text stringlengths 62 2.94k |
|---|
ChatGPTsteered Editing Instructor for Customization of Abstractive Summarization ; Tailoring outputs of large language models, such as ChatGPT, to specific user needs remains a challenge despite their impressive generation quality. In this paper, we propose a triagent generation pipeline consisting of a generator, an ... |
Retrieval Augmented Chest XRay Report Generation using OpenAI GPT models ; We propose Retrieval Augmented Generation RAG as an approach for automated radiology report writing that leverages multimodally aligned embeddings from a contrastively pretrained vision language model for retrieval of relevant candidate radiolo... |
Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models ; We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient trajectory data to jointly lea... |
CausalGAN Learning Causal Implicit Generative Models with Adversarial Training ; We propose an adversarial training procedure for learning a causal implicit generative model for a given causal graph. We show that adversarial training can be used to learn a generative model with true observational and interventional di... |
MultiPLE A Scalable and Extensible Approach to Benchmarking Neural Code Generation ; Large language models have demonstrated the ability to generate both natural language and programming language text. Such models open up the possibility of multilanguage code generation could code generation models generalize knowledg... |
Combinatorial Modelling and Learning with Prediction Markets ; Combining models in appropriate ways to achieve high performance is commonly seen in machine learning fields today. Although a large amount of combinatorial models have been created, little attention is drawn to the commons in different models and their co... |
A ModuleSystem Discipline for ModelDriven Software Development ; Modeldriven development is a pragmatic approach to software development that embraces domainspecific languages DSLs, where models correspond to DSL programs. A distinguishing feature of modeldriven development is that clients of a model can select from a... |
GAMIN An Adversarial Approach to BlackBox Model Inversion ; Recent works have demonstrated that machine learning models are vulnerable to model inversion attacks, which lead to the exposure of sensitive information contained in their training dataset. While some model inversion attacks have been developed in the past ... |
Automated Conversion of Axiomatic to Operational Models Theory and Practice ; A system may be modelled as an operational model which has explicit notions of state and transitions between states or an axiomatic model which is specified entirely as a set of invariants. Most formal methods techniques e.g., IC3, invariant... |
The generic model of General Relativity ; We develop a generic spacetime model in General Relativity which can be used to build any gravitational model within General Relativity. The generic model uses two types of assumptions a Geometric assumptions additional to the inherent geometric identities of the Riemannian ge... |
Domain Generalization using Pretrained Models without Finetuning ; Finetuning pretrained models is a common practice in domain generalization DG tasks. However, finetuning is usually computationally expensive due to the evergrowing size of pretrained models. More importantly, it may cause overfitting on source domain ... |
Generative AI for EndtoEnd Limit Order Book Modelling A TokenLevel Autoregressive Generative Model of Message Flow Using a Deep State Space Network ; Developing a generative model of realistic order flow in financial markets is a challenging open problem, with numerous applications for market participants. Addressing ... |
A unified view of generative models for networks models, methods, opportunities, and challenges ; Research on probabilistic models of networks now spans a wide variety of fields, including physics, sociology, biology, statistics, and machine learning. These efforts have produced a diverse ecology of models and methods... |
An Attentional Neural Conversation Model with Improved Specificity ; In this paper we propose a neural conversation model for conducting dialogues. We demonstrate the use of this model to generate help desk responses, where users are asking questions about PC applications. Our model is distinguished by two characteris... |
Connectivity and Structure in Large Networks ; Large reallife complex networks are often modeled by various random graph constructions and hundreds of further references therein. In many cases it is not at all clear how the modeling strength of differently generated random graph model classes relate to each other. We ... |
Marginally Interpretable Generalized Linear Mixed Models ; Two popular approaches for relating correlated measurements of a nonGaussian response variable to a set of predictors are to fit a marginal model using generalized estimating equations and to fit a generalized linear mixed model by introducing latent random va... |
Learning Dynamic Generator Model by Alternating BackPropagation Through Time ; This paper studies the dynamic generator model for spatialtemporal processes such as dynamic textures and action sequences in video data. In this model, each time frame of the video sequence is generated by a generator model, which is a non... |
VideoFlow A Conditional FlowBased Model for Stochastic Video Generation ; Generative models that can model and predict sequences of future events can, in principle, learn to capture complex realworld phenomena, such as physical interactions. However, a central challenge in video prediction is that the future is highly... |
Zeroshot TexttoSQL Learning with Auxiliary Task ; Recent years have seen great success in the use of neural seq2seq models on the texttoSQL task. However, little work has paid attention to how these models generalize to realistic unseen data, which naturally raises a question does this impressive performance signify a... |
Powering Hidden Markov Model by Neural Network based Generative Models ; Hidden Markov model HMM has been successfully used for sequential data modeling problems. In this work, we propose to power the modeling capacity of HMM by bringing in neural network based generative models. The proposed model is termed as GenHMM... |
Empathetic BERT2BERT Conversational Model Learning Arabic Language Generation with Little Data ; Enabling empathetic behavior in Arabic dialogue agents is an important aspect of building humanlike conversational models. While Arabic Natural Language Processing has seen significant advances in Natural Language Understa... |
GENOME A GENeric methodology for Ontological Modelling of Epics ; Ontological knowledge modelling of epics, though being an established research arena backed by concrete multilingual and multicultural works, still suffer from two key shortcomings. Firstly, all epic ontological models developed till date have been desi... |
Learning to Model Editing Processes ; Most existing sequence generation models produce outputs in one pass, usually lefttoright. However, this is in contrast with a more natural approach that humans use in generating content; iterative refinement and editing. Recent work has introduced editbased models for various tas... |
Interactive Text Generation ; Users interact with text, image, code, or other editors on a daily basis. However, machine learning models are rarely trained in the settings that reflect the interactivity between users and their editor. This is understandable as training AI models with real users is not only slow and co... |
A Probabilistic Fluctuation based Membership Inference Attack for Diffusion Models ; Membership Inference Attack MIA identifies whether a record exists in a machine learning model's training set by querying the model. MIAs on the classic classification models have been wellstudied, and recent works have started to exp... |
Generative Quantum Machine Learning ; The goal of generative machine learning is to model the probability distribution underlying a given data set. This probability distribution helps to characterize the generation process of the data samples. While classical generative machine learning is solely based on classical re... |
ConvGeN Convex space learning improves deepgenerative oversampling for tabular imbalanced classification on smaller datasets ; Data is commonly stored in tabular format. Several fields of research are prone to small imbalanced tabular data. Supervised Machine Learning on such data is often difficult due to class imbal... |
On the Reliability and Explainability of Automated Code Generation Approaches ; Automatic code generation, the task of generating new code snippets from existing code or comments, has long been of interest. Numerous code generation models have been proposed and proven on different benchmark datasets. However, little i... |
Stability in Generalized Modified Gravity ; The stability issue of a large class of modified gravitational models is discussed with particular emphasis to de Sitter solutions. Three approaches are briefly presented and the generalization to more general cases is mentioned. |
Generalized Network Psychometrics Combining Network and Latent Variable Models ; We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with... |
ExactlySolvable Models Derived from a Generalized Gaudin Algebra ; We introduce a generalized Gaudin Lie algebra and a complete set of mutually commuting quantum invariants allowing the derivation of several families of exactly solvable Hamiltonians. Different Hamiltonians correspond to different representations of th... |
On the Use of Cellular Automata in Symmetric Cryptography ; In this work, pseudorandom sequence generators based on finite fields have been analyzed from the point of view of their cryptographic application. In fact, a class of nonlinear sequence generators has been modelled in terms of linear cellular automata. The a... |
Simple model for quantum general relativity from loop quantum gravity ; New progress in loop gravity has lead to a simple model of generalcovariant quantum field theory'. I sum up the definition of the model in selfcontained form, in terms accessible to those outside the subfield. I emphasize its formulation as a gene... |
Thermodynamics in KaluzaKlein Universe ; This paper is devoted to check the validity of laws of thermodynamics for KaluzaKlein universe in the state of thermal equilibrium, composed of dark matter and dark energy. The generalized holographic dark energy and generalized Ricci dark energy models are considered here. It ... |
Morphological Inflection Generation Using Character Sequence to Sequence Learning ; Morphological inflection generation is the task of generating the inflected form of a given lemma corresponding to a particular linguistic transformation. We model the problem of inflection generation as a character sequence to sequenc... |
Nonstandard cohomology for equivariant sheaves The role of generic models ; We generalize the Generic Model Theorem for equivariant presheaves of structures; extending the results of Macintyre and Caicedo. We also introduce a new class of generic cohomologies and show how, for some examples, they simplify to non stand... |
Generalized model for anisotropic compact stars ; In the present investigation an exact generalized model for anisotropic compact stars of embedding class one is sought for under general relativistic background. The generic solutions are verified by exploring different physical aspects, viz. energy conditions, massrad... |
Implicit Modeling A Generalization of Discriminative and Generative Approaches ; We propose a new modeling approach that is a generalization of generative and discriminative models. The core idea is to use an implicit parameterization of a joint probability distribution by specifying only the conditional distribution... |
Generative Code Modeling with Graphs ; Generative models for source code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs. We present a novel model for this problem that uses a graph to represent the inter... |
NIPS 2016 Tutorial Generative Adversarial Networks ; This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks GANs. The tutorial describes 1 Why generative modeling is a topic worth studying, 2 how generative models work, and how GANs compare to other generative model... |
Human Motion Modeling using DVGANs ; We present a novel generative model for human motion modeling using Generative Adversarial Networks GANs. We formulate the GAN discriminator using dense validation at each timescale and perturb the discriminator input to make it translation invariant. Our model is capable of motion... |
Introducing the Generalized GNmodel for Nonlinear Interference Generation including spacefrequency variations of lossgain ; We develop and present a generalization of the GNmodel the generalized Gaussian noise GGN model to enabling a fair application of GNmodel to predict generation of nonlinear interference when lo... |
Locally optimal designs for generalized linear models within the family of Kiefer kcriteria ; Locally optimal designs for generalized linear models are derived at certain values of the regression parameters. In the present paper a general setup of the generalized linear model is considered. Analytic solutions for opti... |
Provable Lipschitz Certification for Generative Models ; We present a scalable technique for upper bounding the Lipschitz constant of generative models. We relate this quantity to the maximal norm over the set of attainable vectorJacobian products of a given generative model. We approximate this set by layerwise conve... |
Topcolorlike dynamics and new matter generations ; We explore a scenarios where topcolorlike dynamics operates in the presence of fourth generation matter fields. Using the Minimal Walking Technicolor as a concrete basis for model building, we construct explicit models and confront them with phenomenology. We show tha... |
DPAGE Diverse Paraphrase Generation ; In this paper, we investigate the diversity aspect of paraphrase generation. Prior deep learning models employ either decoding methods or add random input noise for varying outputs. We propose a simple method Diverse Paraphrase Generation DPAGE, which extends neural machine transl... |
Improving Neural Question Generation using World Knowledge ; In this paper, we propose a method for incorporating world knowledge linked entities and finegrained entity types into a neural question generation model. This world knowledge helps to encode additional information related to the entities present in the pass... |
TMLab Generative Enhanced Model GEM for adversarial attacks ; We present our Generative Enhanced Model GEM that we used to create samples awarded the first prize on the FEVER 2.0 Breakers Task. GEM is the extended language model developed upon GPT2 architecture. The addition of novel target vocabulary input to the alr... |
Label Dependent Deep Variational Paraphrase Generation ; Generating paraphrases that are lexically similar but semantically different is a challenging task. Paraphrases of this form can be used to augment data sets for various NLP tasks such as machine reading comprehension and question answering with nontrivial negat... |
Getting Topology and Point Cloud Generation to Mesh ; In this work, we explore the idea that effective generative models for point clouds under the autoencoding framework must acknowledge the relationship between a continuous surface, a discretized mesh, and a set of points sampled from the surface. This view motivate... |
Towards a Fast and Accurate Model of Intercontact Times for Epidemic Routing ; We present an accurate userencounter trace generator based on analytical models. Our method generates traces of intercontact times faster than models that explicitly generate mobility traces. We use this trace generator to study the charact... |
Hierarchical Video Generation for Complex Data ; Videos can often be created by first outlining a global description of the scene and then adding local details. Inspired by this we propose a hierarchical model for video generation which follows a coarse to fine approach. First our model generates a low resolution vide... |
A Unified Framework for Pun Generation with Humor Principles ; We propose a unified framework to generate both homophonic and homographic puns to resolve the splitup in existing works. Specifically, we incorporate three linguistic attributes of puns to the language models ambiguity, distinctiveness, and surprise. Our ... |
LayoutDiffuse Adapting Foundational Diffusion Models for LayouttoImage Generation ; Layouttoimage generation refers to the task of synthesizing photorealistic images based on semantic layouts. In this paper, we propose LayoutDiffuse that adapts a foundational diffusion model pretrained on largescale image or textimage... |
DiffECG A Generalized Probabilistic Diffusion Model for ECG Signals Synthesis ; In recent years, deep generative models have gained attention as a promising data augmentation solution for heart disease detection using deep learning approaches applied to ECG signals. In this paper, we introduce a novel approach based o... |
Improving Domain Generalization for Sound Classification with Sparse FrequencyRegularized Transformer ; Sound classification models' performance suffers from generalizing on outofdistribution OOD data. Numerous methods have been proposed to help the model generalize. However, most either introduce inference overheads ... |
Stochastic Feature Mapping for PACBayes Classification ; Probabilistic generative modeling of data distributions can potentially exploit hidden information which is useful for discriminative classification. This observation has motivated the development of approaches that couple generative and discriminative models fo... |
Defuse Harnessing Unrestricted Adversarial Examples for Debugging Models Beyond Test Accuracy ; We typically compute aggregate statistics on heldout test data to assess the generalization of machine learning models. However, statistics on test data often overstate model generalization, and thus, the performance of dep... |
Shortterm forecasting of solar irradiance without local telemetry a generalized model using satellite data ; Due to the increasing integration of solar power into the electrical grid, forecasting shortterm solar irradiance has become key for many applications, e.g.operational planning, power purchases, reserve activat... |
PETGEN Personalized Text Generation Attack on Deep Sequence Embeddingbased Classification Models ; What should a malicious user write next to fool a detection model Identifying malicious users is critical to ensure the safety and integrity of internet platforms. Several deep learningbased detection models have been cr... |
A Survey on Generative Diffusion Model ; Deep generative models are a prominent approach for data generation, and have been used to produce high quality samples in various domains. Diffusion models, an emerging class of deep generative models, have attracted considerable attention owing to their exceptional generative... |
Generative Visual Prompt Unifying Distributional Control of PreTrained Generative Models ; Generative models e.g., GANs, diffusion models learn the underlying data distribution in an unsupervised manner. However, many applications of interest require sampling from a particular region of the output space or sampling ev... |
Dual Student Networks for DataFree Model Stealing ; Existing datafree model stealing methods use a generator to produce samples in order to train a student model to match the target model outputs. To this end, the two main challenges are estimating gradients of the target model without access to its parameters, and ge... |
Bottlenecks CLUB Unifying InformationTheoretic Tradeoffs Among Complexity, Leakage, and Utility ; Bottleneck problems are an important class of optimization problems that have recently gained increasing attention in the domain of machine learning and information theory. They are widely used in generative models, fair ... |
Toward a Generalization Metric for Deep Generative Models ; Measuring the generalization capacity of Deep Generative Models DGMs is difficult because of the curse of dimensionality. Evaluation metrics for DGMs such as Inception Score, Fr'echet Inception Distance, PrecisionRecall, and Neural Net Divergence try to estim... |
Neural Text Generation A Practical Guide ; Deep learning methods have recently achieved great empirical success on machine translation, dialogue response generation, summarization, and other text generation tasks. At a high level, the technique has been to train endtoend neural network models consisting of an encoder ... |
Generative models with kernel distance in data space ; Generative models dealing with modeling ajoint data distribution are generally either autoencoder or GAN based. Both have their pros and cons, generating blurry images or being unstable in training or prone to mode collapse phenomenon, respectively. The objective ... |
TwoStreamVAN Improving Motion Modeling in Video Generation ; Video generation is an inherently challenging task, as it requires modeling realistic temporal dynamics as well as spatial content. Existing methods entangle the two intrinsically different tasks of motion and content creation in a single generator network, ... |
Long and Diverse Text Generation with Planningbased Hierarchical Variational Model ; Existing neural methods for datatotext generation are still struggling to produce long and diverse texts they are insufficient to model input data dynamically during generation, to capture intersentence coherence, or to generate diver... |
DeepCopy Grounded Response Generation with Hierarchical Pointer Networks ; Recent advances in neural sequencetosequence models have led to promising results for several language generationbased tasks, including dialogue response generation, summarization, and machine translation. However, these models are known to hav... |
Goaldirected Generation of Discrete Structures with Conditional Generative Models ; Despite recent advances, goaldirected generation of structured discrete data remains challenging. For problems such as program synthesis generating source code and materials design generating molecules, finding examples which satisfy d... |
Image to Image Translation Generating maps from satellite images ; Generation of maps from satellite images is conventionally done by a range of tools. Maps became an important part of life whose conversion from satellite images may be a bit expensive but Generative models can pander to this challenge. These models a... |
Graphine A Dataset for Graphaware Terminology Definition Generation ; Precisely defining the terminology is the first step in scientific communication. Developing neural text generation models for definition generation can circumvent the laborintensity curation, further accelerating scientific discovery. Unfortunately... |
In Search of Probeable Generalization Measures ; Understanding the generalization behaviour of deep neural networks is a topic of recent interest that has driven the production of many studies, notably the development and evaluation of generalization explainability measures that quantify model generalization ability. ... |
A Contextual Latent Space Model Subsequence Modulation in Melodic Sequence ; Some generative models for sequences such as music and text allow us to edit only subsequences, given surrounding context sequences, which plays an important part in steering generation interactively. However, editing subsequences mainly invo... |
Context Matters in Semantically Controlled Language Generation for Taskoriented Dialogue Systems ; This work combines information about the dialogue history encoded by pretrained model with a meaning representation of the current system utterance to realize contextual language generation in taskoriented dialogues. We ... |
Physics guided deep learning generative models for crystal materials discovery ; Deep learning based generative models such as deepfake have been able to generate amazing images and videos. However, these models may need significant transformation when applied to generate crystal materials structures in which the buil... |
GoalDirected Story Generation Augmenting Generative Language Models with Reinforcement Learning ; The advent of large pretrained generative language models has provided a common framework for AI story generation via sampling the model to create sequences that continue the story. However, sampling alone is insufficient... |
Training and Tuning Generative Neural Radiance Fields for AttributeConditional 3DAware Face Generation ; 3Daware GANs based on generative neural radiance fields GNeRF have achieved impressive highquality image generation, while preserving strong 3D consistency. The most notable achievements are made in the face genera... |
MultiSource Diffusion Models for Simultaneous Music Generation and Separation ; In this work, we define a diffusionbased generative model capable of both music synthesis and source separation by learning the score of the joint probability density of sources sharing a context. Alongside the classic total inference task... |
Diffusing Gaussian Mixtures for Generating Categorical Data ; Learning a categorical distribution comes with its own set of challenges. A successful approach taken by stateoftheart works is to cast the problem in a continuous domain to take advantage of the impressive performance of the generative models for continuou... |
MakeAnAnimation LargeScale Textconditional 3D Human Motion Generation ; Textguided human motion generation has drawn significant interest because of its impactful applications spanning animation and robotics. Recently, application of diffusion models for motion generation has enabled improvements in the quality of gen... |
SelfConsuming Generative Models Go MAD ; Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train nextgeneration models. Repeating this process creates an autophagous selfconsuming loop whose properties are poorly understood. We condu... |
Deep Generative Models, Synthetic Tabular Data, and Differential Privacy An Overview and Synthesis ; This article provides a comprehensive synthesis of the recent developments in synthetic data generation via deep generative models, focusing on tabular datasets. We specifically outline the importance of synthetic data... |
A meanfield games laboratory for generative modeling ; In this paper, we demonstrate the versatility of meanfield games MFGs as a mathematical framework for explaining, enhancing, and designing generative models. There is a pervasive sense in the generative modeling community that the various flow and diffusionbased g... |
Generalizing the generalized Chaplygin gas ; The generalized Chaplygin gas is characterized by the equation of state p Arhoalpha, with alpha 1 and w 1. We generalize this model to allow for the cases where alpha 1 or w 1. This generalization leads to three new versions of the generalized Chaplygin gas an early p... |
Generation Mass Hierarchy in Superstring Derived Models ; I discuss the problem of generation mass hierarchy in the context of realistic superstring models which are constructed in the free fermionic formulation. These models correspond to models which are compactified on Z2times Z2 orbifold. I suggest that the hierar... |
Emergent general relativity in the tensor models possessing Gaussian classical solutions ; This paper gives a summary of the author's works concerning the emergent general relativity in a particular class of tensor models, which possess Gaussian classical solutions. In general, a classical solution in a tensor model m... |
Generative Model Selection Using a Scalable and SizeIndependent Complex Network Classifier ; Real networks exhibit nontrivial topological features such as heavytailed degree distribution, high clustering, and smallworldness. Researchers have developed several generative models for synthesizing artificial networks that... |
Improving image generative models with human interactions ; GANs provide a framework for training generative models which mimic a data distribution. However, in many cases we wish to train these generative models to optimize some auxiliary objective function within the data it generates, such as making more aesthetica... |
Kernel Mean Matching for Content Addressability of GANs ; We propose a novel procedure which adds contentaddressability to any given unconditional implicit model e.g., a generative adversarial network GAN. The procedure allows users to control the generative process by specifying a set arbitrary size of desired exampl... |
A general model for planebased clustering with loss function ; In this paper, we propose a general model for planebased clustering. The general model contains many existing planebased clustering methods, e.g., kplane clustering kPC, proximal plane clustering PPC, twin support vector clustering TWSVC and its extensions... |
Outfit Generation and Style Extraction via Bidirectional LSTM and Autoencoder ; When creating an outfit, style is a criterion in selecting each fashion item. This means that style can be regarded as a feature of the overall outfit. However, in various previous studies on outfit generation, there have been few methods ... |
What leads to generalization of object proposals ; Object proposal generation is often the first step in many detection models. It is lucrative to train a good proposal model, that generalizes to unseen classes. This could help scaling detection models to larger number of classes with fewer annotations. Motivated by t... |
Learning Neural Generative Dynamics for Molecular Conformation Generation ; We study how to generate molecule conformations i.e., 3D structures from a molecular graph. Traditional methods, such as molecular dynamics, sample conformations via computationally expensive simulations. Recently, machine learning methods hav... |
A Discrete CVAE for Response Generation on ShortText Conversation ; Neural conversation models such as encoderdecoder models are easy to generate bland and generic responses. Some researchers propose to use the conditional variational autoencoderCVAE which maximizes the lower bound on the conditional loglikelihood on ... |
TexttoImage Generation with Attention Based Recurrent Neural Networks ; Conditional image modeling based on textual descriptions is a relatively new domain in unsupervised learning. Previous approaches use a latent variable model and generative adversarial networks. While the formers are approximated by using variatio... |
Automated Diagram Generation to Build Understanding and Usability ; Causal loop and stock and flow diagrams are broadly used in System Dynamics because they help organize relationships and convey meaning. Using the analytical work of Schoenberg 2019 to select what to include in a compressed model, this paper demonstra... |
Conditional Generative Models for Counterfactual Explanations ; Counterfactual instances offer humaninterpretable insight into the local behaviour of machine learning models. We propose a general framework to generate sparse, indistribution counterfactual model explanations which match a desired target prediction with... |
Topical Language Generation using Transformers ; Largescale transformerbased language models LMs demonstrate impressive capabilities in open text generation. However, controlling the generated text's properties such as the topic, style, and sentiment is challenging and often requires significant changes to the model a... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.