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Latent Diffusion Models for Structural Component Design ; Recent advances in generative modeling, namely Diffusion models, have revolutionized generative modeling, enabling highquality image generation tailored to user needs. This paper proposes a framework for the generative design of structural components. Specifica... |
EvoText Enhancing Natural Language Generation Models via SelfEscalation Learning for UptoDate Knowledge and Improved Performance ; In recent years, pretrained models have been widely used in various fields, including natural language understanding, computer vision, and natural language generation. However, the perform... |
Modelling and analysis of rank ordered data with ties via a generalized PlackettLuce model ; A simple generative model for rank ordered data with ties is presented. The model is based on ordering geometric latent variables and can be seen as the discrete counterpart of the PlackettLuce PL model, a popular, relatively ... |
Knowledge Distillation of Large Language Models ; Knowledge Distillation KD is a promising technique for reducing the high computational demand of large language models LLMs. However, previous KD methods are primarily applied to whitebox classification models or training small models to imitate blackbox model APIs lik... |
Unlocking Model Insights A Dataset for Automated Model Card Generation ; Language models LMs are no longer restricted to ML community, and instructiontuned LMs have led to a rise in autonomous AI agents. As the accessibility of LMs grows, it is imperative that an understanding of their capabilities, intended usage, an... |
Continual Learning of Generative Models with Limited Data From Wasserstein1 Barycenter to Adaptive Coalescence ; Learning generative models is challenging for a network edge node with limited data and computing power. Since tasks in similar environments share model similarity, it is plausible to leverage pretrained ge... |
ProphetNetX LargeScale Pretraining Models for English, Chinese, Multilingual, Dialog, and Code Generation ; Now, the pretraining technique is ubiquitous in natural language processing field. ProphetNet is a pretraining based natural language generation method which shows powerful performance on English text summarizat... |
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation ; The integration of Vector Quantised Variational AutoEncoder VQVAE with autoregressive models as generation part has yielded highquality results on image generation. However, the autoregressive models will strictly follow the pr... |
Generative NoisyLabel Learning by Implicit Dicriminative Approximation with Partial Label Prior ; The learning with noisy labels has been addressed with both discriminative and generative models. Although discriminative models have dominated the field due to their simpler modeling and more efficient computational trai... |
Linguistics Computation, Automatic Model Generation, and Intensions ; Techniques are presented for defining models of computational linguistics theories. The methods of generalized diagrams that were developed by this author for modeling artificial intelligence planning and reasoning are shown to be applicable to mode... |
Improved Classification Based on Deep Belief Networks ; For better classification generative models are used to initialize the model and model features before training a classifier. Typically it is needed to solve separate unsupervised and supervised learning problems. Generative restricted Boltzmann machines and deep... |
Analytical Formulation of the BlockConstrained Configuration Model ; We provide a novel family of generative blockmodels for random graphs that naturally incorporates degree distributions the blockconstrained configuration model. Blockconstrained configuration models build on the generalised hypergeometric ensemble of... |
Application of the Generalized Linear Models in Actuarial Framework ; This paper aims to review the methodology behind the generalized linear models which are used in analyzing the actuarial situations instead of the ordinary multiple linear regression. We introduce how to assess the adequacy of the model which includ... |
A general approach to the exact localized transition points of 1D mosaic disorder models ; In this paper, we present a general correspondence between the mosaic and nonmosaic models, which can be used to obtain the exact solution for the mosaic ones. This relation holds not only for the quasicrystal models, but also f... |
Generalized models as a universal approach to the analysis of nonlinear dynamical systems ; We present a universal approach to the investigation of the dynamics in generalized models. In these models the processes that are taken into account are not restricted to specific functional forms. Therefore a single generaliz... |
PeriodNet A nonautoregressive waveform generation model with a structure separating periodic and aperiodic components ; We propose PeriodNet, a nonautoregressive nonAR waveform generation model with a new model structure for modeling periodic and aperiodic components in speech waveforms. The nonAR waveform generation ... |
GPHD Using Genetic Programming to Generate Dynamical Systems Models for Health Care ; The huge wealth of data in the health domain can be exploited to create models that predict development of health states over time. Temporal learning algorithms are well suited to learn relationships between health states and make pr... |
Adversarial Robustness of FlowBased Generative Models ; Flowbased generative models leverage invertible generator functions to fit a distribution to the training data using maximum likelihood. Despite their use in several application domains, robustness of these models to adversarial attacks has hardly been explored. ... |
Knowledge Injection into Dialogue Generation via Language Models ; Dialogue generation has been successfully learned from scratch by neural networks, but tends to produce the same general response, e.g., what are you talking about, in many conversations. To reduce this homogeneity, external knowledge such as the speak... |
A General 3D SpaceTimeFrequency NonStationary THz Channel Model for 6G UltraMassive MIMO Wireless Communication Systems ; In this paper, a novel threedimensional 3D spacetimefrequency STF nonstationary geometrybased stochastic model GBSM is proposed for the sixth generation 6G terahertz THz wireless communication syst... |
Learning an Adaptive Meta ModelGenerator for Incrementally Updating Recommender Systems ; Recommender Systems RSs in realworld applications often deal with billions of user interactions daily. To capture the most recent trends effectively, it is common to update the model incrementally using only the newly arrived dat... |
Privacypreserving Generative Framework Against Membership Inference Attacks ; Artificial intelligence and machine learning have been integrated into all aspects of our lives and the privacy of personal data has attracted more and more attention. Since the generation of the model needs to extract the effective informat... |
Controlling the Focus of Pretrained Language Generation Models ; The finetuning of pretrained transformerbased language generation models are typically conducted in an endtoend manner, where the model learns to attend to relevant parts of the input by itself. However, there does not exist a mechanism to directly contr... |
On Provable Copyright Protection for Generative Models ; There is a growing concern that learned conditional generative models may output samples that are substantially similar to some copyrighted data C that was in their training set. We give a formal definition of textitnear accessfreeness NAF and prove bounds on th... |
TrueTeacher Learning Factual Consistency Evaluation with Large Language Models ; Factual consistency evaluation is often conducted using Natural Language Inference NLI models, yet these models exhibit limited success in evaluating summaries. Previous work improved such models with synthetic training data. However, the... |
RefDiff Zeroshot Referring Image Segmentation with Generative Models ; Zeroshot referring image segmentation is a challenging task because it aims to find an instance segmentation mask based on the given referring descriptions, without training on this type of paired data. Current zeroshot methods mainly focus on usin... |
Understanding the Properties of Generated Corpora ; Models for text generation have become focal for many research tasks and especially for the generation of sentence corpora. However, understanding the properties of an automatically generated text corpus remains challenging. We propose a set of tools that examine the... |
Generalized Metrical MultiTime Lagrange Model for General Relativity and Electromagnetism ; The paper construct a suitable generalized metrical multitime Lagrange geometrical model for both gravitational and electromagnetic fields, in a general setting. In this construction, the gravitational potentials are described ... |
An example of a noncofibrantly generated model category ; We show that the model category of diagrams of spaces generated by a proper class of orbits is not cofibrantly generated. In particular the category of maps between spaces may be given a noncofibrantly generated model structure. |
Generalized Whittaker functions for degenerate principal series of GL4,R ; We give a characterization of a generalized Whittaker model of a degenerate principal series representation of GLn,R as the kernel of some differential operators. By this characterization, we investigate some examples on GL4,R. We obtain the di... |
A novel repetition normalized adversarial reward for headline generation ; While reinforcement learning can effectively improve language generation models, it often suffers from generating incoherent and repetitive phrases citepaulus2017deep. In this paper, we propose a novel repetition normalized adversarial reward t... |
Are all cofibrantly generated model categories combinatorial ; G. Raptis has recently proved that, assuming Vopvenka's principle, every cofibrantly generated model category is Quillen equivalent to a combinatorial one. His result remains true for a slightly more general concept of a cofibrantly generated model categor... |
General Manipulability Theorem for a Matching Model ; In a manytomany matching model in which agents' preferences satisfy substitutability and the law of aggregate demand, we proof the General Manipulability Theorem. We result generalizes the presented in Sotomayor 1996 and 2012 for the manytoone model. In addition, w... |
Melodyconditioned lyrics generation via finetuning language model and its evaluation with ChatGPT ; We leverage characterlevel language models for syllablelevel lyrics generation from symbolic melody. By finetuning a characterlevel pretrained model, we integrate language knowledge into the beam search of a syllablelev... |
Generation High resolution 3D model from natural language by Generative Adversarial Network ; We present a method of generating high resolution 3D shapes from natural language descriptions. To achieve this goal, we propose two steps that generating low resolution shapes which roughly reflect texts and generating high ... |
WriterForcing Generating more interesting story endings ; We study the problem of generating interesting endings for stories. Neural generative models have shown promising results for various text generation problems. Sequence to Sequence Seq2Seq models are typically trained to generate a single output sequence for a ... |
CatVRNN Generating Category Texts via Multitask Learning ; Controlling the model to generate texts of different categories is a challenging task that is receiving increasing attention. Recently, generative adversarial networks GANs have shown promising results for category text generation. However, the texts generated... |
Longrange Prediction of Vital Signs Using Generative Boosting via LSTM Networks ; Vital signs including heart rate, respiratory rate, body temperature and blood pressure, are critical in the clinical decision making process. Effective early prediction of vital signs help to alert medical practitioner ahead of time and... |
SHADOWCAST Controllable Graph Generation ; We introduce the controllable graph generation problem, formulated as controlling graph attributes during the generative process to produce desired graphs with understandable structures. Using a transparent and straightforward Markov model to guide this generative process, pr... |
Asking Questions Like Educational Experts Automatically Generating QuestionAnswer Pairs on RealWorld Examination Data ; Generating high quality questionanswer pairs is a hard but meaningful task. Although previous works have achieved great results on answeraware question generation, it is difficult to apply them into ... |
Recursive Decoding A Situated Cognition Approach to Compositional Generation in Grounded Language Understanding ; Compositional generalization is a troubling blind spot for neural language models. Recent efforts have presented techniques for improving a model's ability to encode novel combinations of known inputs, but... |
Controllable Text Generation for OpenDomain Creativity and Fairness ; Recent advances in large pretrained language models have demonstrated strong results in generating natural languages and significantly improved performances for many natural language generation NLG applications such as machine translation and text s... |
KDDLGAN Data Limited Image Generation via Knowledge Distillation ; Generative Adversarial Networks GANs rely heavily on largescale training data for training highquality image generation models. With limited training data, the GAN discriminator often suffers from severe overfitting which directly leads to degraded gen... |
GDVDM Generated Depth for better Diffusionbased Video Generation ; The field of generative models has recently witnessed significant progress, with diffusion models showing remarkable performance in image generation. In light of this success, there is a growing interest in exploring the application of diffusion models... |
Trapezoidal Generalization over Linear Constraints ; We are developing a modelbased fuzzing framework that employs mathematical models of system behavior to guide the fuzzing process. Whereas traditional fuzzing frameworks generate tests randomly, a modelbased framework can deduce tests from a behavioral model using a... |
ERNIEViLG Unified Generative Pretraining for Bidirectional VisionLanguage Generation ; Conventional methods for the imagetext generation tasks mainly tackle the naturally bidirectional generation tasks separately, focusing on designing taskspecific frameworks to improve the quality and fidelity of the generated sample... |
The Good, the Bad, and the Missing Neural Code Generation for Machine Learning Tasks ; Machine learning ML has been increasingly used in a variety of domains, while solving ML programming tasks poses unique challenges because of the fundamentally different nature and construction from general programming tasks, especi... |
Bayesian filtering for multiobject systems with independently generated observations ; A general approach for Bayesian filtering of multiobject systems is studied, with particular emphasis on the model where each object generates observations independently of other objects. The approach is based on variational calculu... |
Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing ; Despite their strong performance on many tasks, pretrained language models have been shown to struggle on outofdistribution compositional generalization. Meanwhile, recent work has shown considerable improvements on many NLP ta... |
Generating Gowdy cosmological models ; Using the analogy with stationary axisymmetric solutions, we present a method to generate new analytic cosmological solutions of Einstein's equation belonging to the class of T3 Gowdy cosmological models. We show that the solutions can be generated from their data at the initial ... |
Generalized Poisson sigma models ; A general master action in terms of superfields is given which generates generalized Poisson sigma models by means of a natural ghost number prescription. The simplest representation is the sigma model considered by Cattaneo and Felder. For Dirac brackets considerably more general mo... |
Introspective Generative Modeling Decide Discriminatively ; We study unsupervised learning by developing introspective generative modeling IGM that attains a generator using progressively learned deep convolutional neural networks. The generator is itself a discriminator, capable of introspection being able to selfeva... |
Generator Reversal ; We consider the problem of training generative models with deep neural networks as generators, i.e. to map latent codes to data points. Whereas the dominant paradigm combines simple priors over codes with complex deterministic models, we propose instead to use more flexible code distributions. The... |
MultiTask Learning of Generation and Classification for EmotionAware Dialogue Response Generation ; For a computer to naturally interact with a human, it needs to be humanlike. In this paper, we propose a neural response generation model with multitask learning of generation and classification, focusing on emotion. Ou... |
Deep Generative Modeling with Backward Stochastic Differential Equations ; This paper proposes a novel deep generative model, called BSDEGen, which combines the flexibility of backward stochastic differential equations BSDEs with the power of deep neural networks for generating highdimensional complex target data, par... |
A brief review of supersymmetric nonlinear sigma models and generalized complex geometry ; This is a review of the relation between supersymmetric nonlinear sigma models and target space geometry. In particular, we report on the derivation of generalized Kahler geometry from sigma models with additional spinorial supe... |
Dynamics of homogeneous scalar fields with general selfinteraction potentials cosmological and gravitational collapse models ; The general relativistic dynamics of a wide class of selfinteracting, selfgravitating homogeneous scalar fields models is analyzed. The class is characterized by certain general conditions on ... |
Multipleevent probability in generalrelativistic quantum mechanics a discrete model ; We introduce a simple quantum mechanical model in which time and space are discrete and periodic. These features avoid the complications related to continuousspectrum operators and infinitenorm states. The model provides a tool for d... |
Identifying Generalization Properties in Neural Networks ; While it has not yet been proven, empirical evidence suggests that model generalization is related to local properties of the optima which can be described via the Hessian. We connect model generalization with the local property of a solution under the PACBaye... |
Entropyregularized Optimal Transport Generative Models ; We investigate the use of entropyregularized optimal transport EOT cost in developing generative models to learn implicit distributions. Two generative models are proposed. One uses EOT cost directly in an oneshot optimization problem and the other uses EOT cost... |
Divide and Generate Neural Generation of Complex Sentences ; We propose a task to generate a complex sentence from a simple sentence in order to amplify various kinds of responses in the database. We first divide a complex sentence into a main clause and a subordinate clause to learn a generator model of modifiers, an... |
Flexible Prior Distributions for Deep Generative Models ; We consider the problem of training generative models with deep neural networks as generators, i.e. to map latent codes to data points. Whereas the dominant paradigm combines simple priors over codes with complex deterministic models, we argue that it might be ... |
Learning the Base Distribution in Implicit Generative Models ; Popular generative model learning methods such as Generative Adversarial Networks GANs, and Variational Autoencoders VAE enforce the latent representation to follow simple distributions such as isotropic Gaussian. In this paper, we argue that learning a co... |
Deformed general relativity and scalartensor models ; We calculate the most general action for a scalartensor model up to quadratic order in derivatives with deformed general covariance and nonminimal coupling. We demonstrate how different choices of the free functions recover specific well known scalartensor models. ... |
Latent Variable Modeling for Generative Concept Representations and Deep Generative Models ; Latent representations are the essence of deep generative models and determine their usefulness and power. For latent representations to be useful as generative concept representations, their latent space must support latent s... |
GoalEmbedded Dual Hierarchical Model for TaskOriented Dialogue Generation ; Hierarchical neural networks are often used to model inherent structures within dialogues. For goaloriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns between two interlocuto... |
Sequence Modeling with Unconstrained Generation Order ; The dominant approach to sequence generation is to produce a sequence in some predefined order, e.g. left to right. In contrast, we propose a more general model that can generate the output sequence by inserting tokens in any arbitrary order. Our model learns dec... |
Statistical guarantees for generative models without domination ; In this paper, we introduce a convenient framework for studying adversarial generative models from a statistical perspective. It consists in modeling the generative device as a smooth transformation of the unit hypercube of a dimension that is much smal... |
Deep Denerative Models for Drug Design and Response ; Designing new chemical compounds with desired pharmaceutical properties is a challenging task and takes years of development and testing. Still, a majority of new drugs fail to prove efficient. Recent success of deep generative modeling holds promises of generation... |
Conformal Generative Modeling on Triangulated Surfaces ; We propose conformal generative modeling, a framework for generative modeling on 2D surfaces approximated by discrete triangle meshes. Our approach leverages advances in discrete conformal geometry to develop a map from a source triangle mesh to a target triangl... |
Generalized generalized linear models Convex estimation and online bounds ; We introduce a new computational framework for estimating parameters in generalized generalized linear models GGLM, a class of models that extends the popular generalized linear models GLM to account for dependencies among observations in spat... |
Generalization of nonlinear Murnaghan elastic model for viscoelastic materials ; This paper presents a generalization of Murnaghan elastic material to viscoelastic behavior using the GreenRivlin multipleintegral approach. In the linear limit, the model coincides with the generalized Maxwell model. To create a nonlinea... |
One generalization of the Dicketype models ; We discuss one family of possible generalizations of the JaynesCummings and the TavisCummings models using the technique of algebraic Bethe ansatz related to the Gaudintype models. In particular, we present a family of generically nonHermitian Hamiltonians that generalize p... |
Finiteness of a spinfoam model for euclidean quantum general relativity ; We prove that a certain spinfoam model for euclidean quantum general relativity, recently defined, is finite all its all Feynman diagrams converge. The model is a variant of the BarrettCrane model, and is defined in terms of a field theory over ... |
Some properties of metastable supersymmetrybreaking vacua in WessZumino models ; As a contribution to the current efforts to understand supersymmetrybreaking by metastable vacua, we study general properties of supersymmetrybreaking vacua in WessZumino models we show that treelevel degeneracy is generic, explore some c... |
Results on modelling and products of singularities in Colombeau algebra GR ; Modelling of singularities given by discontinuous functions or distributions by means of generalized functions has proved useful in many problems posed by physical phenomena. We introduce in a systematic way generalized functions of Colombeau... |
On Generalized Rectangular Fuzzy Model for Assessment ; The article is dedicated to the analysis of the existing models for assessment based of the fuzzy logic centroid technique. A new Generalized Rectangular Model were developed. Some generalizations of the existing models are offered. |
Synthetic Language Generation and Model Validation in BEAST2 ; Generating synthetic languages aids in the testing and validation of future computational linguistic models and methods. This thesis extends the BEAST2 phylogenetic framework to add linguistic sequence generation under multiple models. The new plugin is th... |
Supersymmetric Extension of Preonic Models General Remarks ; We present some general remarks on supersymmetric extensions of fermionscalar and threefermion preonic models with an assumption of supersymmetry is realized at preonic level. The motivation and the requirement of this assumption are briefly given. In genera... |
Interpreting canonical tensor model in minisuperspace ; Canonical tensor model is a theory of dynamical fuzzy spaces in arbitrary spacetime dimensions. Examining its simplest case, we find a connection to a minisuperspace model of general relativity in arbitrary dimensions. This is a first step in interpreting variabl... |
Critical trees counterexamples in model checking of CSM systems using CBS algorithm ; The important feature of temporal model checking is the generation of counterexamples. In the report, the requirements for generation of counterexample called critical tree in model checking of CSM systems are described. The output o... |
Generalized operatorscaling random ball model ; This article introduces the operatorscaling random ball model, generalizing the isotropic random ball models investigated recently in the literature to anisotropic setup. The model is introduced as a generalized random field and results on weak convergence are establishe... |
Learning to Compare for Better Training and Evaluation of Open Domain Natural Language Generation Models ; Automated evaluation of open domain natural language generation NLG models remains a challenge and widely used metrics such as BLEU and Perplexity can be misleading in some cases. In our paper, we propose to eval... |
Neural Network based Explicit Mixture Models and Expectationmaximization based Learning ; We propose two neural network based mixture models in this article. The proposed mixture models are explicit in nature. The explicit models have analytical forms with the advantages of computing likelihood and efficiency of gener... |
Learning Generative Models with Visual Attention ; Attention has long been proposed by psychologists as important for effectively dealing with the enormous sensory stimulus available in the neocortex. Inspired by the visual attention models in computational neuroscience and the need of objectcentric data for generativ... |
Empirical Models for the Realistic Generation of Cooperative Awareness Messages in Vehicular Networks ; Most V2X VehicletoEverything applications rely on broadcasting awareness messages known as CAM Cooperative Awareness Messages in ETSI or BSM Basic Safety Message in SAE standards. A large number of studies have been... |
Facilitating automated conversion of scientific knowledge into scientific simulation models with the Machine Assisted Generation, Calibration, and Comparison MAGCC Framework ; The Machine Assisted Generation, Comparison, and Calibration MAGCC framework provides machine assistance and automation of recurrent crucial st... |
Discriminative Models Can Still Outperform Generative Models in Aspect Based Sentiment Analysis ; Aspectbased Sentiment Analysis ABSA helps to explain customers' opinions towards products and services. In the past, ABSA models were discriminative, but more recently generative models have been used to generate aspects ... |
Which Kind Is Better in Opendomain Multiturn Dialog,Hierarchical or Nonhierarchical Models An Empirical Study ; Currently, opendomain generative dialog systems have attracted considerable attention in academia and industry. Despite the success of singleturn dialog generation, multiturn dialog generation is still a big... |
Dynamical Systems Analysis of Various Dark Energy Models ; In this thesis, we used dynamical systems analysis to find the qualitative behaviour of some dark energy models. Specifically, dynamical systems analysis of quintessence scalar field models, chameleon scalar field models and holographic models of dark energy a... |
Study of Thermodynamics in Generalized Holographic and Ricci Dark Energy Models ; We have considered the flat FRW model of the universe which is filled with the combination of dark matter and dark energy. Here we have considered two types of dark energy models i Generalized holographic and ii generalized Ricci dark en... |
MuseGAN Multitrack Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment ; Generating music has a few notable differences from generating images and videos. First, music is an art of time, necessitating a temporal model. Second, music is usually composed of multiple instrumentstrac... |
Generalization in Automated Process Discovery A Framework based on Event Log Patterns ; The importance of quality measures in process mining has increased. One of the key quality aspects, generalization, is concerned with measuring the degree of overfitting of a process model w.r.t. an event log, since the recorded be... |
Incorporating Domain Knowledge through Task Augmentation for FrontEnd JavaScript Code Generation ; Code generation aims to generate a code snippet automatically from natural language descriptions. Generally, the mainstream code generation methods rely on a large amount of paired training data, including both the natur... |
Prompt Generate Train PGT Fewshot Domain Adaption of Retrieval Augmented Generation Models for Open Book QuestionAnswering ; We propose a framework Prompt, Generate, Train PGT to efficiently develop a generative questionanswering model for openbook questionanswering over a proprietary collection of text documents. T... |
A Bayesian Nonparametric Approach to Generative Models Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy ; Generative models have emerged as a promising technique for producing highquality images that are indistinguishable from real images. Generativ... |
Minisuperspace Approach of Generalized Gravitational Models ; Motivated by the dark energy issue, the minisuperspace approach for general relativistic cosmological theories is outlined. |
Efficient Heuristic Generation for Robot Path Planning with Recurrent Generative Model ; Robot path planning is difficult to solve due to the contradiction between optimality of results and complexity of algorithms, even in 2D environments. To find an optimal path, the algorithm needs to search all the state space, wh... |
Deep Generative Models for Galaxy Image Simulations ; Image simulations are essential tools for preparing and validating the analysis of current and future widefield optical surveys. However, the galaxy models used as the basis for these simulations are typically limited to simple parametric light profiles, or use a f... |
FRW Universe Models in Conformally Flat Spacetime Coordinates. I General Formalism ; The 3space of a universe model is defined at a certain simultaneity. Hence space depends on which time is used. We find a general formula generating all known and also some new transformations to conformally flat spacetime coordinates... |
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