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Wasserstein Index Generation Model Automatic Generation of Timeseries Index with Application to Economic Policy Uncertainty ; I propose a novel method, the Wasserstein Index Generation model WIG, to generate a public sentiment index automatically. To test the models effectiveness, an application to generate Economic P... |
Potential Flow Generator with L2 Optimal Transport Regularity for Generative Models ; We propose a potential flow generator with L2 optimal transport regularity, which can be easily integrated into a wide range of generative models including different versions of GANs and flowbased models. We show the correctness and ... |
Creative GANs for generating poems, lyrics, and metaphors ; Generative models for text have substantially contributed to tasks like machine translation and language modeling, using maximum likelihood optimization MLE. However, for creative text generation, where multiple outputs are possible and originality and unique... |
Generative Flows with Matrix Exponential ; Generative flows models enjoy the properties of tractable exact likelihood and efficient sampling, which are composed of a sequence of invertible functions. In this paper, we incorporate matrix exponential into generative flows. Matrix exponential is a map from matrices to in... |
Noncommutative Yang model and its generalizations ; Long time ago, C.N. Yang proposed a model of noncommutative spacetime that generalized the Snyder model to a curved background. In this paper we review his proposal and the generalizations that have been suggested during the years. In particular, we discuss the most ... |
Causally Disentangled Generative Variational AutoEncoder ; We propose a new supervised learning method for Variational AutoEncoder VAE which has a causally disentangled representation and achieves the causally disentangled generation CDG simultaneously. In this paper, CDG is defined as a generative model able to decod... |
GRM Generative Relevance Modeling Using RelevanceAware Sample Estimation for Document Retrieval ; Recent studies show that Generative Relevance Feedback GRF, using text generated by Large Language Models LLMs, can enhance the effectiveness of query expansion. However, LLMs can generate irrelevant information that harm... |
DaST Datafree Substitute Training for Adversarial Attacks ; Machine learning models are vulnerable to adversarial examples. For the blackbox setting, current substitute attacks need pretrained models to generate adversarial examples. However, pretrained models are hard to obtain in realworld tasks. In this paper, we p... |
Plan To Predict Learning an UncertaintyForeseeing Model for ModelBased Reinforcement Learning ; In Modelbased Reinforcement Learning MBRL, model learning is critical since an inaccurate model can bias policy learning via generating misleading samples. However, learning an accurate model can be difficult since the poli... |
Toy amphiphiles on the computer What can we learn from generic models ; Generic coarsegrained models are designed such that they are i simple and ii computationally efficient. They do not aim at representing particular materials, but classes of materials, hence they can offer insight into universal properties of these... |
Support and Plausibility Degrees in Generalized Functional Models ; By discussing several examples, the theory of generalized functional models is shown to be very natural for modeling some situations of reasoning under uncertainty. A generalized functional model is a pair f, P where f is a function describing the int... |
Effect of anisotropy on generalized Chaplygin gas scalar field and its interaction with other dark energy models ; In this work, we establish a correspondence between the interacting holographic, new agegraphic dark energy and generalized Chaplygin gas model in Bianchi type I universe. In continue, we reconstruct the ... |
Generative and Discriminative Text Classification with Recurrent Neural Networks ; We empirically characterize the performance of discriminative and generative LSTM models for text classification. We find that although RNNbased generative models are more powerful than their bagofwords ancestors e.g., they account for ... |
Learning EnergyBased Models as Generative ConvNets via Multigrid Modeling and Sampling ; This paper proposes a multigrid method for learning energybased generative ConvNet models of images. For each grid, we learn an energybased probabilistic model where the energy function is defined by a bottomup convolutional neura... |
On generalized residue network for deep learning of unknown dynamical systems ; We present a general numerical approach for learning unknown dynamical systems using deep neural networks DNNs. Our method is built upon recent studies that identified the residue network ResNet as an effective neural network structure. In... |
A Classifying Variational Autoencoder with Application to Polyphonic Music Generation ; The variational autoencoder VAE is a popular probabilistic generative model. However, one shortcoming of VAEs is that the latent variables cannot be discrete, which makes it difficult to generate data from different modes of a dist... |
Permutation Invariant Graph Generation via ScoreBased Generative Modeling ; Learning generative models for graphstructured data is challenging because graphs are discrete, combinatorial, and the underlying data distribution is invariant to the ordering of nodes. However, most of the existing generative models for grap... |
Discovering Generative Models from Event Logs Datadriven Simulation vs Deep Learning ; A generative model is a statistical model that is able to generate new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical trac... |
Randomwalk Based Generative Model for Classifying Document Networks ; Document networks are found in various collections of realworld data, such as citation networks, hyperlinked web pages, and online social networks. A large number of generative models have been proposed because they offer intuitive and useful pictur... |
Discrete Point Flow Networks for Efficient Point Cloud Generation ; Generative models have proven effective at modeling 3D shapes and their statistical variations. In this paper we investigate their application to point clouds, a 3D shape representation widely used in computer vision for which, however, only few gener... |
Learning Contextual Representations for Semantic Parsing with GenerationAugmented PreTraining ; Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with selfsupervised learning obje... |
General Robot Dynamics Learning and Gen2Real ; Acquiring dynamics is an essential topic in robot learning, but uptodate methods, such as dynamics randomization, need to restart to check nominal parameters, generate simulation data, and train networks whenever they face different robots. To improve it, we novelly inves... |
Meta Internal Learning ; Internal learning for singleimage generation is a framework, where a generator is trained to produce novel images based on a single image. Since these models are trained on a single image, they are limited in their scale and application. To overcome these issues, we propose a metalearning appr... |
Improving Nonautoregressive Generation with Mixup Training ; While pretrained language models have achieved great success on various natural language understanding tasks, how to effectively leverage them into nonautoregressive generation tasks remains a challenge. To solve this problem, we present a nonautoregressive ... |
De Novo Molecular Generation with Stacked Adversarial Model ; Generating novel drug molecules with desired biological properties is a time consuming and complex task. Conditional generative adversarial models have recently been proposed as promising approaches for de novo drug design. In this paper, we propose a new g... |
Multilingual Generative Language Models for ZeroShot CrossLingual Event Argument Extraction ; We present a study on leveraging multilingual pretrained generative language models for zeroshot crosslingual event argument extraction EAE. By formulating EAE as a language generation task, our method effectively encodes eve... |
Mix and Match Learningfree Controllable Text Generation using Energy Language Models ; Recent work on controlled text generation has either required attributebased finetuning of the base language model LM, or has restricted the parameterization of the attribute discriminator to be compatible with the base autoregressi... |
Temporal Domain Generalization with DriftAware Dynamic Neural Networks ; Temporal domain generalization is a promising yet extremely challenging area where the goal is to learn models under temporally changing data distributions and generalize to unseen data distributions following the trends of the change. The advanc... |
Applying Regularized SchrodingerBridgeBased Stochastic Process in Generative Modeling ; Compared to the existing functionbased models in deep generative modeling, the recently proposed diffusion models have achieved outstanding performance with a stochasticprocessbased approach. But a long sampling time is required fo... |
Digital twins for city simulation Automatic, efficient, and robust mesh generation for largescale city modeling and simulation ; The concept of creating digital twins, connected digital models of physical systems, is gaining increasing attention for modeling and simulation of whole cities. The basis for building a dig... |
Leveraging Pretrained Models for Failure Analysis Triplets Generation ; Pretrained Language Models recently gained traction in the Natural Language Processing NLP domain for text summarization, generation and questionanswering tasks. This stems from the innovation introduced in Transformer models and their overwhelmin... |
Fast Graph Generation via Spectral Diffusion ; Generating graphstructured data is a challenging problem, which requires learning the underlying distribution of graphs. Various models such as graph VAE, graph GANs, and graph diffusion models have been proposed to generate meaningful and reliable graphs, among which the... |
Tensor Formulation of the General Linear Model with Einstein Notation ; The general linear model is a universally accepted method to conduct and test multiple linear regression models. Using this model one has the ability to simultaneously regress covariates among different groups of data. Moreover, there are hundreds... |
Geometric Latent Diffusion Models for 3D Molecule Generation ; Generative models, especially diffusion models DMs, have achieved promising results for generating featurerich geometries and advancing foundational science problems such as molecule design. Inspired by the recent huge success of Stable latent Diffusion mo... |
PoET A generative model of protein families as sequencesofsequences ; Generative protein language models are a natural way to design new proteins with desired functions. However, current models are either difficult to direct to produce a protein from a specific family of interest, or must be trained on a large multipl... |
Generative Prompt Model for Weakly Supervised Object Localization ; Weakly supervised object localization WSOL remains challenging when learning object localization models from image category labels. Conventional methods that discriminatively train activation models ignore representative yet less discriminative object... |
Generative Visual Question Answering ; Multimodal tasks involving vision and language in deep learning continue to rise in popularity and are leading to the development of newer models that can generalize beyond the extent of their training data. The current models lack temporal generalization which enables models to ... |
The FiveDollar Model Generating Game Maps and Sprites from Sentence Embeddings ; The fivedollar model is a lightweight texttoimage generative architecture that generates low dimensional images from an encoded text prompt. This model can successfully generate accurate and aesthetically pleasing content in low dimension... |
An Autoethnographic Exploration of XAI in Algorithmic Composition ; Machine Learning models are capable of generating complex music across a range of genres from folk to classical music. However, current generative music AI models are typically difficult to understand and control in meaningful ways. Whilst research ha... |
Atombyatom protein generation and beyond with language models ; Protein language models learn powerful representations directly from sequences of amino acids. However, they are constrained to generate proteins with only the set of amino acids represented in their vocabulary. In contrast, chemical language models learn... |
Generative Design of Hardwareaware DNNs ; To efficiently run DNNs on the edgecloud, many new DNN inference accelerators are being designed and deployed frequently. To enhance the resource efficiency of DNNs, model quantization is a widelyused approach. However, different acceleratorHW has different resources leading t... |
Recipe Generation from Unsegmented Cooking Videos ; This paper tackles recipe generation from unsegmented cooking videos, a task that requires agents to 1 extract key events in completing the dish and 2 generate sentences for the extracted events. Our task is similar to dense video captioning DVC, which aims at detect... |
DomainStudio FineTuning Diffusion Models for DomainDriven Image Generation using Limited Data ; Denoising diffusion probabilistic models DDPMs have been proven capable of synthesizing highquality images with remarkable diversity when trained on large amounts of data. Typical diffusion models and modern largescale cond... |
Generative Modeling by Inclusive Neural Random Fields with Applications in Image Generation and Anomaly Detection ; Neural random fields NRFs, referring to a class of generative models that use neural networks to implement potential functions in random fields a.k.a. energybased models, are not new but receive less att... |
Learning Diverse Stochastic HumanAction Generators by Learning Smooth Latent Transitions ; Humanmotion generation is a longstanding challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN to directly model transitions ... |
Game of Learning Bloch Equation Simulations for MR Fingerprinting ; Purpose This work proposes a novel approach to efficiently generate MR fingerprints for MR fingerprinting MRF problems based on the unsupervised deep learning model generative adversarial networks GAN. Methods The GAN model is adopted and modified for... |
GenMod A generative modeling approach for spectral representation of PDEs with random inputs ; We propose a method for quantifying uncertainty in highdimensional PDE systems with random parameters, where the number of solution evaluations is small. Parametric PDE solutions are often approximated using a spectral decom... |
JaCoText A Pretrained Model for Java CodeText Generation ; Pretrained transformerbased models have shown high performance in natural language generation task. However, a new wave of interest has surged automatic programming language generation. This task consists of translating natural language instructions to a progr... |
GMValuator Similaritybased Data Valuation for Generative Models ; Data valuation plays a crucial role in machine learning. Existing data valuation methods have primarily focused on discriminative models, neglecting generative models that have recently gained considerable attention. A very few existing attempts of data... |
Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation ; Generative models have had a profound impact on vision and language, paving the way for a new era of multimodal generative applications. While these successes have inspired researchers to explore using generative models in sci... |
Stable phantomdivide crossing in two scalar models with matter ; We construct cosmological models with two scalar fields, which has the structure as in the ghost condensation model or kessence model. The models can describe the stable phantom crossing, which should be contrasted with one scalar tensor models, where th... |
Model Selection in HighDimensional Misspecified Models ; Model selection is indispensable to highdimensional sparse modeling in selecting the best set of covariates among a sequence of candidate models. Most existing work assumes implicitly that the model is correctly specified or of fixed dimensions. Yet model misspe... |
On the Equivalence of Generative and Discriminative Formulations of the Sequential Dependence Model ; The sequential dependence model SDM is a popular retrieval model which is based on the theory of probabilistic graphical models. While it was originally introduced by Metzler and Croft as a Markov Random Field aka dis... |
Generalized partially linear models on Riemannian manifolds ; The generalized partially linear models on Riemannian manifolds are introduced. These models, like ordinary generalized linear models, are a generalization of partially linear models on Riemannian manifolds that allow for response variables with error distr... |
NeurallyGuided Procedural Models Amortized Inference for Procedural Graphics Programs using Neural Networks ; Probabilistic inference algorithms such as Sequential Monte Carlo SMC provide powerful tools for constraining procedural models in computer graphics, but they require many samples to produce desirable results.... |
Mean squared displacement in a generalized Levy walk model ; L'evy walks represent a class of stochastic models spacetime coupled continuous time random walks with applications ranging from the laser cooling to the description of animal motion. The initial model was intended for the description of turbulent dispersion... |
Deep Generative Models for Reject Inference in Credit Scoring ; Credit scoring models based on accepted applications may be biased and their consequences can have a statistical and economic impact. Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications. In this ... |
Improving Variational Autoencoder for Text Modelling with TimestepWise Regularisation ; The Variational Autoencoder VAE is a popular and powerful model applied to text modelling to generate diverse sentences. However, an issue known as posterior collapse or KL loss vanishing happens when the VAE is used in text modell... |
Kessence Lagrangians of polytropic and logotropic unified dark matter and dark energy models ; We determine the kessence Lagrangian of a relativistic barotropic fluid. The equation of state of the fluid can be specified in different manners depending on whether the pressure is expressed in terms of the energy density ... |
Pay Attention Accuracy Versus Interpretability Tradeoff in Finetuned Diffusion Models ; The recent progress of diffusion models in terms of image quality has led to a major shift in research related to generative models. Current approaches often finetune pretrained foundation models using domainspecific texttoimage pa... |
Explore and Exploit the Diverse Knowledge in Model Zoo for Domain Generalization ; The proliferation of pretrained models, as a result of advancements in pretraining techniques, has led to the emergence of a vast zoo of publicly available models. Effectively utilizing these resources to obtain models with robust outof... |
Research on an improved Conformer endtoend Speech Recognition Model with RDrop Structure ; To address the issue of poor generalization ability in endtoend speech recognition models within deep learning, this study proposes a new Conformerbased speech recognition model called ConformerR that incorporates the Rdrop stru... |
Image Generation and Translation with Disentangled Representations ; Generative models have made significant progress in the tasks of modeling complex data distributions such as natural images. The introduction of Generative Adversarial Networks GANs and autoencoders lead to the possibility of training on big data set... |
Improving Model Compatibility of Generative Adversarial Networks by Boundary Calibration ; Generative Adversarial Networks GANs is a powerful family of models that learn an underlying distribution to generate synthetic data. Many existing studies of GANs focus on improving the realness of the generated image data for ... |
Fragmentbased molecular generative model with high generalization ability and synthetic accessibility ; Deep generative models are attracting great attention for molecular design with desired properties. Most existing models generate molecules by sequentially adding atoms. This often renders generated molecules with l... |
Plug and Play Counterfactual Text Generation for Model Robustness ; Generating counterfactual testcases is an important backbone for testing NLP models and making them as robust and reliable as traditional software. In generating the testcases, a desired property is the ability to control the testcase generation in a ... |
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning ; Controlled automated story generation seeks to generate natural language stories satisfying constraints from natural language critiques or preferences. Existing methods to control for story preference utilize prompt engineering which ... |
ChatGPT or Human Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPTgenerated Text ; ChatGPT has the ability to generate grammatically flawless and seeminglyhuman replies to different types of questions from various domains. The number of its users and of its applications is ... |
StyleAvatar3D Leveraging ImageText Diffusion Models for HighFidelity 3D Avatar Generation ; The recent advancements in imagetext diffusion models have stimulated research interest in largescale 3D generative models. Nevertheless, the limited availability of diverse 3D resources presents significant challenges to learn... |
Bias Assessment and Mitigation in LLMbased Code Generation ; Utilizing stateoftheart Large Language Models LLMs, automatic code generation models play a pivotal role in enhancing the productivity and efficiency of software development coding procedures. As the adoption of LLMs becomes more widespread in software codin... |
Multiscale sequence modeling with a learned dictionary ; We propose a generalization of neural network sequence models. Instead of predicting one symbol at a time, our multiscale model makes predictions over multiple, potentially overlapping multisymbol tokens. A variation of the bytepair encoding BPE compression algo... |
Model Complexity of Deep Learning A Survey ; Model complexity is a fundamental problem in deep learning. In this paper we conduct a systematic overview of the latest studies on model complexity in deep learning. Model complexity of deep learning can be categorized into expressive capacity and effective model complexit... |
Rethinking the Knowledge Distillation From the Perspective of Model Calibration ; Recent years have witnessed dramatically improvements in the knowledge distillation, which can generate a compact student model for better efficiency while retaining the model effectiveness of the teacher model. Previous studies find tha... |
The matrix model for dessins d'enfants ; We present the matrix models that are the generating functions for branched covers of the complex projective line ramified over 0, 1, and infty Grotendieck's dessins d'enfants of fixed genus, degree, and the ramification profile at infinity. For general ramifications at other p... |
On the Discrepancy between Density Estimation and Sequence Generation ; Many sequencetosequence generation tasks, including machine translation and texttospeech, can be posed as estimating the density of the output y given the input x pyx. Given this interpretation, it is natural to evaluate sequencetosequence models ... |
Relieve the H0 tension with a new coupled generalized threeform dark energy model ; In this work we propose a new coupled generalized threeform dark energy model, in which dark energy are represented by a threeform field and other components are represented by ideal fluids. We first perform a dynamical analysis on the... |
A Tree Adjoining Grammar Representation for Models Of Stochastic Dynamical Systems ; Model structure and complexity selection remains a challenging problem in system identification, especially for parametric nonlinear models. Many Evolutionary Algorithm EA based methods have been proposed in the literature for estimat... |
Maximum Entropy Model Rollouts Fast Model Based Policy Optimization without Compounding Errors ; Model usage is the central challenge of modelbased reinforcement learning. Although dynamics model based on deep neural networks provide good generalization for single step prediction, such ability is over exploited when i... |
Model Extraction and Defenses on Generative Adversarial Networks ; Model extraction attacks aim to duplicate a machine learning model through query access to a target model. Early studies mainly focus on discriminative models. Despite the success, model extraction attacks against generative models are less well explor... |
Timed ModelBased Mutation Operators for Simulink Models ; Modelbased mutation analysis is a recent research area, and realtime system testing can benefit from using model mutants. Modelbased mutation testing MBMT is a particular branch of modelbased testing. It generates faulty versions of a model using mutation opera... |
FlexibleSUSY A spectrum generator generator for supersymmetric models ; We introduce FlexibleSUSY, a Mathematica and C package, which generates a fast, precise C spectrum generator for any SUSY model specified by the user. The generated code is designed with both speed and modularity in mind, making it easy to adapt ... |
Generative Models for Network Neuroscience Prospects and Promise ; Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and to identify principles with which to understand them. Within this discipline, one particularly powerful a... |
GANLeaks A Taxonomy of Membership Inference Attacks against Generative Models ; Deep learning has achieved overwhelming success, spanning from discriminative models to generative models. In particular, deep generative models have facilitated a new level of performance in a myriad of areas, ranging from media manipulat... |
Adversarial Attacks Against Deep Generative Models on Data A Survey ; Deep generative models have gained much attention given their ability to generate data for applications as varied as healthcare to financial technology to surveillance, and many more the most popular models being generative adversarial networks and... |
Are You Robert or RoBERTa Deceiving Online Authorship Attribution Models Using Neural Text Generators ; Recently, there has been a rise in the development of powerful pretrained natural language models, including GPT2, Grover, and XLM. These models have shown stateoftheart capabilities towards a variety of different N... |
Diversity vs. Recognizability Humanlike generalization in oneshot generative models ; Robust generalization to new concepts has long remained a distinctive feature of human intelligence. However, recent progress in deep generative models has now led to neural architectures capable of synthesizing novel instances of un... |
Generalizing to new geometries with GeometryAware Autoregressive Models GAAMs for fast calorimeter simulation ; Generation of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive. One subdetector, the calorimeter, dominates the computatio... |
Learning Joint 2D 3D Diffusion Models for Complete Molecule Generation ; Designing new molecules is essential for drug discovery and material science. Recently, deep generative models that aim to model molecule distribution have made promising progress in narrowing down the chemical research space and generating high... |
Asking Questions the Human Way Scalable QuestionAnswer Generation from Text Corpus ; The ability to ask questions is important in both human and machine intelligence. Learning to ask questions helps knowledge acquisition, improves questionanswering and machine reading comprehension tasks, and helps a chatbot to keep t... |
Unpaired MultiDomain Image Generation via Regularized Conditional GANs ; In this paper, we study the problem of multidomain image generation, the goal of which is to generate pairs of corresponding images from different domains. With the recent development in generative models, image generation has achieved great prog... |
Text2Action Generative Adversarial Synthesis from Language to Action ; In this paper, we propose a generative model which learns the relationship between language and human action in order to generate a human action sequence given a sentence describing human behavior. The proposed generative model is a generative adve... |
DPGAN DiversityPromoting Generative Adversarial Network for Generating Informative and Diversified Text ; Existing text generation methods tend to produce repeated and boring expressions. To tackle this problem, we propose a new text generation model, called DiversityPromoting Generative Adversarial Network DPGAN. The... |
Personalized Patent Claim Generation and Measurement ; This workinprogress paper proposes a framework to generate and measure personalized patent claims. The objective is to help inventors conceive better inventions by learning from relevant inventors. Patent claim generation is a way of augmented inventing. for inven... |
QURIOUS Question Generation Pretraining for Text Generation ; Recent trends in natural language processing using pretraining have shifted focus towards pretraining and finetuning approaches for text generation. Often the focus has been on taskagnostic approaches that generalize the language modeling objective. We prop... |
BOLD Dataset and Metrics for Measuring Biases in OpenEnded Language Generation ; Recent advances in deep learning techniques have enabled machines to generate cohesive openended text when prompted with a sequence of words as context. While these models now empower many downstream applications from conversation bots to... |
MotionDiffuse TextDriven Human Motion Generation with Diffusion Model ; Human motion modeling is important for many modern graphics applications, which typically require professional skills. In order to remove the skill barriers for laymen, recent motion generation methods can directly generate human motions condition... |
DiffusionHPC Generating Synthetic Images with Realistic Humans ; Recent texttoimage generative models have exhibited remarkable abilities in generating highfidelity and photorealistic images. However, despite the visually impressive results, these models often struggle to preserve plausible human structure in the gene... |
CCLAP Controllable Chinese Landscape Painting Generation via Latent Diffusion Model ; With the development of deep generative models, recent years have seen great success of Chinese landscape painting generation. However, few works focus on controllable Chinese landscape painting generation due to the lack of data and... |
Generated Graph Detection ; Graph generative models become increasingly effective for data distribution approximation and data augmentation. While they have aroused public concerns about their malicious misuses or misinformation broadcasts, just as what Deepfake visual and auditory media has been delivering to society... |
CompoNet Learning to Generate the Unseen by Part Synthesis and Composition ; Datadriven generative modeling has made remarkable progress by leveraging the power of deep neural networks. A reoccurring challenge is how to enable a model to generate a rich variety of samples from the entire target distribution, rather th... |
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