable Text Generation with NeurallyDecomposed Oracle ; We propose a general and efficient framework to control autoregressive generation models with NeurAllyDecomposed Oracle NADO. Given a pretrained base language model and a sequencelevel boolean oracle function, we propose to decompose the oracle function int... |
Towards Universal Fake Image Detectors that Generalize Across Generative Models ; With generative models proliferating at a rapid rate, there is a growing need for general purpose fake image detectors. In this work, we first show that the existing paradigm, which consists of training a deep network for realvsfake clas... |
Conditional Generation from Unconditional Diffusion Models using Denoiser Representations ; Denoising diffusion models have gained popularity as a generative modeling technique for producing highquality and diverse images. Applying these models to downstream tasks requires conditioning, which can take the form of text... |
SequenceMatch Imitation Learning for Autoregressive Sequence Modelling with Backtracking ; In many domains, autoregressive models can attain high likelihood on the task of predicting the next observation. However, this maximumlikelihood MLE objective does not necessarily match a downstream usecase of autoregressively ... |
Using Motif Transitions for Temporal Graph Generation ; Graph generative models are highly important for sharing surrogate data and benchmarking purposes. Realworld complex systems often exhibit dynamic nature, where the interactions among nodes change over time in the form of a temporal network. Most temporal network... |
An Accurate Graph Generative Model with Tunable Features ; A graph is a very common and powerful data structure used for modeling communication and social networks. Models that generate graphs with arbitrary features are important basic technologies in repeated simulations of networks and prediction of topology change... |
Unbiased Face Synthesis With Diffusion Models Are We There Yet ; Texttoimage diffusion models have achieved widespread popularity due to their unprecedented image generation capability. In particular, their ability to synthesize and modify human faces has spurred research into using generated face images in both train... |
LiDAR Data Synthesis with Denoising Diffusion Probabilistic Models ; Generative modeling of 3D LiDAR data is an emerging task with promising applications for autonomous mobile robots, such as scalable simulation, scene manipulation, and sparsetodense completion of LiDAR point clouds. Existing approaches have shown the... |
Sensitivity Analysis of the MCRF Model to Different Transiogram Joint Modeling Methods for Simulating Categorical Spatial Variables ; Markov chain geostatistics is a methodology for simulating categorical fields. Its fundamental model for conditional simulation is the Markov chain random field MCRF model, and its basi... |
Leveraging Evolution Dynamics to Generate Benchmark Complex Networks with Community Structures ; The past decade has seen tremendous growth in the field of Complex Social Networks. Several network generation models have been extensively studied to develop an understanding of how real world networks evolve over time. T... |
Learning Inverse Mapping by Autoencoder based Generative Adversarial Nets ; The inverse mapping of GANs'Generative Adversarial Nets generator has a great potential value.Hence, some works have been developed to construct the inverse function of generator by directly learning or adversarial learning.While the results a... |
MolGAN An implicit generative model for small molecular graphs ; Deep generative models for graphstructured data offer a new angle on the problem of chemical synthesis by optimizing differentiable models that directly generate molecular graphs, it is possible to sidestep expensive search procedures in the discrete and... |
Deep Structured Generative Models ; Deep generative models have shown promising results in generating realistic images, but it is still nontrivial to generate images with complicated structures. The main reason is that most of the current generative models fail to explore the structures in the images including spatial... |
Unsupervised Primitive Discovery for Improved 3D Generative Modeling ; 3D shape generation is a challenging problem due to the highdimensional output space and complex part configurations of realworld objects. As a result, existing algorithms experience difficulties in accurate generative modeling of 3D shapes. Here, ... |
Parameterization of Forced Isotropic Turbulent Flow using Autoencoders and Generative Adversarial Networks ; Autoencoders and generative neural network models have recently gained popularity in fluid mechanics due to their spontaneity and low processing time instead of high fidelity CFD simulations. Auto encoders are ... |
Generative Models from the perspective of Continual Learning ; Which generative model is the most suitable for Continual Learning This paper aims at evaluating and comparing generative models on disjoint sequential image generation tasks. We investigate how several models learn and forget, considering various strategi... |
Distributional Discrepancy A Metric for Unconditional Text Generation ; The purpose of unconditional text generation is to train a model with real sentences, then generate novel sentences of the same quality and diversity as the training data. However, when different metrics are used for comparing the methods of uncon... |
THINK A Novel Conversation Model for Generating Grammatically Correct and Coherent Responses ; Many existing conversation models that are based on the encoderdecoder framework have focused on ways to make the encoder more complicated to enrich the context vectors so as to increase the diversity and informativeness of ... |
MOCHA A MultiTask Training Approach for Coherent Text Generation from Cognitive Perspective ; Teaching neural models to generate narrative coherent texts is a critical problem. Recent pretrained language models have achieved promising results, but there is still a gap between human written texts and machinegenerated o... |
LayoutDM Transformerbased Diffusion Model for Layout Generation ; Automatic layout generation that can synthesize highquality layouts is an important tool for graphic design in many applications. Though existing methods based on generative models such as Generative Adversarial Networks GANs and Variational AutoEncoder... |
Large Language Models are Effective TabletoText Generators, Evaluators, and Feedback Providers ; Large language models LLMs have shown remarkable ability on controllable text generation. However, the potential of LLMs in generating text from structured tables remains largely underexplored. In this paper, we study the ... |
Benchmarking Large Language Model Capabilities for Conditional Generation ; Pretrained large language models PLMs underlie most new developments in natural language processing. They have shifted the field from applicationspecific model pipelines to a single model that is adapted to a wide range of tasks. Autoregressiv... |
Generative Forests ; Tabular data represents one of the most prevalent form of data. When it comes to data generation, many approaches would learn a density for the data generation process, but would not necessarily end up with a sampler, even less so being exact with respect to the underlying density. A second issue ... |
Theory of Superselection Sectors for Generalized Ising models ; We apply the theory of superselection sectors in the same way as done by G.Mack and V.Schomerus for the Ising model to generalizations of this model described by J.Frohlich and T.Kerler. |
Path Integral Solubility of a General TwoDimensional Model ; The solubility of a general two dimensional model, which reduces to various models in different limits, is studied within the path integral formalism. Various subtleties and interesting features are pointed out. |
Three generation DistlerKachru models ; DistlerKachru models which yield three generations of chiral fermions with gauge group SO10 are found. These models have mirror partners. |
The Bag Model of Nuclei ; The basic assumptions and the general results of our bag model for nuclei are presented in detail. Nuclei are considered in a unified integration of the mean field theory and the MIT bag model |
On the evolution in the configuration model ; We give precise estimates on the number of activeinactive halfedges in the configuration model used to generate random regular graphs. This is obtained by analyzing a more general urn model with negative eigenvalues. |
Learning Inference Models for Computer Vision ; Computer vision can be understood as the ability to perform inference on image data. Breakthroughs in computer vision technology are often marked by advances in inference techniques. This thesis proposes novel inference schemes and demonstrates applications in computer v... |
Infinite forcing and the generic multiverse ; In this article we present a technique for selecting models of set theory that are complete in a modeltheoretic sense. Specifically, we will apply Robinson infinite forcing to the collections of models of ZFC obtained by Cohen forcing. This technique will be used to sugges... |
Uniform bounds for ruin probability in Multidimensional Risk Model ; In this paper we consider some generalizations of the classical ddimensional Brownian risk model. This contribution derives some nonasymptotic bounds for simultaneous ruin probabilities of interest. In addition, we obtain nonasymptotic bounds also fo... |
Learning Robust Representations Of Generative Models Using SetBased Artificial Fingerprints ; With recent progress in deep generative models, the problem of identifying synthetic data and comparing their underlying generative processes has become an imperative task for various reasons, including fighting visual misinf... |
Exploring Generative Neural Temporal Point Process ; Temporal point process TPP is commonly used to model the asynchronous event sequence featuring occurrence timestamps and revealed by probabilistic models conditioned on historical impacts. While lots of previous works have focused on goodnessoffit' of TPP models b... |
Flops and minimal models for generalized pairs ; We show that given any two minimal models of a generalized lc pair, there exist small birational models which are connected by a sequence of symmetric flops. We also present some applications. |
Multicolored dimer models in onedimension lattice paths and generalized RogersRamanujan identities ; We define and study multicolored dimer models on a segment and on a circle. The multivariate generating functions for the dimer models satisfy the recurrence relations similar to the one for Fibonacci numbers. We give ... |
From Text to Source Results in Detecting Large Language ModelGenerated Content ; The widespread use of Large Language Models LLMs, celebrated for their ability to generate humanlike text, has raised concerns about misinformation and ethical implications. Addressing these concerns necessitates the development of robust... |
Generalized exponential function and discrete growth models ; Here we show that a particular oneparameter generalization of the exponential function is suitable to unify most of the popular onespecies discrete population dynamics models into a simple formula. A physical interpretation is given to this new introduced p... |
A Logicbased Approach to Generatively Defined Discriminative Modeling ; Conditional random fields CRFs are usually specified by graphical models but in this paper we propose to use probabilistic logic programs and specify them generatively. Our intension is first to provide a unified approach to CRFs for complex model... |
Scaffoldbased molecular design using graph generative model ; Searching new molecules in areas like drug discovery often starts from the core structures of candidate molecules to optimize the properties of interest. The way as such has called for a strategy of designing molecules retaining a particular scaffold as a s... |
Exact rankreduction of network models ; With the advent of the big data era, generative models of complex networks are becoming elusive from direct computational simulation. We present an exact, linearalgebraic reduction scheme of generative models of networks. By exploiting the bilinear structure of the matrix repres... |
Conditioning Deep Generative Raw Audio Models for Structured Automatic Music ; Existing automatic music generation approaches that feature deep learning can be broadly classified into two types raw audio models and symbolic models. Symbolic models, which train and generate at the note level, are currently the more pre... |
CATGen Improving Robustness in NLP Models via Controlled Adversarial Text Generation ; NLP models are shown to suffer from robustness issues, i.e., a model's prediction can be easily changed under small perturbations to the input. In this work, we present a Controlled Adversarial Text Generation CATGen model that, giv... |
Generating Math Word Problems from Equations with Topic Controlling and Commonsense Enforcement ; Recent years have seen significant advancement in text generation tasks with the help of neural language models. However, there exists a challenging task generating math problem text based on mathematical equations, which... |
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