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On Finitely Generated Models of Theories with at Most Countably Many Nonisomorphic Finitely Generated Models ; We study finitely generated models of countable theories, having at most countably many nonisomorphic finitely generated models. We intro duce a notion of rank of finitely generated models and we prove, when ... |
Generalized modeling of ecological population dynamics ; Over the past years several authors have used the approach of generalized modeling to study the dynamics of food chains and food webs. Generalized models come close to the efficiency of random matrix models, while being as directly interpretable as conventional ... |
Generating Subsurface Earth Models using Discrete Representation Learning and Deep Autoregressive Network ; Subsurface earth models referred to as geomodels are crucial for characterizing complex subsurface systems. Multiplepoint statistics are commonly used to generate geomodels. In this paper, a deeplearningbased ge... |
On the Generalization of Diffusion Model ; The diffusion probabilistic generative models are widely used to generate highquality data. Though they can synthetic data that does not exist in the training set, the rationale behind such generalization is still unexplored. In this paper, we formally define the generalizati... |
DEFAKE Detection and Attribution of Fake Images Generated by TexttoImage Generation Models ; Texttoimage generation models that generate images based on prompt descriptions have attracted an increasing amount of attention during the past few months. Despite their encouraging performance, these models raise concerns ab... |
Discovering Graph Generation Algorithms ; We provide a novel approach to construct generative models for graphs. Instead of using the traditional probabilistic models or deep generative models, we propose to instead find an algorithm that generates the data. We achieve this using evolutionary search and a powerful fit... |
GenPhys From Physical Processes to Generative Models ; Since diffusion models DM and the more recent Poisson flow generative models PFGM are inspired by physical processes, it is reasonable to ask Can physical processes offer additional new generative models We show that the answer is yes. We introduce a general famil... |
A generative model for molecule generation based on chemical reaction trees ; Deep generative models have been shown powerful in generating novel molecules with desired chemical properties via their representations such as strings, trees or graphs. However, these models are limited in recommending synthetic routes for... |
Diffusion Models for Nonautoregressive Text Generation A Survey ; Nonautoregressive NAR text generation has attracted much attention in the field of natural language processing, which greatly reduces the inference latency but has to sacrifice the generation accuracy. Recently, diffusion models, a class of latent varia... |
ContentBased Search for Deep Generative Models ; The growing proliferation of pretrained generative models has made it infeasible for a user to be fully cognizant of every model in existence. To address this need, we introduce the task of contentbased model search given a query and a large set of generative models, fi... |
Iterative Approximate Byzantine Consensus under a Generalized Fault Model ; In this work, we consider a generalized fault model that can be used to represent a wide range of failure scenarios, including correlated failures and nonuniform node reliabilities. This fault model is general in the sense that fault models st... |
Quantitative Computation Tree Logic Model Checking Based on Generalized Possibility Measures ; We study generalized possibilistic computation tree logic model checking in this paper, which is an extension of possibilistic computation logic model checking introduced by Y.Li, Y.Li and Z.Ma 2014. The system is modeled by... |
Fingerprints of Generative Models in the Frequency Domain ; It is verified in existing works that CNNbased generative models leave unique fingerprints on generated images. There is a lack of analysis about how they are formed in generative models. Interpreting network components in the frequency domain, we derive sour... |
Towards quantitative methods to assess network generative models ; Assessing generative models is not an easy task. Generative models should synthesize graphs which are not replicates of real networks but show topological features similar to real graphs. We introduce an approach for assessing graph generative models u... |
Deep Evolutionary Learning for Molecular Design ; In this paper, we propose a deep evolutionary learning DEL process that integrates fragmentbased deep generative model and multiobjective evolutionary computation for molecular design. Our approach enables 1 evolutionary operations in the latent space of the generative... |
CanvasGAN A simple baseline for text to image generation by incrementally patching a canvas ; We propose a new recurrent generative model for generating images from text captions while attending on specific parts of text captions. Our model creates images by incrementally adding patches on a canvas while attending on ... |
Multimodal Controller for Generative Models ; Classconditional generative models are crucial tools for data generation from userspecified class labels. Existing approaches for classconditional generative models require nontrivial modifications of backbone generative architectures to model conditional information fed i... |
Generative Diffusion Models on Graphs Methods and Applications ; Diffusion models, as a novel generative paradigm, have achieved remarkable success in various image generation tasks such as image inpainting, imagetotext translation, and video generation. Graph generation is a crucial computational task on graphs with ... |
A Note on an R2 Measure for Fixed Effects in the Generalized Linear Mixed Model ; Using the LRT statistic, a model R2 is proposed for the generalized linear mixed model for assessing the association between the correlated outcomes and fixed effects. The R2 compares the full model to a null model with all fixed effects... |
Generative Neurosymbolic Machines ; Reconciling symbolic and distributed representations is a crucial challenge that can potentially resolve the limitations of current deep learning. Remarkable advances in this direction have been achieved recently via generative objectcentric representation models. While learning a r... |
Twist Decoding Diverse Generators Guide Each Other ; Many language generation models are now available for a wide range of generation tasks, including machine translation and summarization. Combining such diverse models may lead to further progress, but ensembling generation models is challenging during inference conv... |
Boosted Generative Models ; We propose a novel approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes. Our metaalgorithmic framework can leverage any existing base learner that permits likelihood evaluation, including recent... |
Image Restoration A General Wavelet Frame Based Model and Its Asymptotic Analysis ; Image restoration is one of the most important areas in imaging science. Mathematical tools have been widely used in image restoration, where wavelet frame based approach is one of the successful examples. In this paper, we introduce a... |
Latent Variable Dialogue Models and their Diversity ; We present a dialogue generation model that directly captures the variability in possible responses to a given input, which reduces the boring output' issue of deterministic dialogue models. Experiments show that our model generates more diverse outputs than baseli... |
Note on the equivalence of hierarchical variational models and auxiliary deep generative models ; This note compares two recently published machine learning methods for constructing flexible, but tractable families of variational hiddenvariable posteriors. The first method, called hierarchical variational models enric... |
ChatGPT is not all you need. A State of the Art Review of large Generative AI models ; During the last two years there has been a plethora of large generative models such as ChatGPT or Stable Diffusion that have been published. Concretely, these models are able to perform tasks such as being a general question and ans... |
Training Discriminative Models to Evaluate Generative Ones ; Generative models are known to be difficult to assess. Recent works, especially on generative adversarial networks GANs, produce good visual samples of varied categories of images. However, the validation of their quality is still difficult to define and the... |
Generalization of the RandallSundrum Model Using Gravitational Model FT, ; In this letter, we explore a generalized model based on two scenarios including the RandallSundrum model and Gravity model FT,Theta. We first study the standard RandallSundrum Gravitational model and then add a function containing two paramete... |
OMSDPM Optimizing the Model Schedule for Diffusion Probabilistic Models ; Diffusion probabilistic models DPMs are a new class of generative models that have achieved stateoftheart generation quality in various domains. Despite the promise, one major drawback of DPMs is the slow generation speed due to the large number... |
Learning to Generate Lumped Hydrological Models ; In a lumped hydrological model structure, the hydrological function of a catchment is characterized by only a few parameters. Given a set of parameter values, a numerical function useful for hydrological prediction is generated. Thus, this study assumes that the hydrol... |
How Do Neural Sequence Models Generalize Local and Global Context Cues for OutofDistribution Prediction ; After a neural sequence model encounters an unexpected token, can its behavior be predicted We show that RNN and transformer language models exhibit structured, consistent generalization in outofdistribution conte... |
ScoreBased Generative Models for Molecule Generation ; Recent advances in generative models have made exploring design spaces easier for de novo molecule generation. However, popular generative models like GANs and normalizing flows face challenges such as training instabilities due to adversarial training and archite... |
FGAMFast Adversarial Malware Generation Method Based on Gradient Sign ; Malware detection models based on deep learning have been widely used, but recent research shows that deep learning models are vulnerable to adversarial attacks. Adversarial attacks are to deceive the deep learning model by generating adversarial ... |
DiffAR Denoising Diffusion Autoregressive Model for Raw Speech Waveform Generation ; Diffusion models have recently been shown to be relevant for highquality speech generation. Most work has been focused on generating spectrograms, and as such, they further require a subsequent model to convert the spectrogram to a wa... |
Unifying GANs and ScoreBased Diffusion as Generative Particle Models ; Particlebased deep generative models, such as gradient flows and scorebased diffusion models, have recently gained traction thanks to their striking performance. Their principle of displacing particle distributions by differential equations is conv... |
Towards Personalized PromptModel Retrieval for Generative Recommendation ; Recommender Systems are built to retrieve relevant items to satisfy users' information needs. The candidate corpus usually consists of a finite set of items that are ready to be served, such as videos, products, or articles. With recent advance... |
Securing Deep Generative Models with Universal Adversarial Signature ; Recent advances in deep generative models have led to the development of methods capable of synthesizing highquality, realistic images. These models pose threats to society due to their potential misuse. Prior research attempted to mitigate these t... |
Evaluating Generative Models for GraphtoText Generation ; Large language models LLMs have been widely employed for graphtotext generation tasks. However, the process of finetuning LLMs requires significant training resources and annotation work. In this paper, we explore the capability of generative models to generate... |
Learning to Make Predictions In Partially Observable Environments Without a Generative Model ; When faced with the problem of learning a model of a highdimensional environment, a common approach is to limit the model to make only a restricted set of predictions, thereby simplifying the learning problem. These partial ... |
Deep Generative Models for Vehicle Speed Trajectories ; Generating realistic vehicle speed trajectories is a crucial component in evaluating vehicle fuel economy and in predictive control of selfdriving cars. Traditional generative models rely on Markov chain methods and can produce accurate synthetic trajectories but... |
Paths of FriedmannRobertsonWalker brane models ; Dynamics of braneworld models of dark energy is reviewed. We demonstrate that simple dark energy brane models can be represented as 2dimensional dynamical systems of a Newtonian type. Hence a fictitious particle moving in a potential well characterizes the model. We inv... |
Distilling the Knowledge of Largescale Generative Models into Retrieval Models for Efficient Opendomain Conversation ; Despite the remarkable performance of largescale generative models in opendomain conversation, they are known to be less practical for building realtime conversation systems due to high latency. On th... |
More on Generalized Heisenberg Ferromagnet Models ; We generalize the integrable Heisenberg ferromagnet model according to each Hermitian symmetric spaces and address various new aspects of the generalized model. Using the first order formalism of generalized spins which are defined on the coadjoint orbits of arbitrar... |
On the Cofibrant Generation of Model Categories ; The paper studies the problem of the cofibrant generation of a model category. We prove that, assuming Vopvenka's principle, every cofibrantly generated model category is Quillen equivalent to a combinatorial model category. We discuss cases where this result implies t... |
Generating Images from Captions with Attention ; Motivated by the recent progress in generative models, we introduce a model that generates images from natural language descriptions. The proposed model iteratively draws patches on a canvas, while attending to the relevant words in the description. After training on Mi... |
Formal Context Generation using Dirichlet Distributions ; We suggest an improved way to randomly generate formal contexts based on Dirichlet distributions. For this purpose we investigate the predominant way to generate formal contexts, a cointossing model, recapitulate some of its shortcomings and examine its stochas... |
Diffusion models for Handwriting Generation ; In this paper, we propose a diffusion probabilistic model for handwriting generation. Diffusion models are a class of generative models where samples start from Gaussian noise and are gradually denoised to produce output. Our method of handwriting generation does not requi... |
Text Generation Based on Generative Adversarial Nets with Latent Variable ; In this paper, we propose a model using generative adversarial net GAN to generate realistic text. Instead of using standard GAN, we combine variational autoencoder VAE with generative adversarial net. The use of highlevel latent random variab... |
Consistency Models ; Diffusion models have significantly advanced the fields of image, audio, and video generation, but they depend on an iterative sampling process that causes slow generation. To overcome this limitation, we propose consistency models, a new family of models that generate high quality samples by dire... |
Dirichlet Diffusion Score Model for Biological Sequence Generation ; Designing biological sequences is an important challenge that requires satisfying complex constraints and thus is a natural problem to address with deep generative modeling. Diffusion generative models have achieved considerable success in many appli... |
A Conditional Generative Chatbot using Transformer Model ; A Chatbot serves as a communication tool between a human user and a machine to achieve an appropriate answer based on the human input. In more recent approaches, a combination of Natural Language Processing and sequential models are used to build a generative ... |
Paired 3D Model Generation with Conditional Generative Adversarial Networks ; Generative Adversarial Networks GANs are shown to be successful at generating new and realistic samples including 3D object models. Conditional GAN, a variant of GANs, allows generating samples in given conditions. However, objects generated... |
MoFlow An Invertible Flow Model for Generating Molecular Graphs ; Generating molecular graphs with desired chemical properties driven by deep graph generative models provides a very promising way to accelerate drug discovery process. Such graph generative models usually consist of two steps learning latent representat... |
Will Largescale Generative Models Corrupt Future Datasets ; Recently proposed largescale texttoimage generative models such as DALLcdotE 2, Midjourney, and StableDiffusion can generate highquality and realistic images from users' prompts. Not limited to the research community, ordinary Internet users enjoy these gener... |
Conditional MoCoGAN for ZeroShot Video Generation ; We propose a conditional generative adversarial network GAN model for zeroshot video generation. In this study, we have explored zeroshot conditional generation setting. In other words, we generate unseen videos from training samples with missing classes. The task is... |
Safer Together Machine Learning Models Trained on Shared Accident Datasets Predict Construction Injuries Better than CompanySpecific Models ; In this study, we capitalized on a collective dataset repository of 57k accidents from 9 companies belonging to 3 domains and tested whether models trained on multiple datasets ... |
On model structure for coreflective subcategories of a model category ; Let bf C be a coreflective subcategory of a cofibrantly generated model category bf D. In this paper we show that under suitable conditions bf C admits a cofibrantly generated model structure which is left Quillen adjunct to the model structure on... |
Are generative deep models for novelty detection truly better ; Many deep models have been recently proposed for anomaly detection. This paper presents comparison of selected generative deep models and classical anomaly detection methods on an extensive number of nonimage benchmark datasets. We provide statistical com... |
Generalization and Memorization The Bias Potential Model ; Models for learning probability distributions such as generative models and density estimators behave quite differently from models for learning functions. One example is found in the memorization phenomenon, namely the ultimate convergence to the empirical di... |
Visual Conceptual Blending with Largescale Language and Vision Models ; We ask the question to what extent can recent largescale language and image generation models blend visual concepts Given an arbitrary object, we identify a relevant object and generate a singlesentence description of the blend of the two using a ... |
Generative Adversarial Imitation Learning for Empathybased AI ; Generative adversarial imitation learning GAIL is a modelfree algorithm that has been shown to provide strong results in imitating complex behaviors in highdimensional environments. In this paper, we utilize the GAIL model for text generation to develop e... |
Towards an Automatic Optimisation Model Generator Assisted with Generative Pretrained Transformer ; This article presents a framework for generating optimisation models using a pretrained generative transformer. The framework involves specifying the features that the optimisation model should have and using a language... |
SkeletontoResponse Dialogue Generation Guided by Retrieval Memory ; For dialogue response generation, traditional generative models generate responses solely from input queries. Such models rely on insufficient information for generating a specific response since a certain query could be answered in multiple ways. Con... |
Alterationfree and Modelagnostic Origin Attribution of Generated Images ; Recently, there has been a growing attention in image generation models. However, concerns have emerged regarding potential misuse and intellectual property IP infringement associated with these models. Therefore, it is necessary to analyze the ... |
Equality conditions for internal entropies of certain classical and quantum models ; Mathematical models use information from past observations to generate predictions about the future. If two models make identical predictions the one that needs less information from the past to do this is preferred. It is already kno... |
Development of a Mathematical Model for HarborManeuvers to Realize Modeling Automation ; A simulation environment of harbor maneuvers is critical for developing automatic berthing. Dynamic models are widely used to estimate harbor maneuvers. However, human decisionmaking and data analysis are necessary to derive, sele... |
A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models ; Existing neural ranking models follow the text matching paradigm, where documenttoquery relevance is estimated through predicting the matching score. Drawing from the rich literature of classical generative retrieval models, we introduce ... |
Reverse Engineering Configurations of Neural Text Generation Models ; This paper seeks to develop a deeper understanding of the fundamental properties of neural text generations models. The study of artifacts that emerge in machine generated text as a result of modeling choices is a nascent research area. Previously, ... |
3d human motion generation from the text via gesture action classification and the autoregressive model ; In this paper, a deep learningbased model for 3D human motion generation from the text is proposed via gesture action classification and an autoregressive model. The model focuses on generating special gestures th... |
Gradient Estimation for Unseen Domain Risk Minimization with PreTrained Models ; Domain generalization aims to build generalized models that perform well on unseen domains when only source domains are available for model optimization. Recent studies have shown that largescale pretrained models can enhance domain gener... |
LongForm Optimizing Instruction Tuning for Long Text Generation with Corpus Extraction ; Instruction tuning enables language models to generalize more effectively and better follow user intent. However, obtaining instruction data can be costly and challenging. Prior works employ methods such as expensive human annotat... |
PLANNER Generating Diversified Paragraph via Latent Language Diffusion Model ; Autoregressive models for text sometimes generate repetitive and lowquality output because errors accumulate during the steps of generation. This issue is often attributed to exposure bias the difference between how a model is trained, and... |
Multispan Style Extraction for Generative Reading Comprehension ; Generative machine reading comprehension MRC requires a model to generate wellformed answers. For this type of MRC, answer generation method is crucial to the model performance. However, generative models, which are supposed to be the right model for th... |
Regularising Inverse Problems with Generative Machine Learning Models ; Deep neural network approaches to inverse imaging problems have produced impressive results in the last few years. In this paper, we consider the use of generative models in a variational regularisation approach to inverse problems. The considered... |
Generalized Univariate Distributions and a New Asymmetric Laplace Model ; This work provides a survey of the general class of distributions generated from the mixture of the beta random variables. We provide an extensive review of the literature, concerning generating new distributions via the inverse CDF transformati... |
Code Generator Composition for ModelDriven Engineering of Robotics Component Connector Systems ; Engineering software for robotics applications requires multidomain and applicationspecific solutions. Modeldriven engineering and modeling language integration provide means for developing specialized, yet reusable model... |
On the goodnessoffit of generalized linear geostatistical models ; We propose a generalization of Zhang's coefficient of determination to generalized linear geostatistical models and illustrate its application to riverblindness mapping. The generalized coefficient of determination has a more intuitive interpretation t... |
Descriptive inner model theory ; A paper for general audience about descriptive inner model theory. |
Cooperative Training of Descriptor and Generator Networks ; This paper studies the cooperative training of two generative models for image modeling and synthesis. Both models are parametrized by convolutional neural networks ConvNets. The first model is a deep energybased model, whose energy function is defined by a b... |
A Model to Search for Synthesizable Molecules ; Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models allow one to generate molecules with desirable properties, they give no guarantees that the molecules can actually be... |
AraGPT2 PreTrained Transformer for Arabic Language Generation ; Recently, pretrained transformerbased architectures have proven to be very efficient at language modeling and understanding, given that they are trained on a large enough corpus. Applications in language generation for Arabic are still lagging in comparis... |
Pruning's Effect on Generalization Through the Lens of Training and Regularization ; Practitioners frequently observe that pruning improves model generalization. A longstanding hypothesis based on biasvariance tradeoff attributes this generalization improvement to model size reduction. However, recent studies on overp... |
MetaCoTGAN A Meta Cooperative Training Paradigm for Improving Adversarial Text Generation ; Training generative models that can generate highquality text with sufficient diversity is an important open problem for Natural Language Generation NLG community. Recently, generative adversarial models have been applied exten... |
Automatic Conditional Generation of Personalized Social Media Short Texts ; Automatic text generation has received much attention owing to rapid development of deep neural networks. In general, text generation systems based on statistical language model will not consider anthropomorphic characteristics, which results ... |
Unconditional Audio Generation with Generative Adversarial Networks and Cycle Regularization ; In a recent paper, we have presented a generative adversarial network GANbased model for unconditional generation of the melspectrograms of singing voices. As the generator of the model is designed to take a variablelength s... |
PHomGeM Persistent Homology for Generative Models ; Generative neural network models, including Generative Adversarial Network GAN and AutoEncoders AE, are among the most popular neural network models to generate adversarial data. The GAN model is composed of a generator that produces synthetic data and of a discrimin... |
FlowSeq NonAutoregressive Conditional Sequence Generation with Generative Flow ; Most sequencetosequence seq2seq models are autoregressive; they generate each token by conditioning on previously generated tokens. In contrast, nonautoregressive seq2seq models generate all tokens in one pass, which leads to increased ef... |
Adapting a Language Model for Controlled Affective Text Generation ; Human use language not just to convey information but also to express their inner feelings and mental states. In this work, we adapt the stateoftheart language generation models to generate affective emotional text. We posit a model capable of genera... |
Planning with Logical Graphbased Language Model for Instruction Generation ; Despite the superior performance of large language models to generate natural language texts, it is hard to generate texts with correct logic according to a given task, due to the difficulties for neural models to capture implied rules from f... |
Modeling Graphs Using a Mixture of Kronecker Models ; Generative models for graphs are increasingly becoming a popular tool for researchers to generate realistic approximations of graphs. While in the past, focus was on generating graphs which follow general laws, such as the power law for degree distribution, current... |
Models of representations and Langlands functoriality ; In this article we explore the interplay between two generalizations of the Whittaker model, namely the Klyachko models and the degenerate Whittaker models, and two functorial constructions, namely base change and automorphic induction, for the class of unitariza... |
Dynamical analysis of a generalized hepatitis B epidemic model and its dynamically consistent discrete model ; The aim of this work is to study qualitative dynamical properties of a generalized hepatitis B epidemic model and its dynamically consistent discrete model. |
Datafree Blackbox Attack based on Diffusion Model ; Since the training data for the target model in a datafree blackbox attack is not available, most recent schemes utilize GANs to generate data for training substitute model. However, these GANsbased schemes suffer from low training efficiency as the generator needs t... |
A Stochastic Grammar for Natural Shapes ; We consider object detection using a generic model for natural shapes. A common approach for object recognition involves matching object models directly to images. Another approach involves building intermediate representations via a generic grouping processes. We argue that t... |
Generalized immediate exchange models and their symmetries ; We reconsider the immediate exchange model and define a more general class of models where mass is split, exchanged and merged. We relate the splitting process to the symmetric inclusion process via thermalization and from that obtain symmetries and selfdual... |
Generative Adversarial Networks for Model Order Reduction in Seismic FullWaveform Inversion ; I train a Generative Adversarial Network to produce realistic seismic wave speed models. I integrate the generator network into seismic FullWaveform Inversion to reduce the number of model parameters and restrict the inverted... |
Adversarial Training Improves Joint EnergyBased Generative Modelling ; We propose the novel framework for generative modelling using hybrid energybased models. In our method we combine the interpretable input gradients of the robust classifier and Langevin Dynamics for sampling. Using the adversarial training we impro... |
Replicating Active Appearance Model by Generator Network ; A recent Cell paper Chang and Tsao, 2017 reports an interesting discovery. For the face stimuli generated by a pretrained active appearance model AAM, the responses of neurons in the areas of the primate brain that are responsible for face recognition exhibit ... |
DIRE for DiffusionGenerated Image Detection ; Diffusion models have shown remarkable success in visual synthesis, but have also raised concerns about potential abuse for malicious purposes. In this paper, we seek to build a detector for telling apart real images from diffusiongenerated images. We find that existing de... |
Synthetic Dataset Generation with ItemsetBased Generative Models ; This paper proposes three different data generators, tailored to transactional datasets, based on existing itemsetbased generative models. All these generators are intuitive and easy to implement and show satisfactory performance. The quality of each g... |
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