{"list":"other","total":87,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"1708.07747","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","created":"2017-08-25","cited_by_count":4349},{"id":"1606.08415","title":"Gaussian Error Linear Units (GELUs)","created":"2016-06-27","cited_by_count":3502},{"id":"1803.03635","title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","created":"2018-03-09","cited_by_count":2262},{"id":"1712.09913","title":"Visualizing the Loss Landscape of Neural Nets","created":"2017-12-28","cited_by_count":1130},{"id":"2301.00774","title":"SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot","created":"2023-01-02","cited_by_count":1121},{"id":"1810.02340","title":"SNIP: Single-shot Network Pruning based on Connection Sensitivity","created":"2018-10-04","cited_by_count":812},{"id":"2306.11695","title":"A Simple and Effective Pruning Approach for Large Language Models","created":"2023-06-20","cited_by_count":800},{"id":"1707.06168","title":"Channel Pruning for Accelerating Very Deep Neural Networks","created":"2017-07-19","cited_by_count":702},{"id":"2305.11627","title":"LLM-Pruner: On the Structural Pruning of Large Language Models","created":"2023-05-19","cited_by_count":650},{"id":"2003.03033","title":"What is the State of Neural Network Pruning?","created":"2020-03-06","cited_by_count":583},{"id":"1610.01145","title":"Error bounds for approximations with deep ReLU networks","created":"2016-10-03","cited_by_count":574},{"id":"2102.00554","title":"Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks","created":"2021-01-31","cited_by_count":527},{"id":"1707.06342","title":"ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression","created":"2017-07-20","cited_by_count":442},{"id":"2310.06694","title":"Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning","created":"2023-10-10","cited_by_count":415},{"id":"1402.1869","title":"On the Number of Linear Regions of Deep Neural Networks","created":"2014-02-08","cited_by_count":381},{"id":"1606.05336","title":"On the Expressive Power of Deep Neural Networks","created":"2016-06-16","cited_by_count":306},{"id":"1611.01491","title":"Understanding Deep Neural Networks with Rectified Linear Units","created":"2016-11-04","cited_by_count":257},{"id":"1306.0543","title":"Predicting Parameters in Deep Learning","created":"2013-06-03","cited_by_count":224},{"id":"1903.06758","title":"Algorithms for Verifying Deep Neural Networks","created":"2019-03-15","cited_by_count":221},{"id":"1911.05248","title":"What Do Compressed Deep Neural Networks Forget?","created":"2019-11-13","cited_by_count":171},{"id":"1901.09021","title":"Complexity of Linear Regions in Deep Networks","created":"2019-01-25","cited_by_count":167},{"id":"2010.03058","title":"Characterising Bias in Compressed Models","created":"2020-10-06","cited_by_count":167},{"id":"1711.02114","title":"Bounding and Counting Linear Regions of Deep Neural Networks","created":"2017-11-06","cited_by_count":161},{"id":"1906.00904","title":"Deep ReLU Networks Have Surprisingly Few Activation Patterns","created":"2019-06-03","cited_by_count":161},{"id":"1706.07351","title":"An approach to reachability analysis for feed-forward ReLU neural networks","created":"2017-06-22","cited_by_count":127},{"id":"1710.11278","title":"Approximating Continuous Functions by ReLU Nets of Minimal Width","created":"2017-10-31","cited_by_count":122},{"id":"1811.08359","title":"Strong mixed-integer programming formulations for trained neural networks","created":"2018-11-20","cited_by_count":115},{"id":"1705.01040","title":"Maximum Resilience of Artificial Neural Networks","created":"2017-04-28","cited_by_count":97},{"id":"1312.6098","title":"On the number of response regions of deep feed forward networks with piece-wise linear activations","created":"2013-12-20","cited_by_count":94},{"id":"1509.08101","title":"Representation Benefits of Deep Feedforward Networks","created":"2015-09-27","cited_by_count":86},{"id":"1910.00744","title":"Reverse-Engineering Deep ReLU Networks","created":"2019-10-02","cited_by_count":83},{"id":"2003.01794","title":"Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection","created":"2020-03-03","cited_by_count":70},{"id":"1809.03008","title":"Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability","created":"2018-09-09","cited_by_count":67},{"id":"2103.03014","title":"Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy","created":"2021-03-04","cited_by_count":62},{"id":"2010.12001","title":"Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing","created":"2020-10-22","cited_by_count":61},{"id":"2007.01429","title":"The Global Landscape of Neural Networks: An Overview","created":"2020-07-02","cited_by_count":57},{"id":"2105.14835","title":"Towards Lower Bounds on the Depth of ReLU Neural Networks","created":"2021-05-31","cited_by_count":56},{"id":"2011.10219","title":"Certified Monotonic Neural Networks","created":"2020-11-20","cited_by_count":51},{"id":"2205.13574","title":"Pruning has a disparate impact on model accuracy","created":"2022-05-26","cited_by_count":44},{"id":"2203.04466","title":"The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks","created":"2022-03-09","cited_by_count":41},{"id":"2302.14623","title":"Fast as CHITA: Neural Network Pruning with Combinatorial Optimization","created":"2023-02-28","cited_by_count":40},{"id":"2102.04373","title":"Partition-based formulations for mixed-integer optimization of trained ReLU neural networks","created":"2021-02-08","cited_by_count":39},{"id":"2305.00241","title":"When Deep Learning Meets Polyhedral Theory: A Survey","created":"2023-04-29","cited_by_count":39},{"id":"2104.08135","title":"Sharp bounds for the number of regions of maxout networks and vertices of Minkowski sums","created":"2021-04-16","cited_by_count":37},{"id":"2302.12553","title":"Lower Bounds on the Depth of Integral ReLU Neural Networks via Lattice Polytopes","created":"2023-02-24","cited_by_count":36},{"id":"2008.09052","title":"On transversality of bent hyperplane arrangements and the topological expressiveness of ReLU neural networks","created":"2020-08-20","cited_by_count":35},{"id":"2202.02414","title":"OMLT: Optimization & Machine Learning Toolkit","created":"2022-02-04","cited_by_count":35},{"id":"2001.00218","title":"Lossless Compression of Deep Neural Networks","created":"2020-01-01","cited_by_count":34},{"id":"1801.07114","title":"Global Deterministic Optimization with Artificial Neural Networks Embedded","created":"2018-01-22","cited_by_count":33},{"id":"1907.03140","title":"ReLU Networks as Surrogate Models in Mixed-Integer Linear Programs","created":"2019-07-06","cited_by_count":33},{"id":"2111.04469","title":"Mixed-Integer Optimization with Constraint Learning","created":"2021-11-04","cited_by_count":31},{"id":"2110.02121","title":"Optimization with Constraint Learning: A Framework and Survey","created":"2021-10-05","cited_by_count":28},{"id":"1911.09461","title":"JANOS: An Integrated Predictive and Prescriptive Modeling Framework","created":"2019-11-21","cited_by_count":27},{"id":"2010.03258","title":"Global Optimization of Objective Functions Represented by ReLU Networks","created":"2020-10-07","cited_by_count":24},{"id":"1810.03370","title":"Empirical Bounds on Linear Regions of Deep Rectifier Networks","created":"2018-10-08","cited_by_count":23},{"id":"2009.09936","title":"Prune Responsibly","created":"2020-09-10","cited_by_count":20},{"id":"2102.07804","title":"Scaling Up Exact Neural Network Compression by ReLU Stability","created":"2021-02-15","cited_by_count":19},{"id":"2502.06283","title":"On the Expressiveness of Rational ReLU Neural Networks With Bounded Depth","created":"2025-02-10","cited_by_count":18},{"id":"1806.01918","title":"A Framework for the construction of upper bounds on the number of affine linear regions of ReLU feed-forward neural networks","created":"2018-06-05","cited_by_count":15},{"id":"2003.07939","title":"Neural Networks for Encoding Dynamic Security-Constrained Optimal Power Flow","created":"2020-03-17","cited_by_count":15},{"id":"2107.00379","title":"On the Expected Complexity of Maxout Networks","created":"2021-07-01","cited_by_count":15},{"id":"2307.09212","title":"How Many Neurons Does it Take to Approximate the Maximum?","created":"2023-07-18","cited_by_count":12},{"id":"1905.11428","title":"Equivalent and Approximate Transformations of Deep Neural Networks","created":"2019-05-27","cited_by_count":11},{"id":"2007.06567","title":"Lossless Compression of Structured Convolutional Models via Lifting","created":"2020-07-13","cited_by_count":11},{"id":"2205.14189","title":"Optimizing Objective Functions from Trained ReLU Neural Networks via Sampling","created":"2022-05-27","cited_by_count":11},{"id":"2401.03451","title":"Optimization Over Trained Neural Networks: Taking a Relaxing Walk","created":"2024-01-07","cited_by_count":10},{"id":"2402.13937","title":"Verifying message-passing neural networks via topology-based bounds tightening","created":"2024-02-21","cited_by_count":10},{"id":"2206.02976","title":"Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm","created":"2022-06-07","cited_by_count":9},{"id":"2312.01228","title":"Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design","created":"2023-12-02","cited_by_count":9},{"id":"2411.03006","title":"Neural Networks and (Virtual) Extended Formulations","created":"2024-11-05","cited_by_count":9},{"id":"2301.07966","title":"Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions","created":"2023-01-19","cited_by_count":8},{"id":"2312.08074","title":"PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs","created":"2023-12-13","cited_by_count":8},{"id":"2408.17163","title":"The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information","created":"2024-08-30","cited_by_count":8},{"id":"1708.00050","title":"Nonconvex piecewise linear functions: Advanced formulations and simple modeling tools","created":"2017-07-31","cited_by_count":7},{"id":"2312.16699","title":"Computational Tradeoffs of Optimization-Based Bound Tightening in ReLU Networks","created":"2023-12-27","cited_by_count":6},{"id":"2401.05280","title":"Bound Tightening using Rolling-Horizon Decomposition for Neural Network Verification","created":"2024-01-10","cited_by_count":6},{"id":"2403.17874","title":"Scaling Mixed-Integer Programming for Certification of Neural Network Controllers Using Bounds Tightening","created":"2024-03-26","cited_by_count":6},{"id":"2111.08577","title":"Neuron-based Pruning of Deep Neural Networks with Better Generalization using Kronecker Factored Curvature Approximation","created":"2021-11-16","cited_by_count":4},{"id":"2206.14056","title":"Deep Neural Networks pruning via the Structured Perspective Regularization","created":"2022-06-28","cited_by_count":4},{"id":"2201.12795","title":"Training Thinner and Deeper Neural Networks: Jumpstart Regularization","created":"2022-01-30","cited_by_count":3},{"id":"2307.07457","title":"Structured Pruning of Neural Networks for Constraints Learning","created":"2023-07-14","cited_by_count":3},{"id":"2502.03016","title":"An analysis of optimization problems involving ReLU neural networks","created":"2025-02-05","cited_by_count":2},{"id":"2503.17506","title":"Optimization over Trained Neural Networks: Difference-of-Convex Algorithm and Application to Data Center Scheduling","created":"2025-03-21","cited_by_count":2},{"id":"2406.10065","title":"An Extended Validity Domain for Constraint Learning","created":"2024-06-14","cited_by_count":1}],"next_cursor":null}