{"list":"other","total":120,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"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":"2203.02155","title":"Training language models to follow instructions with human feedback","created":"2022-03-04","cited_by_count":15971},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"2105.05233","title":"Diffusion Models Beat GANs on Image Synthesis","created":"2021-05-11","cited_by_count":8255},{"id":"2207.12598","title":"Classifier-Free Diffusion Guidance","created":"2022-07-26","cited_by_count":5847},{"id":"2210.02747","title":"Flow Matching for Generative Modeling","created":"2022-10-06","cited_by_count":5702},{"id":"1506.02142","title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","created":"2015-06-06","cited_by_count":4448},{"id":"1312.6114","title":"Auto-Encoding Variational Bayes","created":"2013-12-20","cited_by_count":4053},{"id":"1612.01474","title":"Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles","created":"2016-12-05","cited_by_count":3926},{"id":"1611.01144","title":"Categorical Reparameterization with Gumbel-Softmax","created":"2016-11-03","cited_by_count":2739},{"id":"1308.3432","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","created":"2013-08-15","cited_by_count":2079},{"id":"2005.01643","title":"Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems","created":"2020-05-04","cited_by_count":1920},{"id":"1806.09055","title":"DARTS: Differentiable Architecture Search","created":"2018-06-24","cited_by_count":1798},{"id":"1611.01578","title":"Neural Architecture Search with Reinforcement Learning","created":"2016-11-05","cited_by_count":1499},{"id":"2001.06782","title":"Gradient Surgery for Multi-Task Learning","created":"2020-01-19","cited_by_count":1256},{"id":"1812.05905","title":"Soft Actor-Critic Algorithms and Applications","created":"2018-12-13","cited_by_count":1240},{"id":"1609.04747","title":"An overview of gradient descent optimization algorithms","created":"2016-09-15","cited_by_count":1033},{"id":"2309.03409","title":"Large Language Models as Optimizers","created":"2023-09-07","cited_by_count":864},{"id":"1711.02257","title":"GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks","created":"2017-11-07","cited_by_count":834},{"id":"1912.02164","title":"Plug and Play Language Models: A Simple Approach to Controlled Text Generation","created":"2019-12-04","cited_by_count":799},{"id":"1610.02415","title":"Automatic chemical design using a data-driven continuous representation of molecules","created":"2016-10-07","cited_by_count":765},{"id":"1807.02811","title":"A Tutorial on Bayesian Optimization","created":"2018-07-08","cited_by_count":744},{"id":"1608.04471","title":"Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm","created":"2016-08-16","cited_by_count":507},{"id":"1511.02222","title":"Deep Kernel Learning","created":"2015-11-06","cited_by_count":445},{"id":"1807.01613","title":"Conditional Neural Processes","created":"2018-07-04","cited_by_count":426},{"id":"2106.04399","title":"Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation","created":"2021-06-08","cited_by_count":392},{"id":"2006.10108","title":"Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness","created":"2020-06-17","cited_by_count":389},{"id":"2306.15687","title":"Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale","created":"2023-06-23","cited_by_count":379},{"id":"2102.08363","title":"COMBO: Conservative Offline Model-Based Policy Optimization","created":"2021-02-16","cited_by_count":370},{"id":"1605.09782","title":"Adversarial Feature Learning","created":"2016-05-31","cited_by_count":350},{"id":"1811.09621","title":"GuacaMol: Benchmarking Models for De Novo Molecular Design","created":"2018-11-22","cited_by_count":297},{"id":"2111.09266","title":"GFlowNet Foundations","created":"2021-11-17","cited_by_count":290},{"id":"2304.14772","title":"Multisample Flow Matching: Straightening Flows with Minibatch Couplings","created":"2023-04-28","cited_by_count":278},{"id":"2102.09548","title":"Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development","created":"2021-02-18","cited_by_count":264},{"id":"2201.13259","title":"Trajectory balance: Improved credit assignment in GFlowNets","created":"2022-01-31","cited_by_count":251},{"id":"2203.01387","title":"A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems","created":"2022-03-02","cited_by_count":231},{"id":"2002.04504","title":"pymoo: Multi-objective Optimization in Python","created":"2020-01-22","cited_by_count":200},{"id":"2203.04115","title":"Biological Sequence Design with GFlowNets","created":"2022-03-02","cited_by_count":174},{"id":"1808.07233","title":"Neural Architecture Optimization","created":"2018-08-22","cited_by_count":167},{"id":"2307.08698","title":"Flow Matching in Latent Space","created":"2023-07-17","cited_by_count":160},{"id":"2305.20009","title":"Protein Design with Guided Discrete Diffusion","created":"2023-05-31","cited_by_count":157},{"id":"2202.13903","title":"Bayesian Structure Learning with Generative Flow Networks","created":"2022-02-28","cited_by_count":153},{"id":"2209.12782","title":"Learning GFlowNets from partial episodes for improved convergence and stability","created":"2022-09-26","cited_by_count":132},{"id":"2402.05841","title":"Dirichlet Flow Matching with Applications to DNA Sequence Design","created":"2024-02-08","cited_by_count":115},{"id":"2202.01361","title":"Generative Flow Networks for Discrete Probabilistic Modeling","created":"2022-02-03","cited_by_count":114},{"id":"2109.06716","title":"HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO","created":"2021-09-14","cited_by_count":112},{"id":"1901.10060","title":"Conditioning by adaptive sampling for robust design","created":"2019-01-29","cited_by_count":111},{"id":"2402.04845","title":"AlphaFold Meets Flow Matching for Generating Protein Ensembles","created":"2024-02-07","cited_by_count":110},{"id":"2210.00580","title":"GFlowNets and variational inference","created":"2022-10-02","cited_by_count":106},{"id":"2102.08501","title":"DEUP: Direct Epistemic Uncertainty Prediction","created":"2021-02-16","cited_by_count":104},{"id":"2203.12742","title":"Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders","created":"2022-03-23","cited_by_count":102},{"id":"2210.12765","title":"Multi-Objective GFlowNets","created":"2022-10-23","cited_by_count":101},{"id":"2301.12594","title":"A theory of continuous generative flow networks","created":"2023-01-30","cited_by_count":101},{"id":"2311.13443","title":"Guided Flows for Generative Modeling and Decision Making","created":"2023-11-22","cited_by_count":101},{"id":"2402.18567","title":"Diffusion Language Models Are Versatile Protein Learners","created":"2024-02-28","cited_by_count":98},{"id":"2202.08450","title":"Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization","created":"2022-02-17","cited_by_count":97},{"id":"2206.07632","title":"Exploring Chemical Space with Score-based Out-of-distribution Generation","created":"2022-06-06","cited_by_count":95},{"id":"2107.06882","title":"Conservative Objective Models for Effective Offline Model-Based Optimization","created":"2021-07-14","cited_by_count":88},{"id":"1912.13464","title":"Model Inversion Networks for Model-Based Optimization","created":"2019-12-31","cited_by_count":83},{"id":"1308.4008","title":"A Literature Survey of Benchmark Functions For Global Optimization Problems","created":"2013-08-19","cited_by_count":77},{"id":"2206.02096","title":"PEER: A Comprehensive and Multi-Task Benchmark for Protein Sequence Understanding","created":"2022-06-05","cited_by_count":77},{"id":"2306.07180","title":"Diffusion Models for Black-Box Optimization","created":"2023-06-12","cited_by_count":76},{"id":"2502.15657","title":"Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?","created":"2025-02-21","cited_by_count":75},{"id":"1909.11639","title":"ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots","created":"2019-09-25","cited_by_count":72},{"id":"2405.20971","title":"Amortizing intractable inference in diffusion models for vision, language, and control","created":"2024-05-31","cited_by_count":72},{"id":"2009.12867","title":"An Easy-to-use Real-world Multi-objective Optimization Problem Suite","created":"2020-09-27","cited_by_count":71},{"id":"2404.11599","title":"Variational Bayesian Last Layers","created":"2024-04-17","cited_by_count":69},{"id":"2306.04542","title":"On the Design Fundamentals of Diffusion Models: A Survey","created":"2023-06-07","cited_by_count":60},{"id":"2109.10469","title":"Differentiable Scaffolding Tree for Molecular Optimization","created":"2021-09-22","cited_by_count":53},{"id":"2006.08052","title":"Autofocused oracles for model-based design","created":"2020-06-14","cited_by_count":50},{"id":"2209.14803","title":"polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics","created":"2022-09-29","cited_by_count":48},{"id":"2310.02710","title":"Local Search GFlowNets","created":"2023-10-04","cited_by_count":48},{"id":"2110.14188","title":"RoMA: Robust Model Adaptation for Offline Model-based Optimization","created":"2021-10-27","cited_by_count":39},{"id":"2102.07970","title":"Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation","created":"2021-02-16","cited_by_count":38},{"id":"2306.12360","title":"Protein Discovery with Discrete Walk-Jump Sampling","created":"2023-06-08","cited_by_count":38},{"id":"2206.10786","title":"Generative Pretraining for Black-Box Optimization","created":"2022-06-22","cited_by_count":36},{"id":"2209.07507","title":"Bidirectional Learning for Offline Infinite-width Model-based Optimization","created":"2022-09-15","cited_by_count":35},{"id":"2402.14547","title":"OmniPred: Language Models as Universal Regressors","created":"2024-02-22","cited_by_count":34},{"id":"2204.04213","title":"Structure-aware Protein Self-supervised Learning","created":"2022-04-06","cited_by_count":33},{"id":"2211.11349","title":"Data-Driven Offline Decision-Making via Invariant Representation Learning","created":"2022-11-21","cited_by_count":31},{"id":"2110.11346","title":"Data-Driven Offline Optimization For Architecting Hardware Accelerators","created":"2021-10-20","cited_by_count":29},{"id":"2309.11600","title":"Importance-aware Co-teaching for Offline Model-based Optimization","created":"2023-09-20","cited_by_count":28},{"id":"2310.02823","title":"Learning to Scale Logits for Temperature-Conditional GFlowNets","created":"2023-10-04","cited_by_count":27},{"id":"2207.11719","title":"Gradient-based Bi-level Optimization for Deep Learning: A Survey","created":"2022-07-24","cited_by_count":26},{"id":"2309.11592","title":"Parallel-mentoring for Offline Model-based Optimization","created":"2023-09-20","cited_by_count":26},{"id":"2409.06744","title":"ProteinBench: A Holistic Evaluation of Protein Foundation Models","created":"2024-09-10","cited_by_count":25},{"id":"2306.03111","title":"Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences","created":"2023-06-05","cited_by_count":23},{"id":"2210.12928","title":"GFlowOut: Dropout with Generative Flow Networks","created":"2022-10-24","cited_by_count":22},{"id":"2310.19961","title":"ExPT: Synthetic Pretraining for Few-Shot Experimental Design","created":"2023-10-30","cited_by_count":22},{"id":"2307.00494","title":"Improving Protein Optimization with Smoothed Fitness Landscapes","created":"2023-07-02","cited_by_count":21},{"id":"2301.02931","title":"Bidirectional Learning for Offline Model-based Biological Sequence Design","created":"2023-01-07","cited_by_count":20},{"id":"2405.05349","title":"Offline Model-Based Optimization via Policy-Guided Gradient Search","created":"2024-05-08","cited_by_count":19},{"id":"2208.04321","title":"Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment","created":"2022-08-08","cited_by_count":17},{"id":"2412.03718","title":"ParetoFlow: Guided Flows in Multi-Objective Optimization","created":"2024-12-04","cited_by_count":17},{"id":"2407.04493","title":"PROUD: PaRetO-gUided Diffusion Model for Multi-objective Generation","created":"2024-07-05","cited_by_count":16},{"id":"2303.06275","title":"A Systematic Study of Joint Representation Learning on Protein Sequences and Structures","created":"2023-03-11","cited_by_count":15},{"id":"2407.01624","title":"Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization","created":"2024-06-29","cited_by_count":15},{"id":"2406.03722","title":"Offline Multi-Objective Optimization","created":"2024-06-06","cited_by_count":14}],"next_cursor":"100"}