Privacy-preserving identification framework using sparse approximation and privacy amplification.
problem Protecting privacy in identification applications like biometrics and IoT.
method Sparse approximation with privacy amplification, not symmetric for data owner and user.
result Preserves privacy at low computational cost, storage, and communication burdens.
Paper addresses data reconstruction from privacy-protected templates using STCA.
problem Reconstructing privacy-sensitive data from protected templates.
method Sparse ternary coding with ambiguization (STCA) for privacy preservation.
result STCA maintains theoretical performance against deep reconstruction attacks for synthetic data but requires special measures for real images.
Deep learning produces efficient ternary projections for image compression.
problem Efficiently compress and reconstruct sparse signals from incomplete measurements.
method End-to-end deep learning architecture for learning projection matrices and reconstruction operators.
result Deep learning approach yields more efficient ternary projections compared to state-of-the-art methods.
EC2T creates sparse and ternary neural networks for resource-constrained devices.
problem Deploying deep neural networks on resource-constrained devices.
method Entropy-Constrained Trained Ternarization (EC2T) framework.
result EC2T creates sparse and ternary neural networks that are efficient in terms of storage and computation.
ProSper learns data components with non-standard priors and superpositions.
problem Learning complex data components with non-standard priors and superpositions.
method Probabilistic algorithms for sparse coding with non-standard priors and superpositions.
result Library supports scalable and parallelizable dictionary learning for large-scale applications.
A new method for joint noise removal and trend estimation from sparse signals.
problem Jointly removing noise and estimating trends from sparse signals.
method PENDANTSS combines SOOT/SPOQ penalties with BEADS algorithm in a Trust-Region block alternating variable metric forward-backward approach.
result Outperforms comparable methods in deconvolving analytical chemistry signals.
The group of C1-diffeomorphisms of any sparse Cantor subset of a manifold is countable and discrete (possibly trivial). Thompson's groups come out of this construction when we consider central ternary Cantor subsets of an interval. Brin's higher dimensional generalizations nV of Thompson's group V arise…
Paper ranks stocks by compression risk, not volatility.
problem Investment risk not correlated with stock price volatility.
method Binary-ternary compressive coding of price change time series.
result Compression risk is a better indicator of stock investment risk.
Defines ternary group homology for knot theory applications.
problem Understanding ternary groups and their homology.
method Developed a homology theory for ternary groups using associativity and skew elements.
result Discussed applications of ternary knot groups.
The paper extends ternary algebra concepts using cube roots of unity.
problem Extending algebraic structures from binary to ternary multiplication.
method Introducing ternary associator, commutator, and Lie algebra at cube roots of unity.
result Derived an identity for ternary commutator based on GA(1,5). Ternary groups from knot theory help classify curves.
problem Characterizing ternary groups with knot theory axioms.
method Using semi-commutativity and Reidemeister moves.
result Constructs a curve invariant under Reidemeister moves.
Novel ternary structures reveal new interpretations of linear connections.
problem Examining the ternary structure of Lie algebroid connections.
method Study of endomorphisms and explicit presentation of the endomorphism truss.
result Explicitly presented endomorphism truss of linear connections.
We introduce a notion of ternary distributive algebraic structure, give examples, and relate it to the notion of a quandle. Classification is given for low order structures of this type. Constructions of such structures from ternary bialgebras are provided. We also describe ternary distributive algebraic structures com…
New invariants derived from ternary quasigroups for knot theory.
problem Developing invariants for knot theory using ternary quasigroups.
method Defined subcomplexes and cocycle invariants based on normalized homology.
result Cocycle invariants defined for ternary quasigroups satisfying specific conditions.
New cohomology theories for heaps and ternary operations linked to group cohomology.
problem Defining and studying cohomology theories for heaps and ternary operations.
method Introduced para-associative and heap cohomology theories, and ternary self-distributive cohomology with abelian heap coefficients.
result Heap cohomology is related to group cohomology via a long exact sequence, and injects into ternary self-distributive cohomology.
Introduces Lie semiheaps and their relation to Lie groups and bundles.
problem Defining and understanding Lie semiheaps and their properties.
method Introducing Lie semiheaps and proving their properties in relation to Lie groups and bundles.
result Established the existence of left-invariant vector fields on Lie semiheaps.
We define homology of ternary algebras satisfying axioms derived from particle scattering or, equivalently, from the third Reidemeister move. We show that ternary quasigroups satisfying these axioms appear naturally in invariants of Reidemeister, Yoshikawa, and Roseman moves. Our homology has a degenerate subcomplex. T…
Binary and ternary weights simplify RNNs for mobile devices.
problem Complexity and memory intensity of RNNs on mobile devices.
method Learn binary and ternary weights during training.
result Significant memory saving and inference speedup on ASIC platform.
Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the m…
Sparse coding improves reinforcement learning representations.
problem Improving representation learning in reinforcement learning.
method Developed a supervised sparse coding objective for policy evaluation.
result Sparse coding representations outperform tile-coding representations.
Ternary MobileNets improve efficiency and accuracy on constrained devices.
problem Efficiently compressing MobileNets for real-time applications on constrained devices.
method Per-layer hybrid filter banks for ternary quantization of MobileNets.
result 27.98% energy savings and 51.07% reduction in model size with comparable accuracy.
The paper constructs new algebraic structures from Lie algebras and ternary Nambu-Lie algebras, leading to Yang-Baxter operators.
problem Constructing new algebraic structures from Lie algebras and ternary Nambu-Lie algebras.
method Using compositions of binary Lie algebras, 3-Lie algebras, and ternary Nambu-Lie algebras, the paper constructs ternary self-distributive objects and Yang-Baxter operators.
result The constructed Yang-Baxter operators are not gauge equivalent to the transposition operator and can be deformed to new solutions.
Smart Quantization adapts binary and ternary quantization for neural networks.
problem Resource constraints in deploying neural networks on devices with limited resources.
method Adaptive combination of binary and ternary quantization with a regularization function.
result Adapts quantization depth during training to maintain high model accuracy.
New algebraic structure for vector bundles with special properties.
problem Developing new algebraic structures for vector bundles.
method Introducing para-associative algebroids and showing local triviality conditions.
result Existence of a differential connection is necessary and sufficient for local triviality.
Study of SO(3)-irreducible geometry in complex 5D and ternary Pauli exclusion principle.
problem Exploring SO(3)-irreducible geometry in complex 5D.
method Defined a ternary skew-symmetric tensor, split the 10D space into irreducible SO(3) subspaces, found invariants and defined geometric structures.
result Defined a SO(3)-irreducible geometric structure on a 5D complex Hermitian manifold.
Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics, etc. However, the conventional sparse coding algorithms and its manifold regularized variants (graph sparse coding and Laplacian sparse coding), learn th…
New quantum code lacks sparse lift.
problem Existence of sparse lifts for quantum codes.
method Constructed a sparse Z2 chain complex without a sparse lift. result Found a quantum code without a sparse lift.
Inspired by recent work on convex formulations of clustering (Lashkari & Golland, 2008; Nowozin & Bakir, 2008) we investigate a new formulation of the Sparse Coding Problem (Olshausen & Field, 1997). In sparse coding we attempt to simultaneously represent a sequence of data-vectors sparsely (i.e. sparse approximation (…
Sparse coding has shown its power as an effective data representation method. However, up to now, all the sparse coding approaches are limited within the single domain learning problem. In this paper, we extend the sparse coding to cross domain learning problem, which tries to learn from a source domain to a target dom…
We introduce a way to color the regions of a classical knot diagram using ternary operations, so that the number of colorings is a knot invariant. By choosing appropriate substitutions in the algebras that we assign to diagrams, one obtains the relations from the knot group, and from the core group. Using the ternary o…
Sparse codes improve optimal control tasks with correlated inputs.
problem Optimal control tasks with correlated feature inputs.
method Used a sparse code to represent natural images in an optimal control task solved with neuro-dynamic programming.
result An over-complete sparse code increases memory capacity and learning speed beyond a complete code.
Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been conside…
Paper introduces efficient algorithm for double-sparse coding with theoretical guarantees.
problem High storage and processing costs in sparse coding for high-dimensional data.
method Simple algorithm for double-sparse coding, leveraging neural architectures for efficiency.
result Theoretical analysis shows asymptotic sample complexity and running time benefits over existing methods.
Study confirms sparse coding in whole brain using MRI data.
problem Sparse coding in the whole brain's neural activities.
method Applied various matrix factorization methods to fMRI data.
result Sparse coding hypothesis in information representation in the whole human brain is confirmed.
New method learns sparse distributions by thresholding samples, improving performance and efficiency.
problem Sparse coding optimization in high-dimensional problems is computationally expensive and inefficient.
method Proposes a new variational sparse coding approach that learns sparse distributions by thresholding samples.
result Shows superior performance, statistical efficiency, and gradient estimation compared to other sparse distributions.
Paper develops a decoder for sparse codes without encoder matrix, achieving optimal recovery.
problem Designing a decoder for sparse codes from linear measurements alone.
method Matrix factorization to recover encoder and sparse coding matrices from measurements.
result Decoder-Expander Based Factorisation recovers encoder and sparse coding matrix at optimal measurement rate with high probability.
We present a comprehensive framework for structured sparse coding and modeling extending the recent ideas of using learnable fast regressors to approximate exact sparse codes. For this purpose, we develop a novel block-coordinate proximal splitting method for the iterative solution of hierarchical sparse coding problem…
Unified cosmological and Einstein polytope theories.
problem Unified understanding of cosmological and Einstein polytope theories.
method Unified combinatorial perspective of cosmological and Einstein polytope theories.
result Unified construction of cosmological and Einstein polytope theories.
Deep sparse coding models resist adversarial examples.
problem Adversarial examples can fool deep learning models.
method Used deep sparse coding models to resist adversarial examples.
result Deep sparse coding models are robust to adversarial examples.
New method trains neural networks with binary or ternary weights efficiently.
problem Training high-precision, real-time neural networks on limited hardware.
method Local reparameterization trick modification for discrete weights.
result State-of-the-art results on binary and ternary models on various benchmarks.
Paper proposes semi-supervised method for dictionary learning.
problem Learning from both labeled and unlabeled data.
method Uses semi-supervised dictionary learning with LLE for manifold preservation.
result Significant improvements over other methods demonstrated.
There is a pressing need to build an architecture that could subsume these networks under a unified framework that achieves both higher performance and less overhead. To this end, two fundamental issues are yet to be addressed. The first one is how to implement the back propagation when neuronal activations are discret…
New gradient codes use sparse graphs to speed up distributed computation with slight inaccuracy.
problem Straggler effect in distributed algorithms causing slowest nodes to dictate overall time.
method Sparse random graphs for gradient coding to achieve approximate accuracy.
result Approximate gradient codes can significantly increase robustness to stragglers.
In compressed sensing, we wish to reconstruct a sparse signal x from observed data y. In sparse coding, on the other hand, we wish to find a representation of an observed signal y as a sparse linear combination, with coefficients x, of elements from an overcomplete dictionary. While many algorithms are competit…
Quantum invariant derived from ternary cohomology of self-distributive structures.
problem Defining and proving a quantum invariant from ternary cohomology.
method Constructing a ribbon category from a TSD set, showing it coincides with the cocycle invariant.
result The ribbon cocycle invariant is a quantum invariant.
Recent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry. This paper tackles the problem of sparse coding and dictionary learning in the space of symmetric positive definite matrices, which form a Riemannian manifold. With the aid of…
A new greedy method tackles ℓ0,∞ sparse coding for better image processing.
problem Imbalanced sparsity in ℓ0 and ℓ1 norms for image processing. method Greedy matching pursuit for ℓ0,∞ norm optimization. result Efficient method for ℓ0,∞ sparse coding and dictionary learning. New knot coloring layers simplify homology calculations.
problem Complex knot homology calculations.
method Inductive ternary quasigroup colorings and higher degree homology.
result More efficient homology group access.