Power weighted shortest paths improve clustering of high-dimensional data.
problem Clustering high-dimensional Euclidean data with disjoint low-dimensional manifolds.
method Use of power weighted shortest path distance functions and a fast algorithm.
result Higher clustering accuracy achieved through power weighted shortest paths.
Extended quantum state result for gl_n weight systems.
problem Quantum states associated with gl_n weight systems.
method Extended Corfield et al. result to all gl_n weight systems.
result All gl_n weight systems are quantum states.
Study of metrics on positive-definite matrices from power potential, linking to power means.
problem Understanding metrics on positive-definite matrices derived from power potential.
method Explicit expressions for geodesics and distance function derived from Hessian of power potential.
result Geodesics and distance function converge to weighted matrix geometric mean as β tends to zero.
Paper develops a weighted linearization approach for vector fields.
problem Linearizability of vector fields under weighted conditions.
method Formal Moser trick applied to power series, addressing weighted non-resonance condition.
result Formal Moser trick works over any field of characteristic zero.
A new test method improves goodness-of-fit tests for copulas.
problem Developing robust tests for copula goodness-of-fit.
method Binary Expansion Approximation of UniformiTY (BEAUTY) and Binary Expansion Adaptive Symmetry Test (BEAST).
result The BEAST method improves empirical power against various alternatives.
APoT quantization improves neural network efficiency and accuracy.
problem Efficiently quantizing weights and activations in neural networks.
method Constraining quantization levels as sums of Powers-of-Two terms, applying reparameterization, and weight normalization.
result 4-bit quantized ResNet-50 achieves 76.6% top-1 accuracy, 22% computational cost reduction.
Power-SMC reduces inference latency for training-free LLM reasoning.
problem Training-free LLM reasoning with low latency.
method Power-SMC, a training-free Sequential Monte Carlo scheme targeting sequence-level power distribution.
result Power-SMC reduces inference latency from 16-28× to 1.4-3.3× over baseline decoding.
Power-law portfolios improve diversification by scaling weights sub-linearly.
problem Optimization methods struggle with unstable pair correlations and non-Gaussian risk measures.
method Construct portfolios with penalty proportional to arbitrary order moment of returns, leading to sub-linear weight scaling.
result Infinite order power-law portfolios are perfectly diversified, improving diversification over Kelly portfolios.
Enhances power of covariance matrix tests for high-dimensional data.
problem Testing large covariance matrices in high-dimensional data.
method Proposes a new Fisher's combined probability test for quadratic form and maximum form statistics.
result Boosts power against more general alternatives.
New Stein operator improves robustness in model inference.
problem Improving robustness in inference for unnormalized models.
method Density-power weighted Stein operator (γ-Stein operator). result Robust methods for goodness-of-fit testing and posterior approximation.
Sparse oblique decision tree improves security rules for renewable power systems.
problem Identifying secure operating conditions in power systems with high renewable energy.
method Sparse weighted oblique decision tree to learn and embed linear security rules.
result The method significantly increases secure states and reduces solution time.
Paper explains DRL strategies for portfolio management using linear models.
problem Difficulty in understanding DRL-based trading strategies.
method Empirical approach using linear models and integrated gradients.
result DRL agents show stronger multi-step prediction power than machine learning methods.
We propose an explicit recursive method to approximate a power-law with a finite sum of weighted exponentials. Applications to moving averages with long memory are discussed in relationship with stochastic volatility models.
Paper applies FloatSD8 to LSTM networks, reducing complexity and power.
problem Training and inference complexity of LSTM networks.
method Applied FloatSD8 for weights, 8-bit quantization for gradients/activations, reduced arithmetic precision.
result Successfully trained LSTM models with reduced complexity and preserved accuracy.
New method explains GNNs using power iteration clustering.
problem Mysterious mechanism of message passing in GNNs.
method Subspace power iteration clustering (SPIC) models.
result Message passing in GNNs can be understood through power iteration.
A new method for anomaly detection adapts to local non-stationarity in low-data regimes.
problem Adapting conformal anomaly detection to handle distribution shifts in real-world data.
method Proposes a continuous inference relaxation using continuous weighted kernel density estimation to decouple local adaptation from tail resolution.
result Restores detection capabilities and statistical power in low-data regimes while maintaining valid error control.
Proposes a new complex Gaussian distribution for better modeling of complex-valued signals.
problem Limited ability of Gaussian distribution to represent diverse amplitude characteristics.
method Introduces a power-weighted noncentral complex Gaussian distribution on the complex plane.
result Consistently outperforms conventional distributions in log-likelihood for speech power spectra.
Hyperflux models pruning as a system to reveal weight importance.
problem Pruning large neural networks to reduce latency and power consumption.
method Introduces Hyperflux, a novel L0 method that models pruning as flux and pressure. result Achieves competitive results with ResNet-50, VGG-19, and DeiT-T/S on various datasets.
PPI uses predictions and weighting to infer from partially labeled data.
problem Valid inference with partially labeled data.
method Combines model-based predictions with bias correction from labeled data, using Horvitz-Thompson and Hájek corrections.
result IPW-adjusted PPI with estimated propensities performs similarly to known-probability case.
Graph NNs lose predictive power exponentially with more layers.
problem Graph Neural Networks (graph NNs) lose predictive power exponentially with more layers.
method Generalized the forward propagation of a Graph Convolutional Network (GCN) as a dynamical system and analyzed its asymptotic behaviors.
result GCNs' output exponentially approaches signals related to node degrees and connected components, leading to 'information loss' in the limit of infinite layers.
Deep learning detects pneumonia with 36x compression on low-power devices.
problem High accuracy pneumonia detection on low-power embedded devices.
method Structured weight pruning method for compression and maintaining accuracy.
result Up to 36x compression ratio with no accuracy loss.
This paper proposes a method to improve few-shot learning by generating multi-level weight-centric features.
problem Improving few-shot learning performance by leveraging both representation power and weight generation capacity.
method A multi-level weight-centric feature learning approach with a weight-centric training strategy and multi-level feature incorporation.
result Significantly outperforms existing methods in low-shot classification benchmarks.
In the past years, Deep convolution neural network has achieved great success in many artificial intelligence applications. However, its enormous model size and massive computation cost have become the main obstacle for deployment of such powerful algorithm in the low power and resource-limited mobile systems. As the c…
POWSS simplifies Q-value estimation in POMDPs with continuous observations.
problem Lack of theoretical justification for online sampling-based algorithms in POMDPs with continuous observation spaces.
method Developed POWSS, a simplified algorithm that estimates Q-values accurately with high probability and can approach optimality with increased computational power.
result POWSS provides formal theoretical guarantees for Q-value estimation in POMDPs with continuous observations.
Sharp inequality on Siegel domain involving weighted norms and sub-Laplacian.
problem Establishing a Sobolev trace inequality on a specific domain.
method Using weighted norms and fractional powers of sub-Laplacian on Heisenberg group.
result Sharp Sobolev trace inequality on Siegel domain involving weighted norms.
Bayesian framework improves minority class performance in class-imbalanced data.
problem Class imbalance in predictive toxicology models.
method Weighted likelihood approach modifying likelihood function weights inversely proportional to class proportions.
result Improves balanced accuracy and sensitivity for minority class (toxic compounds).
We empirically show the superiority of the equally weighted S\&P 500 portfolio over Sharpe's market capitalization weighted S\&P 500 portfolio. We proceed to consider the MaxMedian rule, a non-proprietary rule designed for the investor who wishes to do his/her own investing on a laptop with the purchase of only 20 stoc…
We present a growing dimension asymptotic formalism. The perspective in this paper is classification theory and we show that it can accommodate probabilistic networks classifiers, including naive Bayes model and its augmented version. When represented as a Bayesian network these classifiers have an important advantage:…
Develops a new weighted Laplacian method for graph problems.
problem Graph partitioning and balanced minimum cut problems.
method Weighted Laplacian method based on graph theory and PDEs.
result Established equivalence relations among graph problems.
This paper quantifies how well random neural networks can approximate continuous functions.
problem Approximating continuous functions with random neural networks.
method Investigates three types of random neural networks: infinite width, subsampled, and corrected. Analyzes approximation rates and provides bounds.
result A function can be approximated with complexity proportional to δ and d. We consider multi-task regression models where the observations are assumed to be a linear combination of several latent node functions and weight functions, which are both drawn from Gaussian process priors. Driven by the problem of developing scalable methods for forecasting distributed solar and other renewable powe…
Prove top wedge power of Ricci form has finite integral for Kähler manifolds with positive sectional curvature
problem Prove top wedge power of Ricci form has finite integral for Kähler manifolds with positive sectional curvature
method Use Bézout estimates and a Lipschitz weight with finite Monge-Ampère mass
result Prove top wedge power of Ricci form has finite integral for Kähler manifolds with positive sectional curvature
New isoperimetric inequalities in the plane with radial weights identified.
problem Finding isoperimetric shapes in the plane with radial power weights.
method Analyzing the punctured plane with specific volume and perimeter densities.
result Centred balls are uniquely isoperimetric under certain conditions.
Bit-slice sparsity improves ReRAM-based DNN acceleration.
problem Limited ADC power and area constraints in ReRAM-based DNN accelerators.
method Proposed bit-slice L1 algorithm to induce sparsity during training.
result 2x sparsity improvement compared to previous methods.
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.
The paper computes characteristic classes for Lie group representations.
problem Computing characteristic classes for Lie group representations.
method The paper outlines a procedure to compute characteristic classes of irreducible representations of Lie groups, expressing them as polynomial functions in the highest weight.
result The paper expresses characteristic classes of Lie group representations as polynomial functions in the highest weight.
Improves Lasso's stability in correlated predictor settings.
problem Lasso's selection stability deteriorates with correlated predictors.
method Integrates a weighting scheme into the Lasso penalty function, using a correlation-adjusted ranking.
result Demonstrates improved selection stability on simulated and real-world datasets.
WTM reduces clause usage and computation time for pattern recognition.
problem High computation time and memory usage in Tsetlin Machine.
method Weighting clauses and using binomial sampling to reduce complexity.
result WTM achieves similar accuracy with fewer clauses and faster training.
Paper tackles time inconsistency in portfolio management with stochastic volatility and power utility.
problem Time inconsistency in portfolio management with stochastic volatility and power utility.
method Extended Hamilton Jacobi Bellman (HJB) equation, fixed point iteration, and linear parabolic PDE.
result Subgame perfect strategies are characterized and solved through numerical experiments.
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.
Transfer Learning (TL) in Deep Neural Networks is gaining importance because in most of the applications, the labeling of data is costly and time-consuming. Additionally, TL also provides an effective weight initialization strategy for Deep Neural Networks . This paper introduces the idea of Adaptive Transfer Learning …
One unexamined assumption in foreign ownership regulation is the notion that majority voting rights translate to 'effective control'. This assumption is so deeply entrenched in foreign investments law that possession of majority voting rights can determine the nationality of a corporation and its capacity to engage in …
Deep polynomial neural networks measure their expressiveness by the dimension of their functional space.
problem Measuring the expressiveness of deep polynomial neural networks.
method Analyzing the algebraic variety defined by the polynomial neural network's weights and activations.
result The dimension of the algebraic variety is a precise measure of the network's expressiveness.
Random weights in GNNs match learned weights in performance.
problem Feature rank collapse in GNNs.
method Replacing learned weights with random weights.
result Random weights achieve comparable performance to learned weights, reducing training time and memory usage.
FPGAs accelerate DNNs with binarized weights, improving power and speed.
problem Power and speed limitations of GPU-accelerated DNNs.
method Binarized neural networks on FPGAs using OpenCL.
result Near state-of-the-art performance with >16x power savings.
A new algorithm reduces communication in distributed SVD by p factors.
problem Efficiently compute SVD in distributed systems.
method LocalPower algorithm with weighted aggregation and periodic decay of iterations.
result Reduces communication cost by a factor of p. New geometric analysis of PWSPDs balances density and geometry in high-dimensional data.
problem Balancing density and geometry in high-dimensional data.
method Power-weighted shortest-path distances (PWSPDs) and their geometric and computational analyses.
result High probability guarantees on the equivalence of PWSPDs on complete and nearest neighbor graphs.
Paper uses SC to estimate hidden interference for WSRM.
problem Maximizing sum-rate with hidden interfering sources.
method Synthetic control (SC) for estimating counterfactual interference in WMMSE.
result SC-WMMSE outperforms original WMMSE in convergence and objective.