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Parameters of NJL models for a generic representation of the gauge group ; We generalize a nonlocal NambuJonaLasinio model to a generic representation of the gauge group. The critical temperature is given in a closed form as a function of the parameters of the theory and the cutoff. This result is generally useful in ... |
The ChernRicci flow on smooth minimal models of general type ; We show that on a smooth Hermitian minimal model of general type the ChernRicci flow converges to a closed positive current on M. Moreover, the flow converges smoothly to a KahlerEinstein metric on compact sets away from the null locus of KM. This generali... |
Energymomentum distribution of a general plane symmetric spacetime in metric fR gravity ; In this paper, the exact vacuum solution of a general plane symmetric spacetime is investigated in metric fR gravity with the assumption of constant Ricci scalar. For this solution, we have studied the generalized LandauLifshitz ... |
StrokeCoder PathBased Image Generation from Single Examples using Transformers ; This paper demonstrates how a Transformer Neural Network can be used to learn a Generative Model from a single pathbased example image. We further show how a data set can be generated from the example image and how the model can be used t... |
Automatic Evaluation of Neural Personalitybased Chatbots ; Stylistic variation is critical to render the utterances generated by conversational agents natural and engaging. In this paper, we focus on sequencetosequence models for opendomain dialogue response generation and propose a new method to evaluate the extent t... |
Unsupervised Quantum Circuit Learning in High Energy Physics ; Unsupervised training of generative models is a machine learning task that has many applications in scientific computing. In this work we evaluate the efficacy of using quantum circuitbased generative models to generate synthetic data of high energy physic... |
When to Trust Your Model ModelBased Policy Optimization ; Designing effective modelbased reinforcement learning algorithms is difficult because the ease of data generation must be weighed against the bias of modelgenerated data. In this paper, we study the role of model usage in policy optimization both theoretically ... |
Evaluating Generative Patent Language Models ; Generative language models are promising for assisting human writing in various domains. This manuscript aims to build generative language models in the patent domain and evaluate model performance from a humancentric perspective. The perspective is to measure the ratio o... |
The Robustness Limits of SoTA Vision Models to Natural Variation ; Recent stateoftheart vision models introduced new architectures, learning paradigms, and larger pretraining data, leading to impressive performance on tasks such as classification. While previous generations of vision models were shown to lack robustne... |
Optimization of coarsegrained models matching probability density in conformational space ; CoarseGraining CG models are low resolution approximation of high resolution models, such as allatomic AA models. An effective CG model is expected to reproduce equilibrium values of sufficient physical quantities of its AA mod... |
A generalized configuration model with triadic closure ; In this paper we present a generalized configuration model with random triadic closure GCTC. This model possesses five fundamental properties large clustering coefficient, power law degree distribution, short path length, nonzero Pearson degree correlation, and ... |
Classifying Emails into Human vs Machine Category ; It is an essential product requirement of Yahoo Mail to distinguish between personal and machinegenerated emails. The old production classifier in Yahoo Mail was based on a simple logistic regression model. That model was trained by aggregating features at the SMTP a... |
OutofDomain Semantics to the Rescue ZeroShot Hybrid Retrieval Models ; The pretrained language model eg, BERT based deep retrieval models achieved superior performance over lexical retrieval models eg, BM25 in many passage retrieval tasks. However, limited work has been done to generalize a deep retrieval model to oth... |
Multifold CrossValidation Model Averaging for Generalized Additive Partial Linear Models ; Generalized additive partial linear models GAPLMs are appealing for model interpretation and prediction. However, for GAPLMs, the covariates and the degree of smoothing in the nonparametric parts are often difficult to determine... |
ZipIt Merging Models from Different Tasks without Training ; Typical deep visual recognition models are capable of performing the one task they were trained on. In this paper, we tackle the extremely difficult problem of combining completely distinct models with different initializations, each solving a separate task,... |
A Model Structure on the Category of Topological Categories ; In this article, we construct a cofibrantly generated Quillen model structure on the category of small topological categories mathbfCatmathbfTop. It is Quillen equivalent to the Joyal model structure of infty,1categories and the Bergner model structure on m... |
Phylogenetic complexity of the Kimura 3parameter model ; In algebraic statistics, the Kimura 3parameter model is one of the most interesting and classical phylogenetic models. We prove that the ideals associated to this model are generated in degree four, confirming a conjecture by Sturmfels and Sullivant. |
A generalized lattice Boltzmann model for fluid flow system and its application in twophase flows ; In this paper, a generalized lattice Boltzmann LB model with a mass source is proposed to solve both incompressible and nearly incompressible NavierStokes NS equations. This model can be used to deal with singlephase an... |
Timedependent Heston model ; This work presents an exact solution to the generalized Heston model, where the model parameters are assumed to have linear time dependence The solution for the model in expressed in terms of confluent hypergeometric functions. |
Symbolic Knowledge Distillation from General Language Models to Commonsense Models ; The common practice for training commonsense models has gone fromhumantocorpustomachine humans author commonsense knowledge graphs in order to train commonsense models. In this work, we investigate an alternative, frommachinetocorpust... |
Artificial Interrogation for Attributing Language Models ; This paper presents solutions to the Machine Learning Model Attribution challenge MLMAC collectively organized by MITRE, Microsoft, SchmidtFutures, RobustIntelligence, LincolnNetwork, and Huggingface community. The challenge provides twelve opensourced base ve... |
On an inferential model construction using generalized associations ; The inferential model IM approach, like fiducial and its generalizations, depends on a representation of the datagenerating process. Here, a particular variation on the IM construction is considered, one based on generalized associations. The result... |
Synthesizing Tabular Data using Generative Adversarial Networks ; Generative adversarial networks GANs implicitly learn the probability distribution of a dataset and can draw samples from the distribution. This paper presents, Tabular GAN TGAN, a generative adversarial network which can generate tabular data like medi... |
Controllable Text Generation with Focused Variation ; This work introduces FocusedVariation Network FVN, a novel model to control language generation. The main problems in previous controlled language generation models range from the difficulty of generating text according to the given attributes, to the lack of diver... |
Improving Compositional Generalization in Classification Tasks via Structure Annotations ; Compositional generalization is the ability to generalize systematically to a new data distribution by combining known components. Although humans seem to have a great ability to generalize compositionally, stateoftheart neural ... |
TrainingFree LocationAware TexttoImage Synthesis ; Current largescale generative models have impressive efficiency in generating highquality images based on text prompts. However, they lack the ability to precisely control the size and position of objects in the generated image. In this study, we analyze the generativ... |
Towards Diverse and Consistent Typography Generation ; In this work, we consider the typography generation task that aims at producing diverse typographic styling for the given graphic document. We formulate typography generation as a finegrained attribute generation for multiple text elements and build an autoregress... |
Dilated Spatial Generative Adversarial Networks for Ergodic Image Generation ; Generative models have recently received renewed attention as a result of adversarial learning. Generative adversarial networks consist of samples generation model and a discrimination model able to distinguish between genuine and synthetic... |
Generating Pertinent and Diversified Comments with Topicaware PointerGenerator Networks ; Comment generation, a new and challenging task in Natural Language Generation NLG, attracts a lot of attention in recent years. However, comments generated by previous work tend to lack pertinence and diversity. In this paper, we... |
GraphStega Semantic Controllable Steganographic Text Generation Guided by Knowledge Graph ; Most of the existing text generative steganographic methods are based on coding the conditional probability distribution of each word during the generation process, and then selecting specific words according to the secret info... |
Exploiting Pretrained Feature Networks for Generative Adversarial Networks in Audiodomain Loop Generation ; While generative adversarial networks GANs have been widely used in research on audio generation, the training of a GAN model is known to be unstable, time consuming, and data inefficient. Among the attempts to ... |
Learning to Rank in Generative Retrieval ; Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. This paradigm leverages powerful generation models and represents a new paradigm distinct from traditional learningtorank methods... |
Sharpness Minimization Algorithms Do Not Only Minimize Sharpness To Achieve Better Generalization ; Despite extensive studies, the underlying reason as to why overparameterized neural networks can generalize remains elusive. Existing theory shows that common stochastic optimizers prefer flatter minimizers of the train... |
General Canonical Quantum Gravity Theory and that of the Universe and General Black Hole ; This paper gives both a general canonical quantum gravity theory and the general canonical quantum gravity theories of the Universe and general black hole, and discovers the relations reflecting symmetric properties of the stand... |
A Survey on RetrievalAugmented Text Generation ; Recently, retrievalaugmented text generation attracted increasing attention of the computational linguistics community. Compared with conventional generation models, retrievalaugmented text generation has remarkable advantages and particularly has achieved stateoftheart... |
Composing Ensembles of Pretrained Models via Iterative Consensus ; Large pretrained models exhibit distinct and complementary capabilities dependent on the data they are trained on. Language models such as GPT3 are capable of textual reasoning but cannot understand visual information, while vision models such as DALLE... |
ApproximationGeneralization Tradeoffs under Approximate Group Equivariance ; The explicit incorporation of taskspecific inductive biases through symmetry has emerged as a general design precept in the development of highperformance machine learning models. For example, group equivariant neural networks have demonstrat... |
Learning Joint Latent Space EBM Prior Model for Multilayer Generator ; This paper studies the fundamental problem of learning multilayer generator models. The multilayer generator model builds multiple layers of latent variables as a prior model on top of the generator, which benefits learning complex data distributio... |
Distilling Model Knowledge ; Topperforming machine learning systems, such as deep neural networks, large ensembles and complex probabilistic graphical models, can be expensive to store, slow to evaluate and hard to integrate into larger systems. Ideally, we would like to replace such cumbersome models with simpler mod... |
The generalized stochastic preference choice model ; We propose a new discrete choice model, called the generalized stochastic preference GSP model, that incorporates nonrationality into the stochastic preference SP choice model, also known as the rank based choice model. Our model can explain several choice phenomena... |
Forecasting SpatioTemporal Renewable Scenarios a Deep Generative Approach ; The operation and planning of largescale power systems are becoming more challenging with the increasing penetration of stochastic renewable generation. In order to minimize the decision risks in power systems with large amount of renewable re... |
A MetaLearning Framework for Generalized ZeroShot Learning ; Learning to classify unseen class samples at test time is popularly referred to as zeroshot learning ZSL. If test samples can be from training seen as well as unseen classes, it is a more challenging problem due to the existence of strong bias towards seen c... |
Jointly Trained Image and Video Generation using Residual Vectors ; In this work, we propose a modeling technique for jointly training image and video generation models by simultaneously learning to map latent variables with a fixed prior onto real images and interpolate over images to generate videos. The proposed ap... |
Generative models for sampling and phase transition indication in spin systems ; Recently, generative machinelearning models have gained popularity in physics, driven by the goal of improving the efficiency of Markov chain Monte Carlo techniques and of exploring their potential in capturing experimental data distribut... |
ScoreBased Generative Modeling through Stochastic Differential Equations ; Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation SDE that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, a... |
DISCO Distilling Counterfactuals with Large Language Models ; Models trained with counterfactually augmented data learn representations of the causal structure of tasks, enabling robust generalization. However, highquality counterfactual data is scarce for most tasks and not easily generated at scale. When crowdsource... |
q2d Turning Questions into Dialogs to Teach Models How to Search ; One of the exciting capabilities of recent language models for dialog is their ability to independently search for relevant information to ground a given dialog response. However, obtaining training data to teach models how to issue search queries is t... |
Smaller Language Models are Better Blackbox MachineGenerated Text Detectors ; With the advent of fluent generative language models that can produce convincing utterances very similar to those written by humans, distinguishing whether a piece of text is machinegenerated or humanwritten becomes more challenging and more... |
A Federated Channel Modeling System using Generative Neural Networks ; The paper proposes a datadriven approach to airtoground channel estimation in a millimeterwave wireless network on an unmanned aerial vehicle. Unlike traditional centralized learning methods that are specific to certain geographical areas and inapp... |
A General Method for Robust Bayesian Modeling ; Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions. Historically, robust models were mostly developed on a casebycase basis; examples include robust li... |
A Collapsed Generalized AwRascleZhang Model and Its Model Accuracy ; This work presents a collapsed generalized AwRascleZhang CGARZ model, which fits into a generic second order model GSOM framework. GSOMs augment the evolution of the traffic density by a second state variable characterizing a property of vehicles or ... |
One evaluation of modelbased testing and its automation ; Modelbased testing relies on behavior models for the generation of model traces input and expected outputtest casesfor an implementation. We use the case study of an automotive network controller to assess different test suites in terms of error detection, mode... |
Extendability in the Sheaftheoretic Approach Construction of Bell Models from KochenSpecker Models ; Extendability of an empirical model was shown by Abramsky Brandenburger to correspond in a unified manner to both locality and noncontextuality. We develop their approach by presenting a refinement of the notion of ex... |
Automatic Generation of Adaptive Network Models based on Similarity to the Desired Complex Network ; Complex networks have become powerful mechanisms for studying a variety of realworld systems. Consequently, many humandesigned network models are proposed that reproduce nontrivial properties of complex networks, such ... |
A Bidomain Model for Lens Microcirculation ; There exists a large body of research on the lens of mammalian eye over the past several decades. The objective of the current work is to provide a link between the most recent computational models to some of the pioneering work in the 1970s and 80s. We introduce a general ... |
A comparison of streaming models and data augmentation methods for robust speech recognition ; In this paper, we present a comparative study on the robustness of two different online streaming speech recognition models Monotonic Chunkwise Attention MoChA and Recurrent Neural NetworkTransducer RNNT. We explore three re... |
DST Dynamic Substitute Training for Datafree Blackbox Attack ; With the wide applications of deep neural network models in various computer vision tasks, more and more works study the model vulnerability to adversarial examples. For datafree black box attack scenario, existing methods are inspired by the knowledge dis... |
METRO Efficient Denoising Pretraining of Large Scale Autoencoding Language Models with Model Generated Signals ; We present an efficient method of pretraining largescale autoencoding language models using training signals generated by an auxiliary model. Originated in ELECTRA, this training strategy has demonstrated s... |
Minimizing Maximum Model Discrepancy for Transferable Blackbox Targeted Attacks ; In this work, we study the blackbox targeted attack problem from the model discrepancy perspective. On the theoretical side, we present a generalization error bound for blackbox targeted attacks, which gives a rigorous theoretical analys... |
Matching Pairs Attributing FineTuned Models to their PreTrained Large Language Models ; The wide applicability and adaptability of generative large language models LLMs has enabled their rapid adoption. While the pretrained models can perform many tasks, such models are often finetuned to improve their performance on ... |
A generalization of Quillen's small object argument ; We generalize the small object argument in order to allow for its application to proper classes of maps as opposed to sets of maps in Quillen's small object argument. The necessity of such a generalization arose with appearance of several important examples of mode... |
2nDimensional Models with Topological Mass Generation ; The 4dimensional model with topological mass generation that has recently been presented by Dvali, Jackiw and Pi G. Dvali, R. Jackiw, and S.Y. Pi, Phys. Rev. Lett. 96, 081602 2006, hepth0610228 is generalized to any even number of dimensions. As in the 4dimension... |
Finding Optimal Bayesian Networks ; In this paper, we derive optimality results for greedy Bayesiannetwork search algorithms that perform singleedge modifications at each step and use asymptotically consistent scoring criteria. Our results extend those of Meek 1997 and Chickering 2002, who demonstrate that in the limi... |
A Generative Model of People in Clothing ; We present the first imagebased generative model of people in clothing for the full body. We sidestep the commonly used complex graphics rendering pipeline and the need for highquality 3D scans of dressed people. Instead, we learn generative models from a large image database... |
Depth Structure Preserving Scene Image Generation ; Key to automatically generate natural scene images is to properly arrange among various spatial elements, especially in the depth direction. To this end, we introduce a novel depth structure preserving scene image generation network DSPGAN, which favors a hierarchica... |
Generative learning for deep networks ; Learning, taking into account full distribution of the data, referred to as generative, is not feasible with deep neural networks DNNs because they model only the conditional distribution of the outputs given the inputs. Current solutions are either based on joint probability mo... |
Simulating Multichannel Wind Noise Based on the Corcos Model ; A novel multichannel artificial wind noise generator based on a fluid dynamics model, namely the Corcos model, is proposed. In particular, the model is used to approximate the complex coherence function of wind noise signals measured with closelyspaced mic... |
Improving Sampling from Generative Autoencoders with Markov Chains ; We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. Generative autoencoders are those which are trained to softly enforce a prior on the latent dis... |
Augmenting Neural Response Generation with ContextAware Topical Attention ; SequencetoSequence Seq2Seq models have witnessed a notable success in generating natural conversational exchanges. Notwithstanding the syntactically wellformed responses generated by these neural network models, they are prone to be acontextua... |
Generative Deep Neural Networks for Dialogue A Short Review ; Researchers have recently started investigating deep neural networks for dialogue applications. In particular, generative sequencetosequence Seq2Seq models have shown promising results for unstructured tasks, such as wordlevel dialogue response generation. ... |
Learning Deep Generative Models of Graphs ; Graphs are fundamental data structures which concisely capture the relational structure in many important realworld domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative... |
Latent Adversarial Defence with Boundaryguided Generation ; Deep Neural Networks DNNs have recently achieved great success in many tasks, which encourages DNNs to be widely used as a machine learning service in model sharing scenarios. However, attackers can easily generate adversarial examples with a small perturbati... |
CPgeneric expansions of models of Peano Arithmetic ; We study notions of genericity in models of mathsfPA, inspired by lines of inquiry initiated by Chatzidakis and Pillay and continued by Dolich, Miller and Steinhorn in general modeltheoretic contexts. These papers studied the theories obtained by adding a random pre... |
LatentVariable Generative Models for DataEfficient Text Classification ; Generative classifiers offer potential advantages over their discriminative counterparts, namely in the areas of data efficiency, robustness to data shift and adversarial examples, and zeroshot learning Ng and Jordan,2002; Yogatama et al., 2017; ... |
Stochastic DC Optimal Power Flow With Reserve Saturation ; We propose an optimization framework for stochastic optimal power flow with uncertain loads and renewable generator capacity. Our model follows previous work in assuming that generator outputs respond to load imbalances according to an affine control policy, b... |
Generalization Error of Generalized Linear Models in High Dimensions ; At the heart of machine learning lies the question of generalizability of learned rules over previously unseen data. While overparameterized models based on neural networks are now ubiquitous in machine learning applications, our understanding of t... |
Exponential Tilting of Generative Models Improving Sample Quality by Training and Sampling from Latent Energy ; In this paper, we present a general method that can improve the sample quality of pretrained likelihood based generative models. Our method constructs an energy function on the latent variable space that yie... |
Efficient Training Data Generation for PhaseBased DOA Estimation ; Deep learning DL based direction of arrival DOA estimation is an active research topic and currently represents the stateoftheart. Usually, DLbased DOA estimators are trained with recorded data or computationally expensive generated data. Both data typ... |
Integration of Renewable Generators in Synthetic Electric Grids for Dynamic Analysis ; This paper presents a method to better integrate dynamic models for renewable resources into synthetic electric grids. An automated dynamic models assignment process is proposed for wind and solar generators. A realistic composition... |
Test case generation for agentbased models A systematic literature review ; Agentbased models play an important role in simulating complex emergent phenomena and supporting critical decisions. In this context, a software fault may result in poorly informed decisions that lead to disastrous consequences. The ability to... |
Estimating Subjective CrowdEvaluations as an Additional Objective to Improve Natural Language Generation ; Human ratings are one of the most prevalent methods to evaluate the performance of natural language processing algorithms. Similarly, it is common to measure the quality of sentences generated by a natural langua... |
Augmenting Molecular Deep Generative Models with Topological Data Analysis Representations ; Deep generative models have emerged as a powerful tool for learning useful molecular representations and designing novel molecules with desired properties, with applications in drug discovery and material design. However, most... |
View Generalization for Single Image Textured 3D Models ; Humans can easily infer the underlying 3D geometry and texture of an object only from a single 2D image. Current computer vision methods can do this, too, but suffer from view generalization problems the models inferred tend to make poor predictions of appeara... |
Latent Space EnergyBased Model of SymbolVector Coupling for Text Generation and Classification ; We propose a latent space energybased prior model for text generation and classification. The model stands on a generator network that generates the text sequence based on a continuous latent vector. The energy term of the... |
StackGAN Facial Image Generation Optimizations ; Current stateoftheart photorealistic generators are computationally expensive, involve unstable training processes, and have real and synthetic distributions that are dissimilar in higherdimensional spaces. To solve these issues, we propose a variant of the StackGAN arc... |
MetaGeneralization for Multiparty Privacy Learning to Identify Anomaly Multimedia Traffic in Graynet ; Identifying anomaly multimedia traffic in cyberspace is a big challenge in distributed service systems, multiple generation networks and future internet of everything. This letter explores metageneralization for a mu... |
Texture Generation Using DualDomain Feature Flow with MultiView Hallucinations ; We propose a dualdomain generative model to estimate a texture map from a single image for colorizing a 3D human model. When estimating a texture map, a single image is insufficient as it reveals only one facet of a 3D object. To provide ... |
3D pride without 2D prejudice Biascontrolled multilevel generative models for structurebased ligand design ; Generative models for structurebased molecular design hold significant promise for drug discovery, with the potential to speed up the hittolead development cycle, while improving the quality of drug candidates ... |
Exploring Length Generalization in Large Language Models ; The ability to extrapolate from short problem instances to longer ones is an important form of outofdistribution generalization in reasoning tasks, and is crucial when learning from datasets where longer problem instances are rare. These include theorem provin... |
Democratizing Ethical Assessment of Natural Language Generation Models ; Natural language generation models are computer systems that generate coherent language when prompted with a sequence of words as context. Despite their ubiquity and many beneficial applications, language generation models also have the potential... |
Learning to Generate 3D Shapes from a Single Example ; Existing generative models for 3D shapes are typically trained on a large 3D dataset, often of a specific object category. In this paper, we investigate the deep generative model that learns from only a single reference 3D shape. Specifically, we present a multisc... |
Machine Generated Text A Comprehensive Survey of Threat Models and Detection Methods ; Machine generated text is increasingly difficult to distinguish from human authored text. Powerful opensource models are freely available, and userfriendly tools that democratize access to generative models are proliferating. ChatGP... |
Is synthetic data from generative models ready for image recognition ; Recent texttoimage generation models have shown promising results in generating highfidelity photorealistic images. Though the results are astonishing to human eyes, how applicable these generated images are for recognition tasks remains underexplo... |
Comparing Synthetic Tabular Data Generation Between a Probabilistic Model and a Deep Learning Model for Education Use Cases ; The ability to generate synthetic data has a variety of use cases across different domains. In education research, there is a growing need to have access to synthetic data to test certain conce... |
Controllable Text Generation with Language Constraints ; We consider the task of text generation in language models with constraints specified in natural language. To this end, we first create a challenging benchmark Cognac that provides as input to the model a topic with example text, along with a constraint on text ... |
Learning to Generate Questions by Enhancing Text Generation with Sentence Selection ; We introduce an approach for the answeraware question generation problem. Instead of only relying on the capability of strong pretrained language models, we observe that the information of answers and questions can be found in some r... |
Deep Image Fingerprint Towards Low Budget Synthetic Image Detection and Model Lineage Analysis ; The generation of highquality images has become widely accessible and is a rapidly evolving process. As a result, anyone can generate images that are indistinguishable from real ones. This leads to a wide range of applicat... |
Understanding Deep Generative Models with Generalized Empirical Likelihoods ; Understanding how well a deep generative model captures a distribution of highdimensional data remains an important open challenge. It is especially difficult for certain model classes, such as Generative Adversarial Networks and Diffusion M... |
Solving and Generating NPR Sunday Puzzles with Large Language Models ; We explore the ability of large language models to solve and generate puzzles from the NPR Sunday Puzzle game show using PUZZLEQA, a dataset comprising 15 years of onair puzzles. We evaluate four large language models using PUZZLEQA, in both multip... |
MultiBERT for Embeddings for Recommendation System ; In this paper, we propose a novel approach for generating document embeddings using a combination of SentenceBERT SBERT and RoBERTa, two stateoftheart natural language processing models. Our approach treats sentences as tokens and generates embeddings for them, allo... |
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