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CAMPARI CameraAware Decomposed Generative Neural Radiance Fields ; Tremendous progress in deep generative models has led to photorealistic image synthesis. While achieving compelling results, most approaches operate in the twodimensional image domain, ignoring the threedimensional nature of our world. Several recent w... |
OodGAN Generative Adversarial Network for OutofDomain Data Generation ; Detecting an OutofDomain OOD utterance is crucial for a robust dialog system. Most dialog systems are trained on a pool of annotated OOD data to achieve this goal. However, collecting the annotated OOD data for a given domain is an expensive proce... |
A Graph VAE and Graph Transformer Approach to Generating Molecular Graphs ; We propose a combination of a variational autoencoder and a transformer based model which fully utilises graph convolutional and graph pooling layers to operate directly on graphs. The transformer model implements a novel node encoding layer, ... |
A Tunable Model for Graph Generation Using LSTM and Conditional VAE ; With the development of graph applications, generative models for graphs have been more crucial. Classically, stochastic models that generate graphs with a predefined probability of edges and nodes have been studied. Recently, some models that repro... |
DeepCAD A Deep Generative Network for ComputerAided Design Models ; Deep generative models of 3D shapes have received a great deal of research interest. Yet, almost all of them generate discrete shape representations, such as voxels, point clouds, and polygon meshes. We present the first 3D generative model for a dras... |
Learning from Perturbations Diverse and Informative Dialogue Generation with Inverse Adversarial Training ; In this paper, we propose Inverse Adversarial Training IAT algorithm for training neural dialogue systems to avoid generic responses and model dialogue history better. In contrast to standard adversarial trainin... |
Reinforced Generative Adversarial Network for Abstractive Text Summarization ; Sequencetosequence models provide a viable new approach to generative summarization, allowing models that are no longer limited to simply selecting and recombining sentences from the original text. However, these models have three drawbacks... |
Evaluation Metrics for Graph Generative Models Problems, Pitfalls, and Practical Solutions ; Graph generative models are a highly active branch of machine learning. Given the steady development of new models of everincreasing complexity, it is necessary to provide a principled way to evaluate and compare them. In this... |
DualTeacher ClassIncremental Learning With DataFree Generative Replay ; This paper proposes two novel knowledge transfer techniques for classincremental learning CIL. First, we propose datafree generative replay DFGR to mitigate catastrophic forgetting in CIL by using synthetic samples from a generative model. In the ... |
Teach Me What to Say and I Will Learn What to Pick Unsupervised Knowledge Selection Through Response Generation with Pretrained Generative Models ; Knowledge Grounded Conversation Models KGCM are usually based on a selectionretrieval module and a generation module, trained separately or simultaneously, with or without... |
How much do language models copy from their training data Evaluating linguistic novelty in text generation using RAVEN ; Current language models can generate highquality text. Are they simply copying text they have seen before, or have they learned generalizable linguistic abstractions To tease apart these possibiliti... |
Controlling Conditional Language Models without Catastrophic Forgetting ; Machine learning is shifting towards generalpurpose pretrained generative models, trained in a selfsupervised manner on large amounts of data, which can then be applied to solve a large number of tasks. However, due to their generic training met... |
A survey of multimodal deep generative models ; Multimodal learning is a framework for building models that make predictions based on different types of modalities. Important challenges in multimodal learning are the inference of shared representations from arbitrary modalities and crossmodal generation via these repr... |
Analog Bits Generating Discrete Data using Diffusion Models with SelfConditioning ; We present Bit Diffusion a simple and generic approach for generating discrete data with continuous state and continuous time diffusion models. The main idea behind our approach is to first represent the discrete data as binary bits, a... |
Probabilistic Generative Transformer Language models for Generative Design of Molecules ; Selfsupervised neural language models have recently found wide applications in generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional... |
A Generative Approach for ProductionAware Industrial Network Traffic Modeling ; The new wave of digitization induced by Industry 4.0 calls for ubiquitous and reliable connectivity to perform and automate industrial operations. 5G networks can afford the extreme requirements of heterogeneous vertical applications, but ... |
ArchiSound Audio Generation with Diffusion ; The recent surge in popularity of diffusion models for image generation has brought new attention to the potential of these models in other areas of media generation. One area that has yet to be fully explored is the application of diffusion models to audio generation. Audi... |
In What Languages are Generative Language Models the Most Formal Analyzing Formality Distribution across Languages ; Multilingual generative language models LMs are increasingly fluent in a large variety of languages. Trained on the concatenation of corpora in multiple languages, they enable powerful transfer from hig... |
Human Preference Score Better Aligning TexttoImage Models with Human Preference ; Recent years have witnessed a rapid growth of deep generative models, with texttoimage models gaining significant attention from the public. However, existing models often generate images that do not align well with human preferences, su... |
Memory Efficient Diffusion Probabilistic Models via Patchbased Generation ; Diffusion probabilistic models have been successful in generating highquality and diverse images. However, traditional models, whose input and output are highresolution images, suffer from excessive memory requirements, making them less practi... |
XIQE eXplainable Image Quality Evaluation for TexttoImage Generation with Visual Large Language Models ; This paper introduces a novel explainable image quality evaluation approach called XIQE, which leverages visual large language models LLMs to evaluate texttoimage generation methods by generating textual explanatio... |
Improved Visual Story Generation with Adaptive Context Modeling ; Diffusion models developed on top of powerful texttoimage generation models like Stable Diffusion achieve remarkable success in visual story generation. However, the bestperforming approach considers historically generated results as flattened memory ce... |
Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling ; The crystal diffusion variational autoencoder CDVAE is a machine learning model that leverages score matching to generate realistic crystal structures that preserve crystal symmetry. In this study, we leverage no... |
Evaluating the diversity and utility of materials proposed by generative models ; Generative machine learning models can use data generated by scientific modeling to create large quantities of novel material structures. Here, we assess how one stateoftheart generative model, the physicsguided crystal generation model ... |
Applications of generalized special functions in stellar astrophysics ; This article gives an brief outline of the applications of generalized special functions such as generalized hypergeometric functions, Gfunctions and Hfunctions into the general area of nuclear energy generation and reaction rate theory such as th... |
Generic separable metric structures ; We compare three notions of genericity of separable metric structures. Our analysis provides a general model theoretic technique of showing that structures are generic in descriptive set theoretic topological sense and in measure theoretic sense. In particular, it gives a new pe... |
RLDuet Online Music Accompaniment Generation Using Deep Reinforcement Learning ; This paper presents a deep reinforcement learning algorithm for online accompaniment generation, with potential for realtime interactive humanmachine duet improvisation. Different from offline music generation and harmonization, online mu... |
Temporal Generative Adversarial Nets with Singular Value Clipping ; In this paper, we propose a generative model, Temporal Generative Adversarial Nets TGAN, which can learn a semantic representation of unlabeled videos, and is capable of generating videos. Unlike existing Generative Adversarial Nets GANbased methods t... |
GAGAN GeometryAware Generative Adversarial Networks ; Deep generative models learned through adversarial training have become increasingly popular for their ability to generate naturalistic image textures. However, aside from their texture, the visual appearance of objects is significantly influenced by their shape ge... |
Recurrent Deconvolutional Generative Adversarial Networks with Application to Text Guided Video Generation ; This paper proposes a novel model for video generation and especially makes the attempt to deal with the problem of video generation from text descriptions, i.e., synthesizing realistic videos conditioned on gi... |
Privacypreserving Spatiotemporal Scenario Generation of Renewable Energies A Federated Deep Generative Learning Approach ; Scenario generation is a fundamental and crucial tool for decisionmaking in power systems with highpenetration renewables. Based on big historical data, a novel federated deep generative learning ... |
Toward Spatially Unbiased Generative Models ; Recent image generation models show remarkable generation performance. However, they mirror strong location preference in datasets, which we call spatial bias. Therefore, generators render poor samples at unseen locations and scales. We argue that the generators rely on th... |
PAGER Progressive AttributeGuided Extendable Robust Image Generation ; This work presents a generative modeling approach based on successive subspace learning SSL. Unlike most generative models in the literature, our method does not utilize neural networks to analyze the underlying source distribution and synthesize i... |
A General Formulation for Evaluating the Performance of Linear Power Flow Models ; Linear power flow LPF models are essential in power system analysis. Various LPF models are proposed, but some crucial questions are still remained what is the performance bound e.g., the error bound of LPF models, how to know a branch ... |
CAMERO Consistency Regularized Ensemble of Perturbed Language Models with Weight Sharing ; Model ensemble is a popular approach to produce a lowvariance and wellgeneralized model. However, it induces large memory and inference costs, which are often not affordable for realworld deployment. Existing work has resorted t... |
SUnotimes U1c gauge models with spontaneous symmetry breaking ; A possible generalization of the technique of the standard model to SUnotimes U1 gauge models is proposed. A special Higgs mechanism and a new kind of Yukawa couplings in unitary gauge are introduced. These allow us to obtain a general method of deriving ... |
Higgs Boson Mass Bounds in the Standard and Minimal Supersymmetric Standard Model with Four Generations ; We study the question of distinguishability of the Higgs sector between the standard model with four generationsSM4 and the minimal supersymmetric standard model with four generations MSSM4. We find that a gap exi... |
Multiloop correlators for rational theories of 2D gravity from the generalized Kontsevich models ; We introduce a parametrization of the coupling constant space of the generalized Kontsevich models in terms of a set of moments equivalent to those introduced recently in the context of topological gravity. For the simpl... |
The distribution of Pearson residuals in generalized linear models ; In general, the distribution of residuals cannot be obtained explicitly. We give an asymptotic formula for the density of Pearson residuals in continuous generalized linear models corrected to order n1, where n is the sample size. We define corrected... |
Random Sequential Generation of Intervals for the Cascade Model of Food Webs ; The cascade model generates a food web at random. In it the species are labeled from 0 to m, and arcs are given at random between pairs of the species. For an arc with endpoints i and j ij, the species i is eaten by the species labeled j. T... |
The Ising Model on Random Lattices in Arbitrary Dimensions ; We study analytically the Ising model coupled to random lattices in dimension three and higher. The family of random lattices we use is generated by the large N limit of a colored tensor model generalizing the twomatrix model for Ising spins on random surfac... |
Generalized preferential attachment tunable powerlaw degree distribution and clustering coefficient ; We propose a wide class of preferential attachment models of random graphs, generalizing previous approaches. Graphs described by these models obey the powerlaw degree distribution, with the exponent that can be contr... |
Dynamic Term Structure Modelling with Default and Mortality Risk New Results on Existence and Monotonicity ; This paper considers general term structure models like the ones appearing in portfolio credit risk modelling or life insurance. We give a general model starting from families of forward rates driven by infinit... |
Generalized supersymmetry and sigma models ; In this paper, we discuss the generalizations of exact supersymmetries present in the supersymmetrized sigma models. These generalizations are made by making the supersymmetric transformation parameter fielddependent. Remarkably, the supersymmetric effective actions emerge ... |
Inflationary Weak Anisotropic Model with General Dissipation Coefficient ; This paper explores the dynamics of warm intermediate and logamediate inflationary models during weak dissipative regime with a general form of dissipative coefficient. We analyze these models within the framework of locally rotationally symmet... |
Generative Modeling with Conditional Autoencoders Building an Integrated Cell ; We present a conditional generative model to learn variation in cell and nuclear morphology and the location of subcellular structures from microscopy images. Our model generalizes to a wide range of subcellular localization and allows for... |
Combining Generative and Discriminative Approaches to Unsupervised Dependency Parsing via Dual Decomposition ; Unsupervised dependency parsing aims to learn a dependency parser from unannotated sentences. Existing work focuses on either learning generative models using the expectationmaximization algorithm and its var... |
A Deep Ensemble Model with Slot Alignment for SequencetoSequence Natural Language Generation ; Natural language generation lies at the core of generative dialogue systems and conversational agents. We describe an ensemble neural language generator, and present several novel methods for data representation and augmenta... |
Signature change in loop quantum gravity General midisuperspace models and dilaton gravity ; Models of loop quantum gravity based on real connections have a deformed notion of general covariance, which leads to the phenomenon of signature change. This result is confirmed here in a general analysis of all midisuperspac... |
Comparative Study on Generative Adversarial Networks ; In recent years, there have been tremendous advancements in the field of machine learning. These advancements have been made through both academic as well as industrial research. Lately, a fair amount of research has been dedicated to the usage of generative model... |
Deterministic NonAutoregressive Neural Sequence Modeling by Iterative Refinement ; We propose a conditional nonautoregressive neural sequence model based on iterative refinement. The proposed model is designed based on the principles of latent variable models and denoising autoencoders, and is generally applicable to ... |
A NonParametric Test to Detect DataCopying in Generative Models ; Detecting overfitting in generative models is an important challenge in machine learning. In this work, we formalize a form of overfitting that we call emdatacopying where the generative model memorizes and outputs training samples or small variations ... |
General Bayesian L2 calibration of mathematical models ; A mathematical model is a representation of a physical system depending on unknown parameters. Calibration refers to attributing values to these parameters, using observations of the physical system, acknowledging that the mathematical model is an inexact repres... |
Generating Related Work ; Communicating new research ideas involves highlighting similarities and differences with past work. Authors write fluent, often long sections to survey the distinction of a new paper with related work. In this work we model generating related work sections while being cognisant of the motivat... |
ZeroShot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization ; One of the main challenges of the machine reading comprehension MRC models is their fragile outofdomain generalization, which makes these models not properly applicable to realworld generalpurpo... |
Magnetic black holes with generalized ModMax model of nonlinear electrodynamics ; Recently Bandos, Lechner, Sorokin, and Townsend Phys. Rev. D textbf102, 121703 2020 proposed Modified Maxwell ModMax model of nonlinear dualityinvariant conformal electrodynamics. Here, Generalized ModMax GenModMax model of nonlinear ele... |
Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models ; Proteins are macromolecules that mediate a significant fraction of the cellular processes that underlie life. An important task in bioengineering is designing proteins with specific 3D structures and chemical properti... |
Structural generalization is hard for sequencetosequence models ; Sequencetosequence seq2seq models have been successful across many NLP tasks, including ones that require predicting linguistic structure. However, recent work on compositional generalization has shown that seq2seq models achieve very low accuracy in ge... |
Counterfactual Identifiability of Bijective Causal Models ; We study counterfactual identifiability in causal models with bijective generation mechanisms BGM, a class that generalizes several widelyused causal models in the literature. We establish their counterfactual identifiability for three common causal structure... |
Generative Models for 3D Point Clouds ; Point clouds are rich geometric data structures, where their three dimensional structure offers an excellent domain for understanding the representation learning and generative modeling in 3D space. In this work, we aim to improve the performance of point cloud latentspace gener... |
Calliffusion Chinese Calligraphy Generation and Style Transfer with Diffusion Modeling ; In this paper, we propose Calliffusion, a system for generating highquality Chinese calligraphy using diffusion models. Our model architecture is based on DDPM Denoising Diffusion Probabilistic Models, and it is capable of generat... |
Anomaly Detection in Networks via ScoreBased Generative Models ; Node outlier detection in attributed graphs is a challenging problem for which there is no method that would work well across different datasets. Motivated by the stateoftheart results of scorebased models in graph generative modeling, we propose to inco... |
A New Algorithm for Doptimal Designs under General Parametric Statistical Models with Mixed Factors ; In this paper, we consider experiments involving both discrete factors and continuous factors under general parametric statistical models. To search for optimal designs under the Dcriterion, we propose a new algorithm... |
TDG Textguided Domain Generalization ; Domain generalization DG attempts to generalize a model trained on single or multiple source domains to the unseen target domain. Benefiting from the success of VisualandLanguage Pretrained models in recent years, we argue that it is crucial for domain generalization by introduci... |
Towards Product Lining ModelDriven Development Code Generators ; A code generator systematically transforms compact models to detailed code. Today, code generation is regarded as an integral part of modeldriven development MDD. Despite its relevance, the development of code generators is an inherently complex task and... |
Fast Adaptation in Generative Models with Generative Matching Networks ; Despite recent advances, the remaining bottlenecks in deep generative models are necessity of extensive training and difficulties with generalization from small number of training examples. We develop a new generative model called Generative Matc... |
Learning to Generate TimeLapse Videos Using MultiStage Dynamic Generative Adversarial Networks ; Taking a photo outside, can we predict the immediate future, e.g., how would the cloud move in the sky We address this problem by presenting a generative adversarial network GAN based twostage approach to generating realis... |
Model Selection for Generalized Zeroshot Learning ; In the problem of generalized zeroshot learning, the datapoints from unknown classes are not available during training. The main challenge for generalized zeroshot learning is the unbalanced data distribution which makes it hard for the classifier to distinguish if a... |
Generating Multiple Diverse Responses for ShortText Conversation ; Neural generative models have become popular and achieved promising performance on shorttext conversation tasks. They are generally trained to build a 1to1 mapping from the input post to its output response. However, a given post is often associated wi... |
Factorized Deep Generative Models for Trajectory Generation with SpatiotemporalValidity Constraints ; Trajectory data generation is an important domain that characterizes the generative process of mobility data. Traditional methods heavily rely on predefined heuristics and distributions and are weak in learning unknow... |
Language Generation with MultiHop Reasoning on Commonsense Knowledge Graph ; Despite the success of generative pretrained language models on a series of text generation tasks, they still suffer in cases where reasoning over underlying commonsense knowledge is required during generation. Existing approaches that integr... |
Graphbased Multihop Reasoning for Long Text Generation ; Long text generation is an important but challenging task.The main problem lies in learning sentencelevel semantic dependencies which traditional generative models often suffer from. To address this problem, we propose a Multihop Reasoning Generation MRG approac... |
Towards Diverse Paraphrase Generation Using MultiClass Wasserstein GAN ; Paraphrase generation is an important and challenging natural language processing NLP task. In this work, we propose a deep generative model to generate paraphrase with diversity. Our model is based on an encoderdecoder architecture. An additiona... |
Multipleobjective Reinforcement Learning for Inverse Design and Identification ; The aim of the inverse chemical design is to develop new molecules with given optimized molecular properties or objectives. Recently, generative deep learning DL networks are considered as the stateoftheart in inverse chemical design and ... |
Adaptive Parameterization for Neural Dialogue Generation ; Neural conversation systems generate responses based on the sequencetosequence SEQ2SEQ paradigm. Typically, the model is equipped with a single set of learned parameters to generate responses for given input contexts. When confronting diverse conversations, it... |
AffectON Incorporating Affect Into Dialog Generation ; Due to its expressivity, natural language is paramount for explicit and implicit affective state communication among humans. The same linguistic inquiry e.g., How are you might induce responses with different affects depending on the affective state of the convers... |
Enhance Convolutional Neural Networks with Noise Incentive Block ; As a generic modeling tool, Convolutional Neural Networks CNNs have been widely employed in image generation and translation tasks. However, when fed with a flat input, current CNN models may fail to generate vivid results due to the spatially shared c... |
The Perils of Using Mechanical Turk to Evaluate OpenEnded Text Generation ; Recent text generation research has increasingly focused on openended domains such as story and poetry generation. Because models built for such tasks are difficult to evaluate automatically, most researchers in the space justify their modelin... |
Generating Multivariate Load States Using a Conditional Variational Autoencoder ; For planning of power systems and for the calibration of operational tools, it is essential to analyse system performance in a large range of representative scenarios. When the available historical data is limited, generative models are ... |
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM ; This work proposes a method for using any generator network as the foundation of an EnergyBased Model EBM. Our formulation posits that observed images are the sum of unobserved latent variables passed through the generator network and a residual... |
Graph Generation with DestinationPredicting Diffusion Mixture ; Generation of graphs is a major challenge for realworld tasks that require understanding the complex nature of their nonEuclidean structures. Although diffusion models have achieved notable success in graph generation recently, they are illsuited for mode... |
Coincidental Generation ; Generative A.I. models have emerged as versatile tools across diverse industries, with applications in privacypreserving data sharing, computational art, personalization of products and services, and immersive entertainment. Here, we introduce a new privacy concern in the adoption and use of ... |
Ambigram Generation by A Diffusion Model ; Ambigrams are graphical letter designs that can be read not only from the original direction but also from a rotated direction especially with 180 degrees. Designing ambigrams is difficult even for human experts because keeping their dual readability from both directions is o... |
JEN1 TextGuided Universal Music Generation with Omnidirectional Diffusion Models ; Music generation has attracted growing interest with the advancement of deep generative models. However, generating music conditioned on textual descriptions, known as texttomusic, remains challenging due to the complexity of musical st... |
A Model of HotSector Generations ; Possible existence of hotsector generations above the well known 3 generation bound is investigated on the basis of a model of leptons and quarks, which is based on the Harari and Shupe's one. Our model predicts the existence of bf3 1 generations above the ordinary coldsector 3 gene... |
A single model of traversable wormholes supported by generalized phantom energy or Chaplygin gas ; This paper discusses a new variable equation of state parameter leading to exact solutions of the Einstein field equations describing traversable wormholes. In addition to generalizing the notion of phantom energy, the e... |
The Probabilistic Model of Keys Generation of QKD Systems ; The probabilistic model of keys generation of QKD systems is proposed. The model includes all phases of keys generation starting from photons generation to states detection taking characteristics of fiberoptics components into account. The paper describes the... |
Statefinder Description in Generalized Holographic and Ricci Dark Energy Models ; We have considered the generalized holographic and generalized Ricci dark energy models for acceleration of the universe. If the universe filled with only GHDEGRDE the corresponding decel eration parameter, EOS parameter and statefinder ... |
Statefinder Diagnostic for Dark Energy Models in Bianchi I Universe ; In this paper, we investigate the statefinder, the deceleration and equation of state parameters when universe is composed of generalized holographic dark energy or generalized Ricci dark energy for Bianchi I universe model. These parameters are fou... |
Cross Domain Image Generation through Latent Space Exploration with Adversarial Loss ; Conditional domain generation is a good way to interactively control sample generation process of deep generative models. However, once a conditional generative model has been created, it is often expensive to allow it to adapt to n... |
SO32 heterotic standard model vacua in general CalabiYau compactifications ; We study a direct flux breaking scenario in SO32 heterotic string theory on general CalabiYau threefolds. The direct flux breaking, corresponding to hypercharge flux breaking in the Ftheory context, allows us to derive the Standard Model in g... |
Unpriortized Autoencoder For Image Generation ; In this paper, we treat the image generation task using an autoencoder, a representative latent model. Unlike many studies regularizing the latent variable's distribution by assuming a manually specified prior, we approach the image generation task using an autoencoder b... |
GraphNVP An Invertible Flow Model for Generating Molecular Graphs ; We propose GraphNVP, the first invertible, normalizing flowbased molecular graph generation model. We decompose the generation of a graph into two steps generation of i an adjacency tensor and ii node attributes. This decomposition yields the exact li... |
SemiImplicit Generative Model ; To combine explicit and implicit generative models, we introduce semiimplicit generator SIG as a flexible hierarchical model that can be trained in the maximum likelihood framework. Both theoretically and experimentally, we demonstrate that SIG can generate high quality samples especial... |
Towards an Accurate Mathematical Model of Generic NominallyTyped OOP ; The construction of GNOOP as a domaintheoretic model of generic nominallytyped OOP is currently underway. This extended abstract presents the concepts of nominal intervals' and full generication' that are likely to help in building GNOOP as an accu... |
Correlated discrete data generation using adversarial training ; Generative Adversarial Networks GAN have shown great promise in tasks like synthetic image generation, image inpainting, style transfer, and anomaly detection. However, generating discrete data is a challenge. This work presents an adversarial training b... |
Measuring Fairness in Generative Models ; Deep generative models have made much progress in improving training stability and quality of generated data. Recently there has been increased interest in the fairness of deepgenerated data. Fairness is important in many applications, e.g. law enforcement, as biases will affe... |
Selective Sampling and Mixture Models in Generative Adversarial Networks ; In this paper, we propose a multigenerator extension to the adversarial training framework, in which the objective of each generator is to represent a unique component of a target mixture distribution. In the training phase, the generators coop... |
Texygen A Benchmarking Platform for Text Generation Models ; We introduce Texygen, a benchmarking platform to support research on opendomain text generation models. Texygen has not only implemented a majority of text generation models, but also covered a set of metrics that evaluate the diversity, the quality and the ... |
Generative Models for Pose Transfer ; We investigate nearest neighbor and generative models for transferring pose between persons. We take in a video of one person performing a sequence of actions and attempt to generate a video of another person performing the same actions. Our generative model pix2pix outperforms kN... |
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