DUCKQUANT / THE QUANT & AI DIRECTORY
1,058 resources, ranked by in-directory PageRank
Publication: DBQ: A Differentiable Branch Quantizer for Lightweight Deep Neural Networks. Published Jul 19, 2020.
Publication: One Weight Bitwidth to Rule Them All. Published Aug 22, 2020.
Publication: Once-for-All: Train One Network and Specialize it for Efficient Deployment. Published Aug 26, 2019.
Publication: BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations. Published Feb 16, 2020.
Publication: Training Binary Neural Networks with Real-to-Binary Convolutions. Published Mar 25, 2020.
Publication: AdaBits: Neural Network Quantization with Adaptive Bit-Widths. Published Dec 20, 2019.
Publication: LSQ+: Improving low-bit quantization through learnable offsets and better initialization. Published Apr 20, 2020.
Publication: Data-Free Network Quantization With Adversarial Knowledge Distillation. Published May 8, 2020.
Publication: Balanced Binary Neural Networks with Gated Residual. Published Sep 26, 2019.
Publication: Compressing deep neural networks on FPGAs to binary and ternary precision with HLS4ML. Published Mar 11, 2020.
Publication: Structured Compression by Weight Encryption for Unstructured Pruning and Quantization. Published 2020.
Publication: Generalized Product Quantization Network for Semi-Supervised Image Retrieval. Published 2020.
Publication: Central Similarity Quantization for Efficient Image and Video Retrieval. Published 2020.
Publication: Adaptive Loss-Aware Quantization for Multi-Bit Networks. Published 2020.
Publication: APQ: Joint Search for Network Architecture, Pruning and Quantization Policy. Published 2020.
Publication: Efficient Execution of Quantized Deep Learning Models: A Compiler Approach. Published Jun 18, 2020.
Publication: Post-Training Piecewise Linear Quantization for Deep Neural Networks. Published Jan 31, 2020.
Publication: BiQGEMM: Matrix Multiplication with Lookup Table For Binary-Coding-based Quantized DNNs. Published May 20, 2020.
Publication: Leveraging Automated Mixed-Low-Precision Quantization for tiny edge microcontrollers. Published Aug 12, 2020.
Publication: WrapNet: Neural Net Inference with Ultra-Low-Resolution Arithmetic. Published Jul 26, 2020.
Publication: Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation. Published Apr 20, 2020.
Publication: Training Binary Neural Networks using the Bayesian Learning Rule. Published Feb 25, 2020.
Publication: Bit Efficient Quantization for Deep Neural Networks. Published Oct 7, 2019.
Publication: Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization. Published Jun 5, 2019.
Publication: Deep Binary Reconstruction for Cross-modal Hashing. Published Aug 17, 2017.
Publication: Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming. Published Jun 14, 2020.
Publication: How Does Batch Normalization Help Binary Training?. Published Sep 18, 2019.
Publication: Distillation Guided Residual Learning for Binary Convolutional Neural Networks. Published Jul 10, 2020.
Publication: Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs. Published Jun 30, 2020.
Publication: MeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy?. Published Jan 16, 2020.
Publication: RPR: Random Partition Relaxation for Training; Binary and Ternary Weight Neural Networks. Published Jan 4, 2020.
Publication: Towards Lossless Binary Convolutional Neural Networks Using Piecewise Approximation. Published Aug 8, 2020.
Publication: Understanding Learning Dynamics of Binary Neural Networks via Information Bottleneck. Published Jun 13, 2020.
Publication: Gradient $ell_1$ Regularization for Quantization Robustness. Published Feb 18, 2020.
Publication: Shifted and Squeezed 8-bit Floating Point format for Low-Precision Training of Deep Neural Networks. Published Jan 16, 2020.
Publication: Language Models are Few-Shot Learners. Published May 28, 2020.
