{"list":"other","total":68,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"1707.06347","title":"Proximal Policy Optimization Algorithms","created":"2017-07-20","cited_by_count":15215},{"id":"1703.03400","title":"Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks","created":"2017-03-09","cited_by_count":4897},{"id":"1411.4038","title":"Fully Convolutional Networks for Semantic Segmentation","created":"2014-11-14","cited_by_count":4859},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"1509.02971","title":"Continuous control with deep reinforcement learning","created":"2015-09-09","cited_by_count":3407},{"id":"1312.5602","title":"Playing Atari with Deep Reinforcement Learning","created":"2013-12-19","cited_by_count":3274},{"id":"1409.3215","title":"Sequence to Sequence Learning with Neural Networks","created":"2014-09-10","cited_by_count":2986},{"id":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"id":"1510.00149","title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","created":"2015-10-01","cited_by_count":2329},{"id":"1602.01783","title":"Asynchronous Methods for Deep Reinforcement Learning","created":"2016-02-04","cited_by_count":2258},{"id":"1712.05877","title":"Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference","created":"2017-12-15","cited_by_count":1593},{"id":"1912.06680","title":"Dota 2 with Large Scale Deep Reinforcement Learning","created":"2019-12-13","cited_by_count":1103},{"id":"1609.09106","title":"HyperNetworks","created":"2016-09-27","cited_by_count":1027},{"id":"1803.02999","title":"On First-Order Meta-Learning Algorithms","created":"2018-03-08","cited_by_count":1018},{"id":"1704.04760","title":"In-Datacenter Performance Analysis of a Tensor Processing Unit","created":"2017-04-16","cited_by_count":618},{"id":"1911.10635","title":"Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms","created":"2019-11-24","cited_by_count":567},{"id":"1906.12340","title":"Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty","created":"2019-06-28","cited_by_count":514},{"id":"1901.08652","title":"Learning agile and dynamic motor skills for legged robots","created":"2019-01-24","cited_by_count":504},{"id":"1703.09039","title":"Efficient Processing of Deep Neural Networks: A Tutorial and Survey","created":"2017-03-27","cited_by_count":434},{"id":"1610.00633","title":"Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates","created":"2016-10-03","cited_by_count":353},{"id":"1604.06057","title":"Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation","created":"2016-04-20","cited_by_count":286},{"id":"2006.03511","title":"Unsupervised Translation of Programming Languages","created":"2020-06-05","cited_by_count":263},{"id":"1810.03548","title":"Meta-Learning: A Survey","created":"2018-10-08","cited_by_count":256},{"id":"1812.03372","title":"What is the Effect of Importance Weighting in Deep Learning?","created":"2018-12-08","cited_by_count":251},{"id":"1704.02532","title":"Deep Reinforcement Learning framework for Autonomous Driving","created":"2017-04-08","cited_by_count":225},{"id":"1709.06182","title":"A Survey of Machine Learning for Big Code and Naturalness","created":"2017-09-18","cited_by_count":221},{"id":"1712.01208","title":"The Case for Learned Index Structures","created":"2017-12-04","cited_by_count":188},{"id":"1708.04485","title":"SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks","created":"2017-05-23","cited_by_count":135},{"id":"1810.01963","title":"Learning Scheduling Algorithms for Data Processing Clusters","created":"2018-10-03","cited_by_count":134},{"id":"1709.04555","title":"Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network","created":"2017-09-13","cited_by_count":103},{"id":"1802.03691","title":"Tree-to-tree Neural Networks for Program Translation","created":"2018-02-11","cited_by_count":93},{"id":"2005.00406","title":"GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning","created":"2020-04-30","cited_by_count":89},{"id":"1706.04972","title":"Device Placement Optimization with Reinforcement Learning","created":"2017-06-13","cited_by_count":86},{"id":"2004.10746","title":"Chip Placement with Deep Reinforcement Learning","created":"2020-04-22","cited_by_count":83},{"id":"2001.01808","title":"AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs","created":"2020-01-06","cited_by_count":51},{"id":"1911.04021","title":"DRiLLS: Deep Reinforcement Learning for Logic Synthesis","created":"2019-11-11","cited_by_count":47},{"id":"1808.07412","title":"Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks","created":"2018-08-21","cited_by_count":43},{"id":"2009.02010","title":"ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement Learning","created":"2020-09-04","cited_by_count":35},{"id":"1804.05714","title":"Developing Synthesis Flows Without Human Knowledge","created":"2018-04-16","cited_by_count":29},{"id":"1803.02329","title":"Learning Memory Access Patterns","created":"2018-03-06","cited_by_count":28},{"id":"1808.04706","title":"Neural Machine Translation Inspired Binary Code Similarity Comparison beyond Function Pairs","created":"2018-08-08","cited_by_count":25},{"id":"1907.12952","title":"Pyramid: Machine Learning Framework to Estimate the Optimal Timing and Resource Usage of a High-Level Synthesis Design","created":"2019-07-29","cited_by_count":17},{"id":"2102.01723","title":"Apollo: Transferable Architecture Exploration","created":"2021-02-02","cited_by_count":17},{"id":"1911.05289","title":"The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design","created":"2019-11-13","cited_by_count":15},{"id":"2010.04017","title":"DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates","created":"2020-10-08","cited_by_count":14},{"id":"1906.07181","title":"Learning Execution through Neural Code Fusion","created":"2019-06-17","cited_by_count":13},{"id":"1812.00134","title":"Semi-Online Bipartite Matching","created":"2018-12-01","cited_by_count":12},{"id":"1907.10515","title":"BagNet: Berkeley Analog Generator with Layout Optimizer Boosted with Deep Neural Networks","created":"2019-07-23","cited_by_count":11},{"id":"1910.01578","title":"GDP: Generalized Device Placement for Dataflow Graphs","created":"2019-09-28","cited_by_count":10},{"id":"1905.12799","title":"Reinforcement Learning and Adaptive Sampling for Optimized DNN Compilation","created":"2019-05-30","cited_by_count":9},{"id":"2009.00871","title":"HL-Pow: A Learning-Based Power Modeling Framework for High-Level Synthesis","created":"2020-09-02","cited_by_count":9},{"id":"2102.04199","title":"MetaTune: Meta-Learning Based Cost Model for Fast and Efficient Auto-tuning Frameworks","created":"2021-02-08","cited_by_count":9},{"id":"1403.1631","title":"Unsupervised Anomaly-based Malware Detection using Hardware Features","created":"2014-03-07","cited_by_count":8},{"id":"1702.06064","title":"RESPARC: A Reconfigurable and Energy-Efficient Architecture with Memristive Crossbars for Deep Spiking Neural Networks","created":"2017-02-20","cited_by_count":8},{"id":"1810.08869","title":"Learning-based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems","created":"2018-10-20","cited_by_count":8},{"id":"1905.03852","title":"Machine Learning Based Routing Congestion Prediction in FPGA High-Level Synthesis","created":"2019-05-06","cited_by_count":8},{"id":"2007.07298","title":"Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning","created":"2020-07-14","cited_by_count":7},{"id":"1902.00478","title":"Approximate Logic Synthesis: A Reinforcement Learning-Based Technology Mapping Approach","created":"2019-02-01","cited_by_count":6},{"id":"1906.09889","title":"Improving Branch Prediction By Modeling Global History with Convolutional Neural Networks","created":"2019-06-20","cited_by_count":4},{"id":"1804.08733","title":"goSLP: Globally Optimized Superword Level Parallelism Framework","created":"2018-04-23","cited_by_count":3},{"id":"1911.03053","title":"Electric Analog Circuit Design with Hypernetworks and a Differential Simulator","created":"2019-11-08","cited_by_count":3},{"id":"1905.04423","title":"Optimizing Routerless Network-on-Chip Designs: An Innovative Learning-Based Framework","created":"2019-05-11","cited_by_count":2},{"id":"2011.07160","title":"Phoebe: Reuse-Aware Online Caching with Reinforcement Learning for Emerging Storage Models","created":"2020-11-13","cited_by_count":2},{"id":"1712.00905","title":"Data Cache Prefetching with Perceptron Learning","created":"2017-12-04","cited_by_count":1},{"id":"1905.07653","title":"A Case Study: Exploiting Neural Machine Translation to Translate CUDA to OpenCL","created":"2019-05-18","cited_by_count":1},{"id":"1906.07840","title":"A Static Analysis-based Cross-Architecture Performance Prediction Using Machine Learning","created":"2019-06-18","cited_by_count":1}],"next_cursor":null}