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To Point or Not to Point Understanding How Abstractive Summarizers Paraphrase Text ; Abstractive neural summarization models have seen great improvements in recent years, as shown by ROUGE scores of the generated summaries. But despite these improved metrics, there is limited understanding of the strategies different ... |
Network Generation with Differential Privacy ; We consider the problem of generating private synthetic versions of realworld graphs containing private information while maintaining the utility of generated graphs. Differential privacy is a gold standard for data privacy, and the introduction of the differentially priv... |
Riemannian ScoreBased Generative Modelling ; Scorebased generative models SGMs are a powerful class of generative models that exhibit remarkable empirical performance. Scorebased generative modelling SGM consists of a noising'' stage, whereby a diffusion is used to gradually add Gaussian noise to data, and a generativ... |
Can Pushforward Generative Models Fit Multimodal Distributions ; Many generative models synthesize data by transforming a standard Gaussian random variable using a deterministic neural network. Among these models are the Variational Autoencoders and the Generative Adversarial Networks. In this work, we call them pushf... |
Your ViT is Secretly a Hybrid DiscriminativeGenerative Diffusion Model ; Diffusion Denoising Probability Models DDPM and Vision Transformer ViT have demonstrated significant progress in generative tasks and discriminative tasks, respectively, and thus far these models have largely been developed in their own domains. ... |
How good are deep models in understanding the generated images ; My goal in this paper is twofold to study how well deep models can understand the images generated by DALLE 2 and Midjourney, and to quantitatively evaluate these generative models. Two sets of generated images are collected for object recognition and vi... |
CHeart A Conditional SpatioTemporal Generative Model for Cardiac Anatomy ; Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with nonimaging clinical factors such as gender, age and diseases. While the first question can... |
SlotDiffusion ObjectCentric Generative Modeling with Diffusion Models ; Objectcentric learning aims to represent visual data with a set of object entities a.k.a. slots, providing structured representations that enable systematic generalization. Leveraging advanced architectures like Transformers, recent approaches hav... |
GPTFL Generative Pretrained ModelAssisted Federated Learning ; In this work, we propose GPTFL, a generative pretrained modelassisted federated learning FL framework. At its core, GPTFL leverages generative pretrained models to generate diversified synthetic data. These generated data are used to train a downstream mod... |
Exploring an LM to generate Prolog Predicates from Mathematics Questions ; Recently, there has been a surge in interest in NLP driven by ChatGPT. ChatGPT, a transformerbased generative language model of substantial scale, exhibits versatility in performing various tasks based on natural language. Nevertheless, large l... |
Perceptual Generative Autoencoders ; Modern generative models are usually designed to match target distributions directly in the data space, where the intrinsic dimension of data can be much lower than the ambient dimension. We argue that this discrepancy may contribute to the difficulties in training generative model... |
What is the Reward for Handwriting Handwriting Generation by Imitation Learning ; Analyzing the handwriting generation process is an important issue and has been tackled by various generation models, such as kinematics based models and stochastic models. In this study, we use a reinforcement learning RL framework to ... |
Neural RuleExecution Tracking Machine For TransformerBased Text Generation ; SequencetoSequence S2S neural text generation models, especially the pretrained ones e.g., BART and T5, have exhibited compelling performance on various natural language generation tasks. However, the blackbox nature of these models limits th... |
HighFidelity Synthesis with Disentangled Representation ; Learning disentangled representation of data without supervision is an important step towards improving the interpretability of generative models. Despite recent advances in disentangled representation learning, existing approaches often suffer from the tradeof... |
Viable Threat on News Reading Generating Biased News Using Natural Language Models ; Recent advancements in natural language generation has raised serious concerns. Highperformance language models are widely used for language generation tasks because they are able to produce fluent and meaningful sentences. These mode... |
DYPLOC Dynamic Planning of Content Using Mixed Language Models for Text Generation ; We study the task of longform opinion text generation, which faces at least two distinct challenges. First, existing neural generation models fall short of coherence, thus requiring efficient content planning. Second, diverse types of... |
COINS Dynamically Generating COntextualized Inference Rules for Narrative Story Completion ; Despite recent successes of large pretrained language models in solving reasoning tasks, their inference capabilities remain opaque. We posit that such models can be made more interpretable by explicitly generating interim inf... |
CRASH Raw Audio Scorebased Generative Modeling for Controllable Highresolution Drum Sound Synthesis ; In this paper, we propose a novel scorebase generative model for unconditional raw audio synthesis. Our proposal builds upon the latest developments on diffusion process modeling with stochastic differential equations... |
A Kernelised Stein Statistic for Assessing Implicit Generative Models ; Synthetic data generation has become a key ingredient for training machine learning procedures, addressing tasks such as data augmentation, analysing privacysensitive data, or visualising representative samples. Assessing the quality of such synth... |
Articulation GAN Unsupervised modeling of articulatory learning ; Generative deep neural networks are widely used for speech synthesis, but most existing models directly generate waveforms or spectral outputs. Humans, however, produce speech by controlling articulators, which results in the production of speech sounds... |
SILVR Guided Diffusion for Molecule Generation ; Computationally generating novel synthetically accessible compounds with high affinity and low toxicity is a great challenge in drug design. Machinelearning models beyond conventional pharmacophoric methods have shown promise in generating novel small molecule compounds... |
You Can Generate It Again Datatotext Generation with Verification and Correction Prompting ; Despite significant advancements in existing models, generating text descriptions from structured data input, known as datatotext generation, remains a challenging task. In this paper, we propose a novel approach that goes bey... |
Personaaware Generative Model for Codemixed Language ; Codemixing and scriptmixing are prevalent across online social networks and multilingual societies. However, a user's preference toward codemixing depends on the socioeconomic status, demographics of the user, and the local context, which existing generative model... |
IRGAN A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models ; This paper provides a unified account of two schools of thinking in information retrieval modelling the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing ... |
Robustness Analysis of Deep Learning Models for Population Synthesis ; Deep generative models have become useful for synthetic data generation, particularly population synthesis. The models implicitly learn the probability distribution of a dataset and can draw samples from a distribution. Several models have been pro... |
DiffInstruct A Universal Approach for Transferring Knowledge From Pretrained Diffusion Models ; Due to the ease of training, ability to scale, and high sample quality, diffusion models DMs have become the preferred option for generative modeling, with numerous pretrained models available for a wide variety of datasets... |
A Test of Relative Similarity For Model Selection in Generative Models ; Probabilistic generative models provide a powerful framework for representing data that avoids the expense of manual annotation typically needed by discriminative approaches. Model selection in this generative setting can be challenging, however,... |
Modelbased Adversarial Imitation Learning ; Generative adversarial learning is a popular new approach to training generative models which has been proven successful for other related problems as well. The general idea is to maintain an oracle D that discriminates between the expert's data distribution and that of the ... |
Defending Neural Backdoors via Generative Distribution Modeling ; Neural backdoor attack is emerging as a severe security threat to deep learning, while the capability of existing defense methods is limited, especially for complex backdoor triggers. In the work, we explore the space formed by the pixel values of all p... |
Learning Latent Space EnergyBased Prior Model for Molecule Generation ; Deep generative models have recently been applied to molecule design. If the molecules are encoded in linear SMILES strings, modeling becomes convenient. However, models relying on string representations tend to generate invalid samples and duplic... |
The Power of Fragmentation A Hierarchical Transformer Model for Structural Segmentation in Symbolic Music Generation ; Symbolic Music Generation relies on the contextual representation capabilities of the generative model, where the most prevalent approach is the Transformerbased model. The learning of musical context... |
On the Strong Correlation Between Model Invariance and Generalization ; Generalization and invariance are two essential properties of any machine learning model. Generalization captures a model's ability to classify unseen data while invariance measures consistency of model predictions on transformations of the data. ... |
DiffuSeq Sequence to Sequence Text Generation with Diffusion Models ; Recently, diffusion models have emerged as a new paradigm for generative models. Despite the success in domains using continuous signals such as vision and audio, adapting diffusion models to natural language is underexplored due to the discrete nat... |
Automatic Generation of German Drama Texts Using Fine Tuned GPT2 Models ; This study is devoted to the automatic generation of German drama texts. We suggest an approach consisting of two key steps finetuning a GPT2 model the outline model to generate outlines of scenes based on keywords and finetuning a second model ... |
Neural Artistic Style Transfer with Conditional Adversaria ; A neural artistic style transformation NST model can modify the appearance of a simple image by adding the style of a famous image. Even though the transformed images do not look precisely like artworks by the same artist of the respective style images, the ... |
Understanding how Differentially Private Generative Models Spend their Privacy Budget ; Generative models trained with Differential Privacy DP are increasingly used to produce synthetic data while reducing privacy risks. Navigating their specific privacyutility tradeoffs makes it challenging to determine which models ... |
PaDGAN A Generative Adversarial Network for Performance Augmented Diverse Designs ; Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face three challenges 1 generated designs lack diversity ... |
Learning a powerful SVM using piecewise linear loss functions ; In this paper, we have considered general kpiecewise linear convex loss functions in SVM model for measuring the empirical risk. The resulting kPiecewise Linear loss Support Vector Machine kPLSVM model is an adaptive SVM model which can learn a suitable p... |
LSTM based Conversation Models ; In this paper, we present a conversational model that incorporates both context and participant role for twoparty conversations. Different architectures are explored for integrating participant role and context information into a Long Shortterm Memory LSTM language model. The conversat... |
Ai4EComponentLib.jl A Componentbase Model Library in Julia ; Ai4EComponentLib.jlAi4EComponentLib is a componentbase model library based on Julia language, which relies on the differential equation solver DifferentialEquations.jl and the symbolic modeling tool Modelingtoolkit.jl. To handle problems in different physica... |
Generalization Metrics for Practical Quantum Advantage in Generative Models ; As the quantum computing community gravitates towards understanding the practical benefits of quantum computers, having a clear definition and evaluation scheme for assessing practical quantum advantage in the context of specific application... |
Generating Images Part by Part with Composite Generative Adversarial Networks ; Image generation remains a fundamental problem in artificial intelligence in general and deep learning in specific. The generative adversarial network GAN was successful in generating high quality samples of natural images. We propose a mo... |
Generalized HaldaneShastry Models as Supersymmetric Partners of the CalogeroSutherland Type Models ; We consider the supersymmetric CalogeroSutherland type Nparticle problems in one dimension and show that the corresponding fermionic part can be identified with the generalized XY models in the presence of an inhomogen... |
On q Component Models on Cayley Tree The General Case ; In the paper we generalize results of paper 12 for a q component models on a Cayley tree of order kgeq 2. We generalize them in two directions 1 from k2 to any kgeq 2; 2 from concrete examples Potts and SOS models of q component models to any q component models w... |
Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models ; We express the mean and variance terms in a double exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the model, and whether they ... |
Modele FBSPPR des objets d'entreprise a la gestion dynamique des connaissances industrielles ; The phases of the life cycle of an industrial product can be described as a network of business processes. Products and informational materials are both raw materials and results of these processes. Modeling using generic m... |
Zero Intelligence Models of the Continuous Double Auction Econometrics, Empirical Evidence and Generalization ; In the paper, a statistical procedure for estimating the parameters of zero intelligence models by means of tickbytick quote L1 data is proposed. A large class of existing zero intelligence models is reviewe... |
Dynamic Entity Representations in Neural Language Models ; Understanding a long document requires tracking how entities are introduced and evolve over time. We present a new type of language model, EntityNLM, that can explicitly model entities, dynamically update their representations, and contextually generate their ... |
A general class of mosaic random fields ; We present a model of a random field on a topological space M that unifies wellknown models such as the Poisson hyperplane tessellation model, the random token model, and the dead leaves model. In addition to generalizing these submodels from mathbbRd to other spaces such as t... |
Generalizations of the Sommerfield and Schwinger models ; The Sommerfield model with a massive vector field coupled to a massless fermion in 11 dimensions is an exactly solvable analog of a BankZaks model. The physics of the model comprises a massive boson and an unparticle sector that survives at low energy as a conf... |
On a generalized Kuramoto model with relativistic effects and emergent dynamics ; We propose a generalized Kuramoto model with relativistic effects and investigate emergent asymptotic behaviors. The proposed generalized Kuramoto model incorporates relativistic KuramotoRK type models which can be derived from the relat... |
Masked Adversarial Generation for Neural Machine Translation ; Attacking Neural Machine Translation models is an inherently combinatorial task on discrete sequences, solved with approximate heuristics. Most methods use the gradient to attack the model on each sample independently. Instead of mechanically applying the ... |
BERTopic Neural topic modeling with a classbased TFIDF procedure ; Topic models can be useful tools to discover latent topics in collections of documents. Recent studies have shown the feasibility of approach topic modeling as a clustering task. We present BERTopic, a topic model that extends this process by extractin... |
LANCE Stresstesting Visual Models by Generating Languageguided Counterfactual Images ; We propose an automated algorithm to stresstest a trained visual model by generating languageguided counterfactual test images LANCE. Our method leverages recent progress in large language modeling and textbased image editing to aug... |
Diluted Generalized Random Energy Model ; We introduce a layered random spin model, equivalent to the Generalized Random Energy Model GREM. In analogy with diluted spin systems, a diluted GREM DGREM is introduced.It can be applied to calculate approximately thermodynamic properties of spin glass models in low dimensio... |
Exact ground state of the generalized threedimensional ShastrySutherland model ; We generalize the ShastrySutherland model to three dimensions. By representing the model as a sum of the semidefinite positive projection operators, we exactly prove that the model has exact dimer ground state. Several schemes for constru... |
Generalized Cubic Model for BaTiO3like Ferroelectric Substance ; We propose an orderdisorder type microscopic model for BaTiO3like Ferroelectric Substance. Our model has three phase transitions and four phases. The symmetry and directions of the polarizations of the ordered phases agree with the experimental results o... |
Duality of a Generalized Gauge Invariant Ising Model on Random Surfaces ; A generalized gauge invariant Ising model on random surfaces with nontrivial topology is proposed and investigated with the dual transformation. It is proved that the model is selfdual in case of a selfdual lattice. In special cases the model re... |
Matrix Models for Beta Ensembles ; This paper constructs tridiagonal random matrix models for general beta0 betaHermite Gaussian and betaLaguerre Wishart ensembles. These generalize the wellknown Gaussian and Wishart models for beta 1,2,4. Furthermore, in the cases of the betaLaguerre ensembles, we eliminate the expo... |
Discrete mechanics a kinematics for a particular case of causal sets ; The model is a particular case of causal set. This is a discrete model of spacetime in a microscopic level. In paper the most general properties of the model are investigated without any reference to a dynamics. The dynamics of the model is introdu... |
Nonrelativistic matter and Dark energy in a quantum conformal model ; We consider a generalization of the standard model which respects quantum conformal invariance. This model leads to identically zero vacuum energy. We show how nonrelativistic matter and dark energy arises in this model. Hence the model is shown to ... |
Computational Models for Multiview Dense Depth Maps of Dynamic Scene ; This paper reviews the recent progresses of the depth map generation for dynamic scene and its corresponding computational models. This paper mainly covers the homogeneous ambiguity models in depth sensing, resolution models in depth processing, an... |
Model Averaging for Generalized Linear Model with Covariates that are Missing completely at Random ; In this paper, we consider the estimation of generalized linear models with covariates that are missing completely at random. We propose a model averaging estimation method and prove that the corresponding model averag... |
The multitrace matrix model An alternative to Connes NCG and IKKT model ; We present a new multitrace matrix model, which is a generalization of the real quartic one matrix model, exhibiting dynamical emergence of a fuzzy twosphere and its noncommutative gauge theory. This provides a novel and a much simpler alternati... |
Supplemental Material Lifelong Generative Modelling Using Dynamic Expansion Graph Model ; In this article, we provide the appendix for Lifelong Generative Modelling Using Dynamic Expansion Graph Model. This appendix includes additional visual results as well as the numerical results on the challenging datasets. In add... |
Investigating Memorization of Conspiracy Theories in Text Generation ; The adoption of natural language generation NLG models can leave individuals vulnerable to the generation of harmful information memorized by the models, such as conspiracy theories. While previous studies examine conspiracy theories in the context... |
Crystal Transformer Selflearning neural language model for Generative and Tinkering Design of Materials ; Selfsupervised neural language models have recently achieved unprecedented success, from natural language processing to learning the languages of biological sequences and organic molecules. These models have demon... |
ToolCoder Teach Code Generation Models to use API search tools ; Automatically generating source code from natural language descriptions has been a growing field of research in recent years. However, current largescale code generation models often encounter difficulties when selecting appropriate APIs for specific con... |
Interactive Fashion Content Generation Using LLMs and Latent Diffusion Models ; Fashionable image generation aims to synthesize images of diverse fashion prevalent around the globe, helping fashion designers in realtime visualization by giving them a basic customized structure of how a specific design preference would... |
VideoControlNet A MotionGuided VideotoVideo Translation Framework by Using Diffusion Model with ControlNet ; Recently, diffusion models like StableDiffusion have achieved impressive image generation results. However, the generation process of such diffusion models is uncontrollable, which makes it hard to generate vid... |
PluGeN MultiLabel Conditional Generation From PreTrained Models ; Modern generative models achieve excellent quality in a variety of tasks including image or text generation and chemical molecule modeling. However, existing methods often lack the essential ability to generate examples with requested properties, such a... |
MolHF A Hierarchical Normalizing Flow for Molecular Graph Generation ; Molecular de novo design is a critical yet challenging task in scientific fields, aiming to design novel molecular structures with desired property profiles. Significant progress has been made by resorting to generative models for graphs. However, ... |
PatternGPT A PatternDriven Framework for Large Language Model Text Generation ; Large language modelsLLMShave shown excellent text generation capabilities, capable of generating fluent humanlike responses for many downstream tasks. However, applying large language models to realworld critical tasks remains challenging... |
Steered Diffusion A Generalized Framework for PlugandPlay Conditional Image Synthesis ; Conditional generative models typically demand large annotated training sets to achieve highquality synthesis. As a result, there has been significant interest in designing models that perform plugandplay generation, i.e., to use a... |
On Hierarchical MultiResolution Graph Generative Models ; In real world domains, most graphs naturally exhibit a hierarchical structure. However, datadriven graph generation is yet to effectively capture such structures. To address this, we propose a novel approach that recursively generates community structures at mu... |
FFPDG Fast, Fair and Private Data Generation ; Generative modeling has been used frequently in synthetic data generation. Fairness and privacy are two big concerns for synthetic data. Although Recent GAN citegoodfellow2014generative based methods show good results in preserving privacy, the generated data may be more ... |
Classical and Quantum Intertwine ; Model interactions between classical and quantum systems are briefly discussed. These include general measurementlike couplings, SternGerlach experiment, model of a counter, quantum Zeno effect, SQUIDtank model. |
Mathematical Model of Shock Waves ; Presented here is the mathematical model describing the phenomenon of shock waves. The underlying concept is based on the timespace model of wave propagation. |
Using BuiltIn DomainSpecific Modeling Support to Guide ModelBased Test Generation ; We present a modelbased testing approach to support automated test generation with domainspecific concepts. This includes a language expert who is an expert at building test models and domain experts who are experts in the domain of th... |
Smoothing parameter and model selection for general smooth models ; This paper discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be present. By constr... |
Modular and Incremental Global Model Management with Extended Generalized Discrimination Networks ; Complex projects developed under the paradigm of modeldriven engineering nowadays often involve several interrelated models, which are automatically processed via a multitude of model operations. Modular and incremental... |
Generative AgentBased Modeling Unveiling Social System Dynamics through Coupling Mechanistic Models with Generative Artificial Intelligence ; We discuss the emerging new opportunity for building feedbackrich computational models of social systems using generative artificial intelligence. Referred to as Generative Agen... |
Tensor models with generalized melonic interactions ; Tensor models are natural generalizations of matrix models. The interactions and observables in the case of unitary invariant models are generalizations of matrix traces. Some notable interactions in the literature include the melonic ones, the tetrahedral one as w... |
A Generative Approach for Mitigating Structural Biases in Natural Language Inference ; Many natural language inference NLI datasets contain biases that allow models to perform well by only using a biased subset of the input, without considering the remainder features. For instance, models are able to make a classifica... |
Learning BodyAware 3D Shape Generative Models ; The shape of many objects in the built environment is dictated by their relationships to the human body how will a person interact with this object Existing datadriven generative models of 3D shapes produce plausible objects but do not reason about the relationship of th... |
PreTrained Neural Language Models for Automatic Mobile App User Feedback Answer Generation ; Studies show that developers' answers to the mobile app users' feedbacks on app stores can increase the apps' star rating. To help app developers generate answers that are related to the users' issues, recent studies develop m... |
Evaluation of Categorical Generative Models Bridging the Gap Between Real and Synthetic Data ; The machine learning community has mainly relied on real data to benchmark algorithms as it provides compelling evidence of model applicability. Evaluation on synthetic datasets can be a powerful tool to provide a better un... |
Spot the fake lungs Generating Synthetic Medical Images using Neural Diffusion Models ; Generative models are becoming popular for the synthesis of medical images. Recently, neural diffusion models have demonstrated the potential to generate photorealistic images of objects. However, their potential to generate medica... |
Textile Pattern Generation Using Diffusion Models ; The problem of textguided image generation is a complex task in Computer Vision, with various applications, including creating visually appealing artwork and realistic product images. One popular solution widely used for this task is the diffusion model, a generative... |
Comparative Assessment of Markov Models and Recurrent Neural Networks for Jazz Music Generation ; As generative models have risen in popularity, a domain that has risen alongside is generative models for music. Our study aims to compare the performance of a simple Markov chain model and a recurrent neural network RNN ... |
ObjectiveReinforced Generative Adversarial Networks ORGAN for Sequence Generation Models ; In unsupervised data generation tasks, besides the generation of a sample based on previous observations, one would often like to give hints to the model in order to bias the generation towards desirable metrics. We propose a me... |
A Deep Generative Framework for Paraphrase Generation ; Paraphrase generation is an important problem in NLP, especially in question answering, information retrieval, information extraction, conversation systems, to name a few. In this paper, we address the problem of generating paraphrases automatically. Our proposed... |
Manifoldvalued Image Generation with Wasserstein Generative Adversarial Nets ; Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversarial Nets WGANs, are studied on manifoldvalued images that are ... |
Generative chemistry drug discovery with deep learning generative models ; The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts,... |
Deformable Generator Network Unsupervised Disentanglement of Appearance and Geometry ; We present a deformable generator model to disentangle the appearance and geometric information for both image and video data in a purely unsupervised manner. The appearance generator network models the information related to appear... |
EnsembleGAN Adversarial Learning for RetrievalGeneration Ensemble Model on ShortText Conversation ; Generating qualitative responses has always been a challenge for humancomputer dialogue systems. Existing dialogue systems generally derive from either retrievalbased or generativebased approaches, both of which have th... |
Interpreting Spatially Infinite Generative Models ; Traditional deep generative models of images and other spatial modalities can only generate fixed sized outputs. The generated images have exactly the same resolution as the training images, which is dictated by the number of layers in the underlying neural network. ... |
Generation of nonstationary stochastic fields using Generative Adversarial Networks ; In the context of generating geological facies conditioned on observed data, samples corresponding to all possible conditions are not generally available in the training set and hence the generation of these realizations depends prim... |
Factual and Informative Review Generation for Explainable Recommendation ; Recent models can generate fluent and grammatical synthetic reviews while accurately predicting user ratings. The generated reviews, expressing users' estimated opinions towards related products, are often viewed as natural language 'rationales... |
MarioGPT OpenEnded Text2Level Generation through Large Language Models ; Procedural Content Generation PCG algorithms provide a technique to generate complex and diverse environments in an automated way. However, while generating content with PCG methods is often straightforward, generating meaningful content that ref... |
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