Talk:DreamTeam/Reading: Difference between revisions
(new finds, mostly re fpga) |
(recent additions dump) |
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9 Feb 2017 | |||
https://arxiv.org/pdf/1603.09382 | |||
" Deep Networks with Stochastic Depth" | |||
https://arxiv.org/pdf/1504.04871 | |||
" DEEP-CARVING: Discovering Visual Attributes by Carving Deep Neural Nets" | |||
https://arxiv.org/pdf/1608.06993 | |||
" Densely Connected Convolutional Networks" | |||
https://arxiv.org/pdf/1602.01616 | |||
" FPGA Based Implementation of Deep Neural Networks Using On-chip Memory Only" | |||
https://arxiv.org/pdf/1511.06072 | |||
" Mediated Experts for Deep Convolutional Networks" | |||
https://arxiv.org/pdf/1611.06973 | |||
" RhoanaNet Pipeline: Dense Automatic Neural Annotation" | |||
https://arxiv.org/pdf/1701.04465 | |||
" The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning" | |||
https://arxiv.org/pdf/1602.08124 | |||
" vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design" | |||
https://arxiv.org/pdf/1610.00163 | |||
" X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets" | |||
https://arxiv.org/pdf/1610.01891 | |||
"A New Data Representation Based on Training Data Characteristics to Extract Drug Named-Entity in Medical Text" | |||
https://arxiv.org/pdf/1603.07400 | |||
"A Reconfigurable Low Power High Throughput Architecture for Deep Network Training" | |||
https://arxiv.org/pdf/1608.04064 | |||
"About Pyramid Structure in Convolutional Neural Networks" | |||
https://arxiv.org/pdf/1603.07341 | |||
"Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices" | |||
https://arxiv.org/pdf/1506.02690 | |||
"Adaptive Normalized Risk-Averting Training For Deep Neural Networks" | |||
https://arxiv.org/pdf/1306.0152 | |||
"An Analysis of the Connections Between Layers of Deep Neural Networks" | |||
https://arxiv.org/pdf/1502.03436 | |||
"An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections" | |||
https://arxiv.org/pdf/1502.02476 | |||
"An Infinite Restricted Boltzmann Machine" | |||
https://arxiv.org/pdf/1508.04535 | |||
"Bit-Scalable Deep Hashing with Regularized Similarity Learning for Image Retrieval and Person Re-identification" | |||
https://arxiv.org/pdf/1601.06071 | |||
"Bitwise Neural Networks" | |||
https://arxiv.org/pdf/1604.05897 | |||
"CLAASIC: a Cortex-Inspired Hardware Accelerator" | |||
https://arxiv.org/pdf/1606.04884v1 | |||
"cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL" | |||
https://arxiv.org/pdf/1504.04788 | |||
"Compressing Neural Networks with the Hashing Trick" | |||
https://arxiv.org/pdf/1509.08745 | |||
"Compression of Deep Neural Networks on the Fly" | |||
https://arxiv.org/pdf/1509.08971 | |||
"Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition" | |||
https://arxiv.org/pdf/1601.04187 | |||
"Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware" | |||
https://arxiv.org/pdf/1603.08270 | |||
"Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing" | |||
https://arxiv.org/pdf/1603.01025 | |||
"Convolutional Neural Networks using Logarithmic Data Representation" | |||
https://arxiv.org/pdf/1604.06154 | |||
"Deep Adaptive Network: An Efficient Deep Neural Network with Sparse Binary Connections" | |||
https://arxiv.org/pdf/1509.02470 | |||
"Deep Attributes from Context-Aware Regional Neural Codes" | |||
https://arxiv.org/pdf/1510.00149 | |||
"DEEP COMPRESSION : COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING , TRAINED QUANTIZATION AND HUFFMAN CODING" | |||
https://arxiv.org/pdf/1605.09507 | |||
"Deep convolutional neural networks for predominant instrument recognition in polyphonic music" | |||
https://arxiv.org/pdf/1611.00710 | |||
"Deep counter networks for asynchronous event-based processing" | |||
https://arxiv.org/pdf/1606.07230 | |||
"Deep Learning Markov Random Field for Semantic Segmentation" | |||
https://papers.nips.cc/paper/6388-deep-learning-models-of-the-retinal-response-to-natural-scenes.pdf | |||
"Deep Learning Models of the Retinal Response to Natural Scenes" | |||
https://arxiv.org/pdf/1610.09650 | |||
"Deep Model Compression: Distilling Knowledge from Noisy Teachers" | |||
https://arxiv.org/pdf/1406.3284 | |||
"Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition" | |||
https://arxiv.org/pdf/1409.5185 | |||
"Deeply-Supervised Nets" | |||
https://arxiv.org/pdf/1612.04770 | |||
"Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling" | |||
https://arxiv.org/pdf/1604.08220 | |||
"Diving deeper into mentee networks" | |||
https://arxiv.org/pdf/1601.00917 | |||
"DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks" | |||
https://arxiv.org/pdf/1506.04477 | |||
"Dual Memory Architectures for Fast Deep Learning of Stream Data via an Online-Incremental-Transfer Strategy" | |||
https://arxiv.org/pdf/1602.01528 | |||
"EIE: Efficient Inference Engine on Compressed Deep Neural Network" | |||
https://arxiv.org/pdf/1603.02844 | |||
"Fast Training of Triplet-based Deep Binary Embedding Networks" | |||
https://arxiv.org/pdf/1511.00175 | |||
"FireCaffe: near-linear acceleration of deep neural network training on compute clusters" | |||
https://arxiv.org/pdf/0911.0787 | |||
"Generalized Discriminant Analysis algorithm for feature reduction in Cyber Attack Detection System" | |||
https://arxiv.org/pdf/1511.06951 | |||
"Gradual DropIn of Layers to Train Very Deep Neural Networks" | |||
https://arxiv.org/pdf/1606.03498 | |||
"Improved Techniques for Training GANs" | |||
https://arxiv.org/pdf/1611.06473 | |||
"LCNN: Lookup-based Convolutional Neural Network" | |||
https://arxiv.org/pdf/1608.06037 | |||
"Let�s keep it simple: Using simple architectures to outperform deeper architectures" | |||
https://arxiv.org/pdf/1610.09893 | |||
"LightRNN: Memory and Computation-Efficient Recurrent Neural Networks" | |||
https://arxiv.org/pdf/1411.5458 | |||
"Liquid State Machine with Dendritically Enhanced Readout for Low-power, Neuromorphic VLSI Implementations" | |||
https://arxiv.org/pdf/1511.06381 | |||
"MANIFOLD REGULARIZED DEEP NEURAL NETWORKS USING ADVERSARIAL EXAMPLES" | |||
https://arxiv.org/pdf/1509.07302 | |||
"Mapping Generative Models onto a Network of Digital Spiking Neurons" | |||
https://arxiv.org/pdf/1412.1442 | |||
"Memory Bounded Deep Convolutional Networks" | |||
https://arxiv.org/pdf/1602.09046v1 | |||
"On Complex Valued Convolutional Neural Networks" | |||
https://arxiv.org/pdf/1512.04295 | |||
"Origami: A 803 GOp/s/W Convolutional Network Accelerator" | |||
https://arxiv.org/pdf/1612.00891 | |||
"Parameter Compression of Recurrent Neural Networks and Degredation of Short-term Memory" | |||
https://arxiv.org/pdf/1701.08734 | |||
"PathNet: Evolution Channels Gradient Descent in Super Neural Networks" | |||
https://arxiv.org/pdf/1512.06216 | |||
"Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines" | |||
https://arxiv.org/pdf/1609.07061 | |||
"Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations" | |||
https://arxiv.org/pdf/1408.5405 | |||
"Recurrent Neural Network Based Hybrid Model of Gene Regulatory Network" | |||
https://arxiv.org/pdf/1611.01639 | |||
"Representing inferential uncertainty in deep neural networks through sampling" | |||
https://arxiv.org/pdf/1511.06306v2 | |||
"Robust Convolutional Neural Networks under Adversarial Noise" | |||
https://arxiv.org/pdf/1607.05418 | |||
"Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off" | |||
https://arxiv.org/pdf/1611.05939 | |||
"SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing" | |||
https://arxiv.org/pdf/1602.08194 | |||
"Scalable and Sustainable Deep Learning via Randomized Hashing" | |||
https://arxiv.org/pdf/1602.08556 | |||
"Significance Driven Hybrid 8T-6T SRAM for Energy-Efficient Synaptic Storage in Artificial Neural Networks" | |||
https://arxiv.org/pdf/1611.07385 | |||
"Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Readin" | |||
https://arxiv.org/pdf/1605.08512 | |||
"SNN: Stacked Neural Networks" | |||
https://arxiv.org/pdf/1611.01427 | |||
"Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks" | |||
https://arxiv.org/pdf/1506.03767 | |||
"Spectral Representations for Convolutional Neural Networks" | |||
https://arxiv.org/pdf/1602.07360 | |||
"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size" | |||
https://arxiv.org/pdf/1508.05463 | |||
"StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity" | |||
https://arxiv.org/pdf/1512.08571 | |||
"Structured Pruning of Deep Convolutional Neural Networks" | |||
https://arxiv.org/pdf/1605.02971 | |||
"Structured Receptive Fields in CNNs" | |||
https://arxiv.org/pdf/1412.8648 | |||
"STT-SNN: A Spin-Transfer-Torque Based Soft-Limiting Non-Linear Neuron for Low-Power Artificial Neural Networks" | |||
https://arxiv.org/pdf/1412.3409 | |||
"Teaching Deep Convolutional Neural Networks to Play Go" | |||
https://arxiv.org/pdf/1609.00222 | |||
"Ternary Neural Networks for Resource-Efficient AI Applications" | |||
http://www.arxiv.org/pdf/1605.04711 | |||
"Ternary weight networks" | |||
https://arxiv.org/pdf/1510.03283 | |||
"Text-Attentional Convolutional Neural Networks for Scene Text Detection" | |||
https://arxiv.org/pdf/1611.01773 | |||
"The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs" | |||
https://arxiv.org/pdf/1512.00242 | |||
"Towards Dropout Training for Convolutional Neural Networks" | |||
https://arxiv.org/pdf/1412.6596 | |||
"Training Deep Neural Networks on Noisy Labels with Bootstrapping" | |||
https://arxiv.org/pdf/1412.7024 | |||
"TRAINING DEEP NEURAL NETWORKS WITH LOW PRECISION MULTIPLICATIONS" | |||
https://arxiv.org/pdf/1608.04622 | |||
"Training Echo State Networks with Regularization through Dimensionality Reduction" | |||
https://arxiv.org/pdf/1601.04183 | |||
"TrueHappiness: Neuromorphic Emotion Recognition on TrueNorth" | |||
https://arxiv.org/pdf/1603.05201v2.pdf | |||
"Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units" | |||
https://arxiv.org/pdf/1612.03940 | |||
"Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks" | |||
https://arxiv.org/pdf/1509.08967 | |||
"Very Deep Multilingual Convolutional Neural Networks for LVCSR" | |||
----- | |||
2 Feb 2017 | |||
http://www.ijsret.org/pdf/120399.pdf | http://www.ijsret.org/pdf/120399.pdf |
Latest revision as of 00:32, 10 February 2017
9 Feb 2017
https://arxiv.org/pdf/1603.09382 " Deep Networks with Stochastic Depth"
https://arxiv.org/pdf/1504.04871
" DEEP-CARVING: Discovering Visual Attributes by Carving Deep Neural Nets"
https://arxiv.org/pdf/1608.06993
" Densely Connected Convolutional Networks"
https://arxiv.org/pdf/1602.01616
" FPGA Based Implementation of Deep Neural Networks Using On-chip Memory Only"
https://arxiv.org/pdf/1511.06072
" Mediated Experts for Deep Convolutional Networks"
https://arxiv.org/pdf/1611.06973
" RhoanaNet Pipeline: Dense Automatic Neural Annotation"
https://arxiv.org/pdf/1701.04465
" The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning"
https://arxiv.org/pdf/1602.08124
" vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design"
https://arxiv.org/pdf/1610.00163
" X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets"
https://arxiv.org/pdf/1610.01891
"A New Data Representation Based on Training Data Characteristics to Extract Drug Named-Entity in Medical Text"
https://arxiv.org/pdf/1603.07400
"A Reconfigurable Low Power High Throughput Architecture for Deep Network Training"
https://arxiv.org/pdf/1608.04064
"About Pyramid Structure in Convolutional Neural Networks"
https://arxiv.org/pdf/1603.07341
"Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices"
https://arxiv.org/pdf/1506.02690
"Adaptive Normalized Risk-Averting Training For Deep Neural Networks"
https://arxiv.org/pdf/1306.0152
"An Analysis of the Connections Between Layers of Deep Neural Networks"
https://arxiv.org/pdf/1502.03436
"An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections"
https://arxiv.org/pdf/1502.02476
"An Infinite Restricted Boltzmann Machine"
https://arxiv.org/pdf/1508.04535
"Bit-Scalable Deep Hashing with Regularized Similarity Learning for Image Retrieval and Person Re-identification"
https://arxiv.org/pdf/1601.06071
"Bitwise Neural Networks"
https://arxiv.org/pdf/1604.05897
"CLAASIC: a Cortex-Inspired Hardware Accelerator"
https://arxiv.org/pdf/1606.04884v1
"cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL"
https://arxiv.org/pdf/1504.04788
"Compressing Neural Networks with the Hashing Trick"
https://arxiv.org/pdf/1509.08745
"Compression of Deep Neural Networks on the Fly"
https://arxiv.org/pdf/1509.08971
"Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition"
https://arxiv.org/pdf/1601.04187
"Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware"
https://arxiv.org/pdf/1603.08270
"Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing"
https://arxiv.org/pdf/1603.01025
"Convolutional Neural Networks using Logarithmic Data Representation"
https://arxiv.org/pdf/1604.06154
"Deep Adaptive Network: An Efficient Deep Neural Network with Sparse Binary Connections"
https://arxiv.org/pdf/1509.02470
"Deep Attributes from Context-Aware Regional Neural Codes"
https://arxiv.org/pdf/1510.00149
"DEEP COMPRESSION : COMPRESSING DEEP NEURAL NETWORKS WITH PRUNING , TRAINED QUANTIZATION AND HUFFMAN CODING"
https://arxiv.org/pdf/1605.09507
"Deep convolutional neural networks for predominant instrument recognition in polyphonic music"
https://arxiv.org/pdf/1611.00710
"Deep counter networks for asynchronous event-based processing"
https://arxiv.org/pdf/1606.07230
"Deep Learning Markov Random Field for Semantic Segmentation"
https://papers.nips.cc/paper/6388-deep-learning-models-of-the-retinal-response-to-natural-scenes.pdf
"Deep Learning Models of the Retinal Response to Natural Scenes"
https://arxiv.org/pdf/1610.09650
"Deep Model Compression: Distilling Knowledge from Noisy Teachers"
https://arxiv.org/pdf/1406.3284
"Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition"
https://arxiv.org/pdf/1409.5185
"Deeply-Supervised Nets"
https://arxiv.org/pdf/1612.04770
"Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling"
https://arxiv.org/pdf/1604.08220
"Diving deeper into mentee networks"
https://arxiv.org/pdf/1601.00917
"DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks"
https://arxiv.org/pdf/1506.04477
"Dual Memory Architectures for Fast Deep Learning of Stream Data via an Online-Incremental-Transfer Strategy"
https://arxiv.org/pdf/1602.01528
"EIE: Efficient Inference Engine on Compressed Deep Neural Network"
https://arxiv.org/pdf/1603.02844
"Fast Training of Triplet-based Deep Binary Embedding Networks"
https://arxiv.org/pdf/1511.00175
"FireCaffe: near-linear acceleration of deep neural network training on compute clusters"
https://arxiv.org/pdf/0911.0787
"Generalized Discriminant Analysis algorithm for feature reduction in Cyber Attack Detection System"
https://arxiv.org/pdf/1511.06951
"Gradual DropIn of Layers to Train Very Deep Neural Networks"
https://arxiv.org/pdf/1606.03498
"Improved Techniques for Training GANs"
https://arxiv.org/pdf/1611.06473
"LCNN: Lookup-based Convolutional Neural Network"
https://arxiv.org/pdf/1608.06037
"Let�s keep it simple: Using simple architectures to outperform deeper architectures"
https://arxiv.org/pdf/1610.09893
"LightRNN: Memory and Computation-Efficient Recurrent Neural Networks"
https://arxiv.org/pdf/1411.5458
"Liquid State Machine with Dendritically Enhanced Readout for Low-power, Neuromorphic VLSI Implementations"
https://arxiv.org/pdf/1511.06381
"MANIFOLD REGULARIZED DEEP NEURAL NETWORKS USING ADVERSARIAL EXAMPLES"
https://arxiv.org/pdf/1509.07302
"Mapping Generative Models onto a Network of Digital Spiking Neurons"
https://arxiv.org/pdf/1412.1442
"Memory Bounded Deep Convolutional Networks"
https://arxiv.org/pdf/1602.09046v1
"On Complex Valued Convolutional Neural Networks"
https://arxiv.org/pdf/1512.04295
"Origami: A 803 GOp/s/W Convolutional Network Accelerator"
https://arxiv.org/pdf/1612.00891
"Parameter Compression of Recurrent Neural Networks and Degredation of Short-term Memory"
https://arxiv.org/pdf/1701.08734
"PathNet: Evolution Channels Gradient Descent in Super Neural Networks"
https://arxiv.org/pdf/1512.06216
"Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines"
https://arxiv.org/pdf/1609.07061
"Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations"
https://arxiv.org/pdf/1408.5405
"Recurrent Neural Network Based Hybrid Model of Gene Regulatory Network"
https://arxiv.org/pdf/1611.01639
"Representing inferential uncertainty in deep neural networks through sampling"
https://arxiv.org/pdf/1511.06306v2
"Robust Convolutional Neural Networks under Adversarial Noise"
https://arxiv.org/pdf/1607.05418
"Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off"
https://arxiv.org/pdf/1611.05939
"SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing"
https://arxiv.org/pdf/1602.08194
"Scalable and Sustainable Deep Learning via Randomized Hashing"
https://arxiv.org/pdf/1602.08556
"Significance Driven Hybrid 8T-6T SRAM for Energy-Efficient Synaptic Storage in Artificial Neural Networks"
https://arxiv.org/pdf/1611.07385
"Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Readin"
https://arxiv.org/pdf/1605.08512
"SNN: Stacked Neural Networks"
https://arxiv.org/pdf/1611.01427
"Sparsely-Connected Neural Networks: Towards Efficient VLSI Implementation of Deep Neural Networks"
https://arxiv.org/pdf/1506.03767
"Spectral Representations for Convolutional Neural Networks"
https://arxiv.org/pdf/1602.07360
"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size"
https://arxiv.org/pdf/1508.05463
"StochasticNet: Forming Deep Neural Networks via Stochastic Connectivity"
https://arxiv.org/pdf/1512.08571
"Structured Pruning of Deep Convolutional Neural Networks"
https://arxiv.org/pdf/1605.02971
"Structured Receptive Fields in CNNs"
https://arxiv.org/pdf/1412.8648
"STT-SNN: A Spin-Transfer-Torque Based Soft-Limiting Non-Linear Neuron for Low-Power Artificial Neural Networks"
https://arxiv.org/pdf/1412.3409
"Teaching Deep Convolutional Neural Networks to Play Go"
https://arxiv.org/pdf/1609.00222
"Ternary Neural Networks for Resource-Efficient AI Applications"
http://www.arxiv.org/pdf/1605.04711
"Ternary weight networks"
https://arxiv.org/pdf/1510.03283
"Text-Attentional Convolutional Neural Networks for Scene Text Detection"
https://arxiv.org/pdf/1611.01773
"The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs"
https://arxiv.org/pdf/1512.00242
"Towards Dropout Training for Convolutional Neural Networks"
https://arxiv.org/pdf/1412.6596
"Training Deep Neural Networks on Noisy Labels with Bootstrapping"
https://arxiv.org/pdf/1412.7024
"TRAINING DEEP NEURAL NETWORKS WITH LOW PRECISION MULTIPLICATIONS"
https://arxiv.org/pdf/1608.04622
"Training Echo State Networks with Regularization through Dimensionality Reduction"
https://arxiv.org/pdf/1601.04183
"TrueHappiness: Neuromorphic Emotion Recognition on TrueNorth"
https://arxiv.org/pdf/1603.05201v2.pdf
"Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units"
https://arxiv.org/pdf/1612.03940
"Understanding the Impact of Precision Quantization on the Accuracy and Energy of Neural Networks"
https://arxiv.org/pdf/1509.08967
"Very Deep Multilingual Convolutional Neural Networks for LVCSR"
2 Feb 2017
http://www.ijsret.org/pdf/120399.pdf "A Literature survey for Object Recognition using Neural Networks in FPGA"
https://kar.kent.ac.uk/14766/1/FPGA_based_Lorrentz_Howells.pdf
"An FPGA based adaptive weightless Neural Network Hardware"
http://infoteh.etf.unssa.rs.ba/zbornik/2016/radovi/KST-1/KST-1-15.pdf
"Analysis of Visible Light Communication System for Implementation in Sensor Networks"
http://www.ccs.fau.edu/~fuchs/pub/Exp_brain_res_slav.pdf
"Anatomically constrained minimum variance beamforming applied to EEG"
https://www.ijsr.net/archive/v5i3/NOV162166.pdf
"Based on Multi-FPGA Neuron Simulation Hardware Platform"
https://arxiv.org/pdf/1611.03000v1
"Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning"
https://arxiv.org/pdf/1606.00094v2
"Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms"
https://arxiv.org/pdf/1609.09671v1
"Caffeinated FPGAs: FPGA Framework For Convolutional Neural Networks"
https://arxiv.org/pdf/1606.04884v1
"cltorch: a Hardware-Agnostic Backend for the Torch Deep Neural Network Library, Based on OpenCL"
https://arxiv.org/pdf/1511.07376v2
"CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android"
https://arxiv.org/pdf/1609.09296v1
"Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs"
https://arxiv.org/pdf/1511.06530v2
"Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications"
https://arxiv.org/pdf/1608.04363v2
"Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification"
https://arxiv.org/pdf/1611.05128v1
"Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning"
http://www.ijser.org/researchpaper/Digital-Hardware-Implementation-of-Artificial-Neural-Network-for-Signal-Processing.pdf
"Digital Hardware Implementation of Artificial Neural Network for Signal Processing"
https://arxiv.org/pdf/1612.00694v1
"ESE: Efficient Speech Recognition Engine with Compressed LSTM on FPGA"
http://ethesis.nitrkl.ac.in/4217/1/FPGA_implementation_of_artificial_neural_networks.pdf
"FPGA IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS"
http://lab.fs.uni-lj.si/lasin/wp/IMIT_files/neural/doc/Omondi2006.pdf
"FPGA Implementations of Neural Networks"
http://vast.cs.ucla.edu/sites/default/files/publications/ASP-DAC2017-1352-11.pdf
"FPGA-based Accelerator for Long Short-Term Memory Recurrent Neural Networks"
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.409.7533&rep=rep1&type=pdf
"FPGA-TARGETED NEURAL ARCHITECTURE FOR EMBEDDED ALERTNESS DETECTION"
http://yann.lecun.com/exdb/publis/pdf/farabet-iscas-10.pdf
"Hardware Accelerated Convolutional Neural Networks for Synthetic Vision Systems"
http://www.emo.org.tr/ekler/21eb0b827c09dd1_ek.pdf
"HARDWARE IMPLEMENTATION OF A FEEDFORWARD NEURAL NETWORK USING FPGAs"
http://arxiv.org/pdf/1609.01287v1
"Holographic Entanglement Entropy"
http://jestec.taylors.edu.my/Vol%206%20Issue%204%20August%2011/Vol_6_4_411_428_AL%20JAMMAS.pdf
"IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA"
http://www.nmr.mgh.harvard.edu/meg/pdfs/1993-Hamalainen-RMP.pdf
"Magnetoencephalography - theory, instrumentation, and applications to non-invasive studies of the working human brain"
https://arxiv.org/pdf/1602.09046v1
"On Complex Valued Convolutional Neural Networks"
http://arxiv.org/ftp/arxiv/papers/1201/1201.4617.pdf
"Photo-Thermal Neural Excitation by Extrinsic and Intrinsic Absorbers: A Temperature-Rate Model"
https://arxiv.org/pdf/1611.02450v1
"PipeCNN: An OpenCL-Based FPGA Accelerator for Large-Scale Convolution Neuron Networks"
https://arxiv.org/pdf/1511.05552v4.pdf
"Recurrent Neural Networks Hardware Implementation on FPGA"
https://arxiv.org/pdf/1605.06402v1
"Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks"
https://arxiv.org/pdf/1511.06306v2
"Robust Convolutional Neural Networks under Adversarial Noise"
https://arxiv.org/pdf/1701.03400v2
"Scaling Binarized Neural Networks on Reconfigurable Logic"
https://homes.cs.washington.edu/~luisceze/publications/snnap-hpca-2015.pdf
"SNNAP: Approximate Computing on Programmable SoCs via Neural Acceleration"
https://arxiv.org/pdf/1406.4729v4
"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition"
https://arxiv.org/pdf/1612.04052v1
"Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks"
https://arxiv.org/pdf/1701.00485v2
"Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices"
https://arxiv.org/pdf/1603.05201v2.pdf
"Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units"
https://arxiv.org/pdf/1606.05487v1
"YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights"