Wide residual networks achieve low error rates with single-bit weights.
problem Deploying deep neural networks on resource-constrained hardware with low memory.
method Binarizing weights using sign function and scaling factors, applying warm-restart learning rate schedule.
result Achieved error rates of 3.9% on CIFAR-10, 18.5% on CIFAR-100, and 26.0% on ImageNet with 1-bit-per-weight.
Improved SNNs with quantized activations outperform traditional networks.
problem Maintaining SotA accuracy in SNNs with limited bit precision.
method Interpolating between non-spiking and spiking regimes using signal processing tools.
result First hybrid SNN outperforms traditional RNNs in accuracy with reduced bit precision.
Quantum computing improves training of binary neural networks.
problem Training binary neural networks (BiNNs) is challenging.
method Employing a Variational Quantum Algorithm to generate binary weights using quantum circuit measurements.
result The proposed methods improve trainability and generalization of BiNNs.
Paper studies signal detection in noisy environments with limited communication.
problem Signal detection in Gaussian noise with 1-bit communication constraints.
method Derives lower bounds and exhibits optimal testing strategies.
result Optimal distributed testing strategies attain the derived lower bound.
Paper proposes efficient BNN inference techniques on FPGA.
problem Redundancy in BNN inference leading to high computation and data access costs.
method Analyzed image similarity and BNN kernel weights to exploit redundancy. Proposed two types of fast and energy-efficient architectures.
result 80% reduction in computation and 40% in buffer access, achieving 17% power reduction.
Regularized linear regression improves binary classification performance, especially with ridge and ℓ 1 \ell_1 ℓ 1 regularization.
problem Improving binary classification accuracy with noisy labels.
method Systematic study of regularization strengths on linear classifiers trained on noisy binary classification data.
result Ridge regression consistently improves classification error, while ℓ 1 \ell_1 ℓ 1 regularization can induce sparsity and ℓ ∞ \ell_\infty ℓ ∞ regularization can concentrate weights to two values. BENN improves binary neural networks by ensemble methods, boosting accuracy without sacrificing efficiency.
problem Inefficiency and accuracy degradation in binary neural networks.
method Proposes Binary Ensemble Neural Network (BENN) using ensemble techniques.
result BENN outperforms state-of-the-art binary networks and full-precision networks.
New approach replaces BN layers with shifted-ReLU for embedded systems.
problem Complexity and overheads of BN layers in low-power embedded systems.
method Used shifted-ReLU layers instead of BN layers in wide residual networks.
result Shifted-ReLU layers offer advantages in speed, memory, and complexity without significant accuracy loss.
New algorithm tackles batched stochastic linear bandits with 1-bit communication constraints.
problem Stochastic linear bandits with 1-bit communication constraints.
method Phased-elimination algorithms based on G-optimal designs and 1-bit mean estimation.
result Achieves near-optimal regret bounds for broad scaling regimes.
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.
A study on distributed mean estimation with trade-offs between communication and accuracy.
problem Estimating the arithmetic average of distributed vectors with limited communication.
method Proposes a flexible family of randomized algorithms exploring the trade-off between communication cost and estimation error.
result Improves error rate to O ( r / n ) \mathcal{O}(r/n) O ( r / n ) when communicating a single bit per coordinate, where r r r is the bit representation of a floating point value. Estimating mean from one-bit samples of symmetric log-concave distributions.
problem Estimating the mean of a symmetric log-concave distribution with limited one-bit measurements.
method Analyzes mean squared error in three settings: centralized, adaptive, and distributed, with and without quantization.
result One round of adaptivity is sufficient to achieve optimal mean-square error in the adaptive setting.
SATNet integrates logical reasoning into deep learning with a differentiable MAXSAT solver.
problem Integrating logical reasoning into deep learning architectures.
method Differentiable MAXSAT solver based on fast coordinate descent for SDP.
result Minimally supervised learning of logical structures in deep learning systems.
Linear regression studies the problem of estimating a model parameter β ∗ ∈ R p β^* \in \mathbb{R}^p β ∗ ∈ R p , from n n n observations { ( y i , x i ) } i = 1 n \{(y_i,\mathbf{x}_i)\}_{i=1}^n {( y i , x i ) } i = 1 n from linear model y i = ⟨ x i , β ∗ ⟩ + ε i y_i = \langle \mathbf{x}_i,β^* \rangle + ε_i y i = ⟨ x i , β ∗ ⟩ + ε i . We consider a significant generalization in which the relationship between $\langle \mathbf{x}_i,β^* \ran…
Deep learning improves one-bit OFDM receiver performance.
problem One-bit quantization complicates accurate channel estimation and data detection in OFDM receivers.
method Developed deep neural networks for channel estimation and data detection, using a two-step training policy.
result Deep learning-based designs achieve lower BER than unquantized OFDM at moderate SNRs.
Optimizes web crawling with unknown change rates using online estimation.
problem Optimizing web crawling with unknown change rates and limited bandwidth.
method Characterizes conditions for solving the problem online, proposes an estimator, and analyzes regret.
result Explore-and-commit algorithm achieves O ( T ) \mathcal{O}(\sqrt{T}) O ( T ) regret. A multi-player bandit system resists adversarial attacks with near-optimal regret.
problem Adversaries attempt to manipulate rewards in a multi-player multi-armed bandit game.
method Players communicate a single bit to resist attacks, achieving near-optimal regret.
result Achieves near-optimal regret of O ( log 1 + δ T + W ) O(\log^{1+δ}T + W) O ( log 1 + δ T + W ) , where W W W is the total time of adversarial attacks. New algorithms reduce estimation error for functions with large ranges.
problem Estimating means of functions with large ranges efficiently.
method Statistical queries and single-bit communication models.
result Error scales linearly with standard deviation and logarithmically with second moment.
New algorithm learns efficiently with a simple 'yes/no' oracle.
problem Can efficient learning be achieved with a simpler oracle than ERM?
method Developed an oracle that returns 'yes' or 'no' for realizable datasets.
result Learnability is possible with a polynomial price in VC dimension.
Model uses Preisach hysteresis to predict gig worker acceptance, reducing costs and improving fill rates.
problem Predicting and optimizing gig worker acceptance in labor markets.
method Preisach hysteresis model applied to neural network and XGBoost classifier for binary transaction outcomes.
result Model reduces total wage bill by 21.3% and increases expected fill rate by 9.7 pp.
A new weighted MCC measure improves classifier performance evaluation.
problem Lack of measures sensitive to observation weights in multiclass classification.
method Proposes weighted versions of Pearson-Matthews Correlation Coefficient (MCC) for binary and multiclass classification.
result Weighted MCC values are higher for classifiers that perform better on highly weighted observations.
Develops theory of weightings for Lie groupoids and algebroids.
problem Understanding differential geometry of weightings for Lie groupoids and algebroids.
method Extending work on weighted manifolds, defining weighted submanifolds, and developing theories of linear weightings and multiplicative weightings.
result Characterizes infinitesimally multiplicative weightings for Lie algebroids and classifies multiplicative weightings of Lie groupoids.
Stability of weighted extremal manifolds proven through blowups.
problem Stability of weighted extremal manifolds.
method Blowup technique to analyze weighted extremal Kähler manifolds.
result Proves weighted extremal manifolds are relatively weighted K-polystable.
The paper extends spin geometry to weighted manifolds and defines a new mass for Ricci flow.
problem Generalizing spin geometry to weighted manifolds and defining a new mass.
method Investigates spectral properties of the weighted Dirac operator and defines a new mass.
result Defines a new mass for weighted asymptotically Euclidean manifolds and shows its monotonicity under Ricci flow.
Paper generalizes CR Obata theorem to weighted Sasakian manifolds.
problem Deriving eigenvalue estimates for weighted Kohn Laplacian.
method Derived weighted CR Reilly's formula and applied to Sasakian manifolds.
result CR Obata theorem proven for weighted Sasakian manifolds.
New mass and staticity concepts derived from weighted curvature maps.
problem Deriving mass and staticity concepts for weighted manifolds.
method Developed a weighted curvature map and its adjoint, leading to weighted mass and static metrics.
result Equivalence and uniqueness theorems for weighted static manifolds and Penrose inequality.
The study explores weightings on submanifolds and their geometric properties.
problem Understanding weightings on submanifolds and their geometric implications.
method Detailed exploration of weighted normal bundles, weighted deformation spaces, and weighted blow-ups.
result A description of weightings in terms of subbundles of higher tangent bundles, leading to new concepts for Lie algebroids and groupoids.
Proves existence and uniqueness of weighted metrics for smooth spaces.
problem Existence and uniqueness of weighted metrics for smooth metric measure spaces.
method Proves existence and uniqueness using weighted ambient metrics and Poincaré metrics.
result Existence and uniqueness of weighted metrics for smooth metric measure spaces.
Defines and proves properties of weighted renormalized volume coefficients.
problem None explicitly stated; focuses on mathematical definitions and proofs.
method Defines weighted renormalized volume coefficients and proves their variational nature and polynomial representation.
result Weighted renormalized volume coefficients are variational and can be expressed as polynomials of specific tensors.
A new weighted FDA method improves face recognition accuracy.
problem Equal treatment of all class pairs in FDA leads to suboptimal performance.
method Cosine-weighted and automatically weighted FDA methods are proposed.
result Improved face recognition accuracy through weighted FDA.
New invariants help solve existence of weighted cscK metrics.
problem Existence of weighted cscK metrics in K-stability.
method Introduced weighted analytic delta invariant and beta invariant.
result Sufficient condition for existence of weighted cscK metrics.
Proves positive mass theorem for non-spin weighted manifolds.
problem Proving the positive mass theorem for non-spin weighted manifolds.
method Establishing density theorem and generalizing Geroch conjecture.
result Proves positive weighted mass theorem for non-spin weighted manifolds.
Derives integral formulae on weighted manifolds.
problem No specific problem stated; focuses on mathematical derivations.
method Introduces weighted mean sigma-r curvature and uses weighted Newton transformations.
result Derives integral formulae generalizing previous work.
Method measures weight similarity in neural networks using normalization and statistical inference.
problem Quantifying weight similarity in non-convex neural networks.
method Chain normalization rule and hypothesis-training-testing statistical inference.
result Weights of identical neural networks converge to similar local solutions.
The paper studies weighted Ricci curvatures and characterizes Randers metrics.
problem Characterizing Randers metrics with weighted Ricci curvatures.
method General weighted Ricci curvatures and characterization of Randers metrics.
result Characterization of Randers metrics with almost isotropic weighted Ricci curvatures.
Study on stable minimal hypersurfaces under Ricci curvature constraints.
problem Stability of weighted minimal hypersurfaces under Ricci curvature bounds.
method Derive geometric consequences and prove a Schoen-Yau type criterion.
result Structure theorem for three-dimensional weighted manifolds of non-negative Ricci curvature.
The paper classifies vertices in weighted networks using spectral embedding and edge weight distributions.
problem Classifying vertices in weighted networks where edge weights and adjacencies encode class membership.
method Introduced a edge weight distribution matrix to the K-Block Stochastic Block Model for weighted networks. Developed classification procedures based on spectral embedding of the unweighted adjacency matrix under two assumptions on edge weight distributions.
result Proposed classifiers outperform quadratic discriminant analysis on transformed weighted networks.
Explains weightings along submanifolds, focusing on Lie groupoids.
problem None explicitly stated; focuses on theory review.
method Reviews basic notions and emphasizes multiplicative weightings.
result Provides a comprehensive overview of weightings along submanifolds.
The study establishes comparison theorems for weighted Finsler manifolds and spacetimes.
problem Analyzing weighted Finsler manifolds and spacetimes with curvature conditions.
method Using weight function and ε ε ε -range, the Bonnet-Myers theorem, Laplacian comparison theorem, and Bishop-Gromov volume comparison theorem are formulated. result New comparison theorems for weighted Finsler manifolds and spacetimes are derived, including those for weighted Riemannian manifolds.
A new method compresses deep neural networks by predicting and quantizing weights between layers.
problem Resource constraints in deep neural networks.
method Inter-Layer Weight Prediction (ILWP) and quantization based on Smoothly Varying Weight Hypothesis (SVWH).
result The method achieves higher weight compression rates at the same accuracy level.
The paper generalizes K-stability results to singular and weighted settings.
problem Generalizing K-stability to singular and weighted settings.
method Generalization of results in \cite{Li22a} to singular and weighted settings.
result The \(\mathbb{G}\)-uniform weighted K-stability for models implies \(\mathbb{G}\)-coercivity of the weighted Mabuchi functional.
The study analyzes weighted manifolds with curvature bounds, proving eigenvalue estimates and inequalities.
problem Analyzing geometric properties of weighted manifolds under Ricci curvature bounds.
method Develops geometric analysis techniques on weighted Riemannian manifolds with lower 0 0 0 -weighted Ricci curvature bounds. result Proves eigenvalue estimates for Steklov and ABP inequalities on weighted manifolds.
Adaptive learning of sample weights for better model performance.
problem Overfitting to biased training data with corrupted labels or class imbalance.
method Adaptive learning of an explicit weighting function using a meta-weight-net.
result Improves model accuracy in class imbalance and noisy label cases.
Study on deformation of weighted scalar curvature, proving geometric results and stability.
problem Deformation of weighted scalar curvature and related geometric properties.
method Linearization of weighted scalar curvature, studying kernel of formal adjoint.
result Definition and study of weighted vacuum static spaces, stability results on flat spaces.
This paper reviews weighted clustering ensemble methods.
problem Improving clustering results from individual methods.
method Different types of weights and approaches to determining weight values.
result Unified framework for selecting appropriate weighting mechanisms.
New algorithms avoid weight transport, outperforming current deep learning methods.
problem Current deep learning algorithms rely on weight transport, which is biologically implausible.
method Two mechanisms: weight mirror and modified Kolen-Pollack algorithm, using random feedback weights.
result These mechanisms outperform feedback alignment and other methods on visual recognition tasks.
Reverse-weighted portfolios outperform in commodity futures markets.
problem Efficiency of commodity futures markets.
method Permutation-weighted portfolios, rank-based methods.
result Reverse-weighted portfolio outperforms price-weighted portfolio.
The Penrose theorem and Hawking's topology theorem are extended to weighted spacetimes.
problem Extending Penrose's singularity theorem and Hawking's topology theorem to weighted spacetimes.
method Using weighted null energy condition and synthetic dimension to generalize the theorems.
result Generalized versions of the Penrose and Hawking theorems hold under a weighted null energy condition.