Recurrent Parameter Generators
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[1] Ross B. Girshick,et al. Fast and Accurate Model Scaling , 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Shilin He,et al. Multi-Task Learning with Shared Encoder for Non-Autoregressive Machine Translation , 2020, NAACL.
[3] S. Gelly,et al. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale , 2020, ICLR.
[4] Samy Wu Fung,et al. Fixed Point Networks: Implicit Depth Models with Jacobian-Free Backprop , 2021, ArXiv.
[5] Xiangyu Zhang,et al. Implicit Feature Pyramid Network for Object Detection , 2020, ArXiv.
[6] Mark Chen,et al. Scaling Laws for Autoregressive Generative Modeling , 2020, ArXiv.
[7] Erich Elsen,et al. Sparse GPU Kernels for Deep Learning , 2020, SC20: International Conference for High Performance Computing, Networking, Storage and Analysis.
[8] Vladlen Koltun,et al. Multiscale Deep Equilibrium Models , 2020, NeurIPS.
[9] Mark Chen,et al. Language Models are Few-Shot Learners , 2020, NeurIPS.
[10] Ashish Khetan,et al. PruneNet: Channel Pruning via Global Importance , 2020, ArXiv.
[11] Yuandong Tian,et al. FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Jose Javier Gonzalez Ortiz,et al. What is the State of Neural Network Pruning? , 2020, MLSys.
[13] Lihi Zelnik-Manor,et al. Knapsack Pruning with Inner Distillation , 2020, ArXiv.
[14] Thang D. Bui,et al. Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights , 2020, NeurIPS.
[15] Stella X. Yu,et al. Orthogonal Convolutional Neural Networks , 2019, Computer Vision and Pattern Recognition.
[16] J. Z. Kolter,et al. Deep Equilibrium Models , 2019, NeurIPS.
[17] Deng Cai,et al. COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning , 2019, IJCAI.
[18] Chao Xu,et al. LegoNet: Efficient Convolutional Neural Networks with Lego Filters , 2019, ICML.
[19] Quoc V. Le,et al. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.
[20] Yi Yang,et al. Network Pruning via Transformable Architecture Search , 2019, NeurIPS.
[21] Vincent Lepetit,et al. SharpNet: Fast and Accurate Recovery of Occluding Contours in Monocular Depth Estimation , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[22] Gintare Karolina Dziugaite,et al. Stabilizing the Lottery Ticket Hypothesis , 2019 .
[23] Bruno A. Olshausen,et al. Superposition of many models into one , 2019, NeurIPS.
[24] Ning Xu,et al. Slimmable Neural Networks , 2018, ICLR.
[25] Ping Liu,et al. Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Song Han,et al. ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware , 2018, ICLR.
[27] Mingjie Sun,et al. Rethinking the Value of Network Pruning , 2018, ICLR.
[28] Max Welling,et al. Relaxed Quantization for Discretized Neural Networks , 2018, ICLR.
[29] Michael Carbin,et al. The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks , 2018, ICLR.
[30] Boris Katz,et al. ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models , 2019, NeurIPS.
[31] Peter Dayan,et al. Probabilistic Meta-Representations Of Neural Networks , 2018, ArXiv.
[32] Yi Yang,et al. Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks , 2018, IJCAI.
[33] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[34] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[35] Yi Yang,et al. More is Less: A More Complicated Network with Less Inference Complexity , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Silvio Savarese,et al. Joint 2D-3D-Semantic Data for Indoor Scene Understanding , 2017, ArXiv.
[37] Lin Sun,et al. Feedback Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Quoc V. Le,et al. Neural Architecture Search with Reinforcement Learning , 2016, ICLR.
[39] Ran El-Yaniv,et al. Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations , 2016, J. Mach. Learn. Res..
[40] Fei-Fei Li,et al. Deep visual-semantic alignments for generating image descriptions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[41] Jia Deng,et al. Stacked Hourglass Networks for Human Pose Estimation , 2016, ECCV.
[42] Ali Farhadi,et al. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks , 2016, ECCV.
[43] Forrest N. Iandola,et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.
[44] Varun Ramakrishna,et al. Convolutional Pose Machines , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[46] Jitendra Malik,et al. Iterative Instance Segmentation , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[47] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.
[48] Jitendra Malik,et al. Human Pose Estimation with Iterative Error Feedback , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[49] Dit-Yan Yeung,et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.
[50] Yixin Chen,et al. Compressing Neural Networks with the Hashing Trick , 2015, ICML.
[51] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[52] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[53] Varun Ramakrishna,et al. Pose Machines: Articulated Pose Estimation via Inference Machines , 2014, ECCV.
[54] Alex Graves,et al. Recurrent Models of Visual Attention , 2014, NIPS.
[55] Bernt Schiele,et al. 2D Human Pose Estimation: New Benchmark and State of the Art Analysis , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[56] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[57] Yann LeCun,et al. Regularization of Neural Networks using DropConnect , 2013, ICML.
[58] Tim Curran,et al. The Limits of Feedforward Vision: Recurrent Processing Promotes Robust Object Recognition when Objects Are Degraded , 2012, Journal of Cognitive Neuroscience.
[59] Ben Taskar,et al. Structured Prediction Cascades , 2010, AISTATS.
[60] Nicholas J. Butko,et al. Optimal scanning for faster object detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[61] Kenneth O. Stanley,et al. A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks , 2009, Artificial Life.
[62] C. Gilbert,et al. Brain States: Top-Down Influences in Sensory Processing , 2007, Neuron.
[63] Kenneth O. Stanley,et al. Compositional Pattern Producing Networks : A Novel Abstraction of Development , 2007 .
[64] J. M. Hupé,et al. Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons , 1998, Nature.
[65] Geoffrey E. Hinton,et al. Simplifying Neural Networks by Soft Weight-Sharing , 1992, Neural Computation.
[66] David H. Wolpert,et al. Stacked generalization , 1992, Neural Networks.
[67] Anders Krogh,et al. A Simple Weight Decay Can Improve Generalization , 1991, NIPS.
[68] Michael C. Mozer,et al. Using Relevance to Reduce Network Size Automatically , 1989 .
[69] Yann LeCun,et al. Optimal Brain Damage , 1989, NIPS.
[70] Yann Le Cun,et al. A Theoretical Framework for Back-Propagation , 1988 .