Talk:DreamTeam/Reading

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"