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Found 17 papers, 5 papers with code
A Unifying Tensor View for Lightweight CNNs
Despite the decomposition of convolutional kernels for lightweight CNNs being well studied, existing works that rely on tensor network diagrams or hyperdimensional abstraction lack geometry intuition.
Lite it fly: An All-Deformable-Butterfly Network
Most deep neural networks (DNNs) consist fundamentally of convolutional and/or fully connected layers, wherein the linear transform can be cast as the product between a filter matrix and a data matrix obtained by arranging feature tensors into columns.
Cluster-based Method for Eavesdropping Identification and Localization in Optical Links
We propose a cluster-based method to detect and locate eavesdropping events in optical line systems characterized by small power losses.
A Spectral Perspective towards Understanding and Improving Adversarial Robustness
Deep neural networks (DNNs) are incredibly vulnerable to crafted, imperceptible adversarial perturbations.
Frequency Regularization for Improving Adversarial Robustness
Deep neural networks are incredibly vulnerable to crafted, human-imperceptible adversarial perturbations.
PECAN: A Product-Quantized Content Addressable Memory Network
A novel deep neural network (DNN) architecture is proposed wherein the filtering and linear transform are realized solely with product quantization (PQ).
Coarse to Fine: Image Restoration Boosted by Multi-Scale Low-Rank Tensor Completion
Existing low-rank tensor completion (LRTC) approaches aim at restoring a partially observed tensor by imposing a global low-rank constraint on the underlying completed tensor.
Deformable Butterfly: A Highly Structured and Sparse Linear Transform
We introduce a new kind of linear transform named Deformable Butterfly (DeBut) that generalizes the conventional butterfly matrices and can be adapted to various input-output dimensions.
What Do Adversarially trained Neural Networks Focus: A Fourier Domain-based Study
We are hopeful this work can shed light on the design of more robust neural networks.
Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks
Elasticities in depth, width, kernel size and resolution have been explored in compressing deep neural networks (DNNs).
EZCrop: Energy-Zoned Channels for Robust Output Pruning
Recent results have revealed an interesting observation in a trained convolutional neural network (CNN), namely, the rank of a feature map channel matrix remains surprisingly constant despite the input images.
FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware Transformation
Prior arts often discretize the network weights by carefully tuning hyper-parameters of quantization (e. g. non-uniform stepsize and layer-wise bitwidths), which are complicated and sub-optimal because the full-precision and low-precision models have a large discrepancy.
Optimized Observable Readout from Single-shot Images of Ultracold Atoms via Machine Learning
The efficient extraction of observables from single-shot images is thus crucial.
Quantum Gases Quantum Physics
HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper CNN Compression
The emerging edge computing has promoted immense interests in compacting a neural network without sacrificing much accuracy.
MCTDH-X: The multiconfigurational time-dependent Hartree method for indistinguishable particles software
The MCTDH-X software is a set of programs and scripts to compute, analyze, and visualize solutions for the time-dependent and time-independent many-body Schr\"{o}dinger equation for indistinguishable quantum particles.
Quantum Gases
MiSC: Mixed Strategies Crowdsourcing
Popular crowdsourcing techniques mostly focus on evaluating workers' labeling quality before adjusting their weights during label aggregation.
