{"list":"other","total":74,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"2205.11487","title":"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding","created":"2022-05-23","cited_by_count":6355},{"id":"2203.00667","title":"Generative Adversarial Networks","created":"2022-03-01","cited_by_count":5627},{"id":"2204.02311","title":"PaLM: Scaling Language Modeling with Pathways","created":"2022-04-05","cited_by_count":5326},{"id":"2204.14198","title":"Flamingo: a Visual Language Model for Few-Shot Learning","created":"2022-04-29","cited_by_count":4386},{"id":"1606.03498","title":"Improved Techniques for Training GANs","created":"2016-06-10","cited_by_count":2970},{"id":"1911.08265","title":"Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model","created":"2019-11-19","cited_by_count":1353},{"id":"2209.00796","title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","created":"2022-09-02","cited_by_count":1114},{"id":"1906.02691","title":"An Introduction to Variational Autoencoders","created":"2019-06-06","cited_by_count":1000},{"id":"2203.17003","title":"Equivariant Diffusion for Molecule Generation in 3D","created":"2022-03-31","cited_by_count":677},{"id":"1704.01665","title":"Learning Combinatorial Optimization Algorithms over Graphs","created":"2017-04-05","cited_by_count":561},{"id":"1606.00709","title":"f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization","created":"2016-06-02","cited_by_count":506},{"id":"1904.05046","title":"Generalizing from a Few Examples: A Survey on Few-Shot Learning","created":"2019-04-10","cited_by_count":463},{"id":"1808.01974","title":"A Survey on Deep Transfer Learning","created":"2018-08-06","cited_by_count":419},{"id":"1906.01629","title":"Exact Combinatorial Optimization with Graph Convolutional Neural Networks","created":"2019-06-04","cited_by_count":318},{"id":"2109.05014","title":"An Empirical Study of GPT-3 for Few-Shot Knowledge-Based VQA","created":"2021-09-10","cited_by_count":301},{"id":"2102.09544","title":"Combinatorial optimization and reasoning with graph neural networks","created":"2021-02-18","cited_by_count":268},{"id":"1906.02735","title":"Residual Flows for Invertible Generative Modeling","created":"2019-06-06","cited_by_count":257},{"id":"2103.04922","title":"Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models","created":"2021-03-08","cited_by_count":252},{"id":"2309.10020","title":"Multimodal Foundation Models: From Specialists to General-Purpose Assistants","created":"2023-09-18","cited_by_count":247},{"id":"1810.10659","title":"Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search","created":"2018-10-25","cited_by_count":183},{"id":"2301.13823","title":"Grounding Language Models to Images for Multimodal Inputs and Outputs","created":"2023-01-31","cited_by_count":131},{"id":"2008.12595","title":"Dynamical Variational Autoencoders: A Comprehensive Review","created":"2020-08-28","cited_by_count":128},{"id":"1811.08359","title":"Strong mixed-integer programming formulations for trained neural networks","created":"2018-11-20","cited_by_count":115},{"id":"2006.10643","title":"Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs","created":"2020-06-18","cited_by_count":115},{"id":"2106.07802","title":"GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles","created":"2021-06-08","cited_by_count":107},{"id":"1909.12072","title":"Towards Explainable Artificial Intelligence","created":"2019-09-26","cited_by_count":106},{"id":"2110.10863","title":"Deep Generative Models in Engineering Design: A Review","created":"2021-10-21","cited_by_count":105},{"id":"2208.05740","title":"General Cutting Planes for Bound-Propagation-Based Neural Network Verification","created":"2022-08-11","cited_by_count":103},{"id":"2306.06283","title":"14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon","created":"2023-06-09","cited_by_count":86},{"id":"1802.00850","title":"Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification","created":"2018-02-02","cited_by_count":81},{"id":"2110.07579","title":"Diffusion Normalizing Flow","created":"2021-10-14","cited_by_count":77},{"id":"1902.02119","title":"Mol-CycleGAN - a generative model for molecular optimization","created":"2019-02-06","cited_by_count":66},{"id":"2006.11941","title":"VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data","created":"2020-06-21","cited_by_count":66},{"id":"2206.01201","title":"REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering","created":"2022-06-02","cited_by_count":59},{"id":"2209.05710","title":"MDM: Molecular Diffusion Model for 3D Molecule Generation","created":"2022-09-13","cited_by_count":58},{"id":"1707.09676","title":"Model-Free Renewable Scenario Generation Using Generative Adversarial Networks","created":"2017-07-30","cited_by_count":51},{"id":"2105.13645","title":"Learning to Select Cuts for Efficient Mixed-Integer Programming","created":"2021-05-28","cited_by_count":48},{"id":"2109.06407","title":"Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling","created":"2021-09-14","cited_by_count":41},{"id":"1907.03140","title":"ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs","created":"2019-07-06","cited_by_count":33},{"id":"2302.10909","title":"Multi-modal Machine Learning in Engineering Design: A Review and Future Directions","created":"2023-02-14","cited_by_count":27},{"id":"2111.07171","title":"Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning","created":"2021-11-13","cited_by_count":24},{"id":"2310.02807","title":"A Deep Instance Generative Framework for MILP Solvers Under Limited Data Availability","created":"2023-10-04","cited_by_count":21},{"id":"1512.05702","title":"Synthesis of recurrent neural networks for dynamical system simulation","created":"2015-12-17","cited_by_count":16},{"id":"1705.05584","title":"Metaheuristic Design of Feedforward Neural Networks: A Review of Two Decades of Research","created":"2017-05-16","cited_by_count":16},{"id":"1904.01934","title":"Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming","created":"2019-04-03","cited_by_count":14},{"id":"2005.10902","title":"Global Optimization of Gaussian processes","created":"2020-05-21","cited_by_count":14},{"id":"2111.13557","title":"On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments","created":"2021-11-26","cited_by_count":14},{"id":"2102.05875","title":"Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems","created":"2021-02-11","cited_by_count":13},{"id":"2206.06965","title":"Deep Reinforcement Learning for Exact Combinatorial Optimization: Learning to Branch","created":"2022-06-14","cited_by_count":12},{"id":"2010.03651","title":"Learning the aerodynamic design of supercritical airfoils through deep reinforcement learning","created":"2020-10-05","cited_by_count":10},{"id":"2207.12051","title":"Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks","created":"2022-07-25","cited_by_count":10},{"id":"2208.00778","title":"SFILES 2.0: An extended text-based flowsheet representation","created":"2022-07-25","cited_by_count":10},{"id":"2208.00859","title":"Learning from flowsheets: A generative transformer model for autocompletion of flowsheets","created":"2022-08-01","cited_by_count":10},{"id":"2308.07822","title":"Deep reinforcement learning for process design: Review and perspective","created":"2023-08-15","cited_by_count":10},{"id":"2111.10489","title":"Modeling Design and Control Problems Involving Neural Network Surrogates","created":"2021-11-20","cited_by_count":9},{"id":"1802.00868","title":"Bayesian Renewables Scenario Generation via Deep Generative Networks","created":"2018-02-02","cited_by_count":7},{"id":"2207.13779","title":"Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction","created":"2022-07-27","cited_by_count":7},{"id":"2005.08968","title":"Deep Learning and Knowledge-Based Methods for Computer Aided Molecular Design -- Toward a Unified Approach: State-of-the-Art and Future Directions","created":"2020-05-18","cited_by_count":6},{"id":"2108.05242","title":"Integrating process design and control using reinforcement learning","created":"2021-08-11","cited_by_count":6},{"id":"2206.00619","title":"Graph Machine Learning for Design of High-Octane Fuels","created":"2022-06-01","cited_by_count":6},{"id":"2003.00264","title":"Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems","created":"2020-02-29","cited_by_count":5},{"id":"2002.12486","title":"Distributionally Robust Chance Constrained Programming with Generative Adversarial Networks (GANs)","created":"2020-02-28","cited_by_count":4},{"id":"2110.03760","title":"Design Strategy Network: A deep hierarchical framework to represent generative design strategies in complex action spaces","created":"2021-10-07","cited_by_count":4},{"id":"2306.03099","title":"CrystalGPT: Enhancing system-to-system transferability in crystallization prediction and control using time-series-transformers","created":"2023-05-31","cited_by_count":4},{"id":"1701.03978","title":"Computer-aided molecular design: An introduction and review of tools, applications, and solution techniques","created":"2017-01-15","cited_by_count":3},{"id":"2009.00665","title":"On the Impact of Deep Learning-based Time-series Forecasts on Multistage Stochastic Programming Policies","created":"2020-09-01","cited_by_count":3},{"id":"2109.09810","title":"Robust economic model predictive control with zone tracking","created":"2021-09-20","cited_by_count":3},{"id":"2207.03844","title":"On data-driven chance constraint learning for mixed-integer optimization problems","created":"2022-07-08","cited_by_count":3},{"id":"2003.09664","title":"Particle Swarm Metaheuristics for Robust Optimisation with Implementation Uncertainty","created":"2020-03-21","cited_by_count":2}],"next_cursor":null}