A new algorithm learns a dictionary for blind one-bit compressed sensing.
problem Reconstructing sparse signals from one-bit compressed measurements without prior knowledge of the sparsity domain.
method Dictionary learning to learn the matrix $\Db=\AbΦ$ using a steepest-descent method.
result The proposed algorithm outperforms the no-dictionary-learning case, especially with more training signals and measurements.
Sparse diffusion steepest-descent for one-bit CS in sensor networks.
problem Estimating sparse vectors from sign measurements in wireless sensor networks.
method Diffusion strategy combined with steepest-descent optimization for cooperative sparse vector estimation.
result Simulation results show the proposed algorithm outperforms non-distributive methods.
Study 1-bit compressive sensing with generative models, improving recovery accuracy.
problem Accurately recover sparse vectors from binary measurements with generative models.
method Analyzes noiseless and noisy 1-bit measurements with i.i.d.~Gaussian and Lipschitz continuous generative priors, proving sample complexity bounds and stability properties.
result Proves sample complexity bounds and stability properties for 1-bit compressive sensing with generative models.
Paper offers robust recovery for 1-bit sensing with partial Gaussian circulant matrices.
problem Accurately recovering vectors from 1-bit measurements using structured matrices.
method Correlation-based optimization with randomly signed partial Gaussian circulant matrices and generative models.
result Recovery guarantees match those for i.i.d. Gaussian matrices but with faster computation.
Bayesian algorithm improves sparse recovery in noisy one-bit CS with perturbation.
problem Noisy sparse recovery in one-bit compressed sensing with perturbation.
method BHT-MLE algorithm using Bayesian hypothesis test and ML estimator.
result BHT-MLE offers more accurate reconstruction than MLE at lower computational cost.
AdaBoost improves binary classification in robust one-bit compressed sensing with adversarial errors.
problem Binary classification in robust one-bit compressed sensing with adversarial errors.
method AdaBoost and max-ℓ1-margin-classifier approach, with convergence rates improved under certain feature conditions. result Improved convergence rates and explanation for harmless interpolating adversarial noise.
Paper proposes a hybrid model-based and data-driven method for one-bit compressive variational autoencoding.
problem Designing efficient one-bit compressive sensing systems.
method Hybrid model-based and data-driven approach for one-bit compressive variational autoencoding.
result Significant improvement in one-bit compressive sensing compared to state-of-the-art methods.
Binary Iterative Hard Thresholding converges with optimal number of 1-bit measurements.
problem Recovering sparse signals from 1-bit compressed measurements.
method Binary Iterative Hard Thresholding (BIHT) algorithm.
result BIHT converges with only O(k/ε) measurements, optimal for recovery.
This letter improves sparse signal detection from one bit compressed sensing measurements.
problem Sparse signal detection from one bit compressed sensing measurements.
method Extended GLRT detector with optimal quantizer design and a double-detector scheme.
result The double-detector scheme outperforms existing methods in detection performance.
Paper proposes a hybrid model-based and data-driven approach for one-bit compressive autoencoding.
problem Designing efficient one-bit compressive autoencoding models for complex systems.
method Hybrid model-based and data-driven methodology for one-bit sparse signal recovery.
result Significant improvement in one-bit compressive autoencoding compared to state-of-the-art algorithms.
Paper proposes algorithms for robust 1-bit compressive sensing with nonconvex penalties.
problem Recovering sparse signals from one-bit measurements.
method Develops algorithms based on convex and nonconvex penalties, providing analytical solutions.
result Analytical solutions for several nonconvex penalties are found, making the recovery process faster and more efficient.
New pinball loss improves decoding of noisy one-bit compressive sensing.
problem Improving decoding performance of noisy one-bit compressive sensing.
method Proposed pinball loss and convex models, designed dual coordinate ascent algorithms.
result Effective pinball loss minimization improves decoding performance.
Paper proposes a 1-bit quantization scheme for high-dimensional statistical estimation.
problem High-dimensional statistical estimation with limited data.
method Uniformly dithered 1-bit quantization for sparse covariance matrix estimation, sparse linear regression, and matrix completion.
result Near minimax rates in sub-Gaussian regime and improved rates in heavy-tailed regime.
Paper tackles 1-bit compressed sensing, presenting efficient algorithm for sparse signal estimation.
problem Estimating sparse signals from binary measurements.
method Non-convex sparsity-constrained program with one-shot hard thresholding.
result Simple algorithm produces accurate signal approximation with high probability.
Paper tackles one-bit compressed sensing using PAC learning theory.
problem One-bit compressed sensing problem.
method Formulated as PAC learning problem, uses VC-dimension and PAC learning theory.
result Consistent algorithm can recover k-sparse vectors with O(klg(n/k)) measurements. Quantized Compressive K-Means improves clustering efficiency by compressing data before sensing.
problem Efficiently clustering large datasets with minimal computational and resource costs.
method Generalizes CKM to 1-bit quantization, compressing data before sensing.
result 1-bit quantization significantly reduces resource usage without compromising clustering accuracy.
This paper improves support recovery in universal one-bit compressed sensing with fewer measurements.
problem Support recovery in universal one-bit compressed sensing.
method Developed algorithms to recover the support of sparse signals with a small number of false positives.
result Support recovery with ildeO(k3/2) measurements, improving to ildeO(k) with known dynamic range. Paper analyzes BIHT for noisy 1-bit CS, improving results with up to τ-fraction of incorrect measurements.
problem Estimating sparse vectors from noisy sign measurements in 1-bit compressed sensing.
method Binary Iterative Hard Thresholding (BIHT) algorithm, using Gaussian matrices and high-dimensional geometry analysis.
result BIHT provides estimates within ε+τ error with τ-fraction of incorrect measurements, maintaining universality of measurements.
This paper improves support recovery in universal one-bit compressed sensing.
problem Support recovery in one-bit compressed sensing for sparse signals.
method Proposes approximate support recovery and superset recovery algorithms with polynomial-time complexity.
result Achieves improved support recovery with fewer measurements compared to existing methods.
Consider the recovery of an unknown signal x from quantized linear measurements. In the one-bit compressive sensing setting, one typically assumes that x is sparse, and that the measurements are of the form sign(⟨ai,x⟩)∈{±1}. Since such measurements give no informati…
DeepFPC uses neural networks to recover sparse signals from quantized measurements.
problem Recovering sparse signals from quantized measurements.
method Unfolding the fixed-point continuation algorithm into a deep neural network.
result DeepFPC outperforms state-of-the-art algorithms in DOA estimation.
Study on recovering supports of multiple sparse vectors from mixed linear measurements.
problem Recovering supports of multiple sparse vectors from a mixture of linear measurements.
method Developed algorithms to identify the support of all component vectors using polynomial and quasi-polynomial number of measurements.
result Polynomial and quasi-polynomial number of measurements sufficient for recovering the supports of all component vectors.
Bit-Swap improves lossless compression for hierarchical latent variable models.
problem Efficient lossless compression for latent variable models with hierarchical structure.
method Generalizes bits-back coding to hierarchical latent variable models with Markov chain structure.
result Achieves superior lossless compression rates for hierarchical latent variable models.
Study on recovering sparse linear classifiers from mixed binary responses.
problem Learning a mixture of sparse linear classifiers from binary responses.
method Query-based approach to identify all sparse vectors from a set.
result Upper bounds on the number of queries required for recovery.
New method compresses neural networks using random code, improving efficiency.
problem Large memory footprint of deep neural networks.
method Training a variational distribution over weights, encoding using Kullback-Leibler divergence.
result Achieves state-of-the-art compression rates and test performance.
End-to-end auto-encoder for neural image compression with variable bit rates.
problem Efficient image compression with variable bit rates.
method Block-based auto-encoder system with novel contributions.
result Incremental performance improvement of each contribution.
A new method compresses MNIST dataset with VAE at near optimal rate.
problem Practical lossless compression with latent variable models.
method Bits Back with ANS (BB-ANS) for near optimal compression rate.
result Achieved superior compression rates to standard methods.
Kernel Quantization improves CNN compression without sacrificing performance.
problem Efficiently compressing CNN models without significant performance loss.
method Quantizes convolution kernels as the unit, learning a codebook for low-bit indexes.
result Significant compression ratio achieved with minimal accuracy loss.
SHVC improves image compression with fewer parameters.
problem Challenges in VAE compression, especially with bits-back coding.
method Introduces autoregressive sub-pixel convolution and autoregressive initial bits.
result Achieves state-of-the-art compression performance with fewer model parameters.
We consider the reconstruction problem in compressed sensing in which the observations are recorded in a finite number of bits. They may thus contain quantization errors (from being rounded to the nearest representable value) and saturation errors (from being outside the range of representable values). Our formulation …
New method improves image compression using bits-back coding.
problem Lossy image compression with deep latent variable models.
method Iterative inference, stochastic annealing, bits-back coding.
result New state-of-the-art performance on lossy image compression.
Generalizes bits back coding for time-series models with latent Markov structures.
problem Efficiently compressing time-series data with latent Markov structures.
method Extends bits back coding to time-series models with latent Markov structures, including HMMs and LGSSMs.
result Effective for small scale models, promising for larger scale settings like video compression.
Unified framework for uniform signal recovery in nonlinear GCS with 1-bit/quantized measurements.
problem Uniform recovery guarantees for nonlinear generative compressed sensing.
method Unified framework using generalized Lasso and Lipschitz approximation.
result Uniform recovery of all signals in the ball up to an error of ε using approximately O(k/ε^2) samples.
Deep learning compresses L-values for QAM symbols, reducing memory and improving performance.
problem Efficiently storing log-likelihood ratios (L-values) for QAM modulation.
method A deep autoencoder that jointly compresses and reconstructs L-values with a weighted loss function.
result Reduces memory footprint by up to two times with less than 0.1 dB performance loss.
One-bit quantization improves inference speed for Random Features models.
problem Efficient inference on resource-constrained devices.
method Analysis of one-bit quantization in Random Features model.
result Asymptotically, quantizing weights except the last incurs no loss in generalization error.
This paper improves binary embeddings and quantized compressed sensing methods.
problem Distance-preserving binary embeddings and quantization for compressed sensing.
method Quantization of fast Johnson-Lindenstrauss embeddings and bounded orthonormal systems.
result Quantization methods yield reconstruction errors that decay polynomially and exponentially in the number of measurements.
A new method, REC, compresses images by encoding their latent representations efficiently.
problem Efficiently compressing single images with latent representations.
method Relative Entropy Coding (REC) that directly encodes latent representations with codelength close to relative entropy.
result REC is more efficient for single image compression compared to previous methods and is competitive for lossy compression.
FleXOR trains fractional quantization for neural networks, improving accuracy and size.
problem Quantization limits to integer bits restricts compression and accuracy.
method Encryption algorithm with XOR gates for fractional bits during inference.
result FleXOR achieves high accuracy with fractional sub-1-bit weights.
New technique for flow models achieves theoretical compression lengths.
problem No guaranteed computationally efficient codes for flow models.
method Local bits-back coding for flow models.
result Efficient algorithms achieve theoretical codelengths for flow models.
1-bit quantized weights improve LSTM model performance without accuracy loss.
problem Efficiently compress deep neural networks for memory-intensive tasks.
method Iterative weight quantization followed by retraining.
result 1-bit quantized weights maintain accuracy for LSTM models.
New techniques save bits in image compression with upsampling.
problem Lack of context dependence in current image compression methods with upsampling.
method Simple, inexpensive techniques exploiting context to predict Laplace distribution parameters.
result Average savings of 0.645 bits per difference, up to 1.489 bits.
Diffusion models improve image compression at low bit-rates.
problem Efficiently compressing images at very low bit-rates.
method Encoding into an embedding, using diffusion models to refine the embedding iteratively.
result Realistic reconstructions can be generated at extremely low bit-rates.
Paper introduces adversarial lossy compression for video artifacts reduction.
problem Unpleasant reconstruction artifacts in standard video coding schemes at low bit-rates.
method Adversarial lossy video compression model minimizing an adversarial distortion objective.
result Reduction of perceptual artifacts and detail reconstruction under extreme compression.
Differentiable model compression adds noise to parameters during training.
problem Model compression for efficient deployment.
method Adding pseudo quantization noise to model parameters during training.
result Compresses model by more than 8x on ImageNet with 0.3% accuracy loss.
Optimal privacy and accuracy in distributed mean estimation with compression.
problem Achieving optimal accuracy under privacy and communication constraints.
method Compression to reduce communication while maintaining privacy and accuracy.
result Achieves optimal error with significantly reduced communication.
New framework recovers sparse vectors via ReLU networks, achieving near-optimal statistical rate.
problem Robust one-bit compressed sensing with implicit sparsity constraints.
method Unconstrained empirical risk minimization on a ReLU generative network.
result Achieves a statistical rate of m = ~kn log(d/ε^2) for uniform recovery of any G(x0).
This paper resolves BIHT convergence, showing normalization is not necessary in noiseless settings but crucial for robustness.
problem Analyzing convergence and robustness of BIHT for 1-bit compressed sensing.
method Characterizes BIHT convergence and robustness, proving necessity of normalization for robustness under sign corruptions.
result Per-iteration normalization is not necessary for optimal recovery in noiseless settings but is crucial for robustness under sign corruptions.
DJPQ optimizes neural network pruning and quantization for hardware efficiency.
problem Efficiently compress neural networks for hardware inference.
method Joint gradient-based optimization of pruning and quantization into a differentiable loss function.
result Significant reduction in Bit-Operations (BOPs) with minimal accuracy loss.