Deep neural network detects falls from IoT sensor data.
problem Detecting irregular patterns in IoT streaming data for fall detection.
method Deep neural network model using accelerometer data.
result 98.75 percent accuracy in detecting falls.
On-device research index
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A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
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48 results for “IBM Watson”
Voice-controlled e-commerce app enhances user experience for visually impaired.
problem Limited user experience for visually impaired in e-commerce applications.
method Proposes a voice-controlled e-commerce application using IBM Watson speech-to-text.
result Demonstrates enhanced usability for visually impaired users.
Deep learning (DL) training-as-a-service (TaaS) is an important emerging industrial workload. The unique challenge of TaaS is that it must satisfy a wide range of customers who have no experience and resources to tune DL hyper-parameters, and meticulous tuning for each user's dataset is prohibitively expensive. Therefo…
This note provides an error bound for the Hartman-Watson integral's leading term.
problem Bounding the error of the leading term of the Hartman-Watson integral.
method Asymptotic expansion analysis focusing on the regime constant.
result The error term is bounded uniformly as .
This paper models stock prices using a Janardan Galton Watson process.
problem Modeling stock price fluctuations and predicting market trends.
method Extends Janardan Galton Watson process to model stock prices, considering initial close price and number of offspring.
result The model predicts return values and probability of market extinction.
A new method TNW-CATE estimates treatment effects using neural networks.
problem Estimating heterogeneous treatment effects with limited controls and many treatments.
method Trainable Nadaraya-Watson regression with shared parameters neural network.
result TNW-CATE outperforms traditional methods in various simulation experiments.
New method handles missing data using AI for efficient inference.
problem Parameter estimation and inference with blockwise missing data.
method Tractable solution using AI models and semiparametric theory.
result IBM(RAY) and IBM(Adaptive) estimators achieve efficiency gains.
Barrieu, Rouault, and Yor [J. Appl. Probab. 41 (2004)] determined asymptotics for the logarithm of the distribution function of the Hartman-Watson distribution. We determine the asymptotics of the density. This refinement can be applied to the pricing of Asian options in the Black-Scholes model.
Paper provides an upper bound for bias of Nadaraya-Watson kernel regression.
problem Estimating bias of Nadaraya-Watson kernel regression for finite bandwidths.
method Proposes an upper bound for bias under Lipschitz assumptions, extending to discontinuous derivatives and multidimensional domains.
result Upper bound on bias for finite bandwidths, tighter than previous infinitesimal bandwidth analysis.
New insights into Nadaraya-Watson interpolators show varied generalization behaviors.
problem Understanding generalization of interpolating predictors, especially in noisy data.
method Revisiting Nadaraya-Watson estimator with a single hyperparameter.
result Multiple overfitting behaviors exist, ranging from catastrophic to tempered.
We show that any exceptional non-trivial Dehn surgery on a twist knot, except the trefoil, yields a 3-manifold whose fundamental group is left-orderable. This is a generalization of a result of Clay, Lidman and Watson, and also gives a new supporting evidence for a conjecture of Boyer, Gordon and Watson.
We study the secondary structure of RNA determined by Watson-Crick pairing without pseudo-knots using Milnor invariants of links. We focus on the first non-trivial invariant, which we call the Heisenberg invariant. The Heisenberg invariant, which is an integer, can be interpreted in terms of the Heisenberg group as wel…
The purpose of this paper is to provide a sharp analysis on the asymptotic behavior of the Durbin-Watson statistic. We focus our attention on the first-order autoregressive process where the driven noise is also given by a first-order autoregressive process. We establish the almost sure convergence and the asymptotic n…
Authors derive the first two terms of the Hartman-Watson distribution's expansion for small t.
problem The Hartman-Watson distribution's integral density is difficult to evaluate numerically for small t.
method Saddle point methods and numerical estimates of the integrand.
result Obtained the first two terms of the expansion of the Hartman-Watson distribution.
We prove that the link of a complex normal surface singularity is an L--space if and only if the singularity is rational. This via a recent result of Hanselman, J. Rasmussen, S. D. Rasmussen and Watson (proving the conjecture of Boyer, Gordon and Watson), shows that a singularity link is not rational if and only if its…
Boyer, Gordon, and Watson have conjectured that an irreducible rational homology 3-sphere is an L-space if and only if its fundamental group is not left-orderable. Since large classes of L-spaces can be produced from Dehn surgery on knots in the 3-sphere, it is natural to ask what conditions on the knot group are suffi…
A new loss function for VAEs improves image quality and efficiency.
problem Training VAEs to generate realistic images requires a loss function that reflects human perception.
method Based on Watson's perceptual model, the loss function computes a weighted distance in frequency space, accounts for luminance and contrast masking, and is extended to color images.
result VAEs trained with the new loss function generated high-quality, less blurry images with fewer artifacts and less computational resources.
Nadaraya-Watson kernel smoothing as a random energy modelcond-mat.dis-nn
New insights into simple kernel smoothing reveal surprising asymptotics.
problem Understanding precise asymptotics of Nadaraya-Watson kernel smoothing.
method Using ideas from the random energy model in statistical physics.
result Sharp asymptotics for the NW predictor on the sphere.
Quantum computing offers new solutions for finance problems.
problem Challenging classical computational problems in finance.
method Quantum algorithms for finance applications.
result Potential benefits for financial services.
It has been recently conjectured by Boyer-Gordon-Watson that a closed, orientable, irreducible -manifold is a Heegaard Floer -space if and only if is not left-orderable. In this article, we study this conjecture from the point of view of lattice cohomology, an invariant introduced by Némethi which is…
Estimates time-series drifts from i.i.d. data using a direct Nadaraya-Watson plug-in method.
problem Nonparametric estimation of Schrödinger bridge drifts from single time interval data.
method Direct Nadaraya-Watson plug-in estimator based on kernelized numerator and denominator terms.
result Uniform non-asymptotic bound, CLT under undersmoothing, and adaptive bandwidth selector.
Using an invariant defined by Rasmussen, we extend an argument given by Hedden and Watson which further restricts which Alexander polynomials can be realized by -space knots.
Study finds a small correction to Asian option volatility.
problem Implied volatility of Asian options at short maturity.
method Large deviations property and asymptotic expansion for the Hartman-Watson distribution.
result Subleading correction to Asian option volatility is derived.
Quantum computers can optimize foreign exchange reserves management.
problem Optimizing foreign exchange reserves management using quantum computing.
method Demonstrated through quantum Monte Carlo risk measurement and quantum algorithms for portfolio optimization.
result Quantum computers can theoretically optimize FX reserves management in the future.
Boyer, Gordon, and Watson have conjectured that an irreducible rational homology 3-sphere is an L-space if and only if its fundamental group is not left-orderable. Since Dehn surgeries on knots in can produce large families of L-spaces, it is natural to examine the conjecture on these 3-manifolds. Greene, Lewalle…
Improved local feature attributions using neighbourhood reference distributions.
problem Misleading results from global population in local model behaviour.
method Formulation of neighbourhood reference distributions and self-normalised importance sampling.
result Neighbourhood Shapley values provide meaningful sparse feature attributions.
New method uses quantum computing to process classical data efficiently.
problem Inefficient quantum machine learning due to data loading and trainability issues.
method Linear Hamiltonian-based machine learning with ground state problems for k-local Hamiltonians.
result Demonstrated the effectiveness and scalability of the method on up to 50 qubits.
We show that any exceptional non-trivial Dehn surgery on a hyperbolic two-bridge knot yields a 3-manifold whose fundamental group is left-orderable. This gives a new supporting evidence for a conjecture of Boyer, Gordon and Watson.
The paper introduces a method to quantify uncertainty in neural networks without parametric assumptions.
problem Uncertainty quantification for neural network predictions.
method Nonparametric estimation of conditional label distribution using Nadaraya-Watson kernel.
result The method effectively disentangles aleatoric and epistemic uncertainties.
Causal inference analysis is the estimation of the effects of actions on outcomes. In the context of healthcare data this means estimating the outcome of counter-factual treatments (i.e. including treatments that were not observed) on a patient's outcome. Compared to classic machine learning methods, evaluation and val…
Let L \subset S^3 denote an alternating link and Sigma(L) its branched double-cover. We give a short proof of the fact that the fundamental group of Sigma(L) admits a left-ordering iff L is an unlink. This result is originally due to Boyer-Gordon-Watson.
A new polynomial invariant for strongly involutive links.
problem Characterizing strongly involutive links using polynomial invariants.
method Introducing a two-variable polynomial invariant \(P^e\) with equivariant skein relations.
result Specialisation of \(P^e\) recovers the graded Euler characteristic of a spectral sequence.
Generative models use kernel smoothing for conditioning on small example sets.
problem Improving generative models' performance with limited conditioning examples.
method Showed that cross-attention conditioning is equivalent to kernel smoothing, specifically a Nadaraya--Watson kernel smoother.
result The approach predicts and confirms three failure regimes for kernel-based conditioning.
Study on predicting graph labels at nodes using local averaging and distance estimation.
problem Predicting graph labels at nodes given observations at other nodes.
method Local averaging and distance estimation methods for graph regression.
result Alternative methods can achieve standard nonparametric rates even when graph neighborhoods are too large or small.
Efficient classifier with uncertainty bounds for safety-critical applications.
problem Lack of uncertainty bounds in high-accuracy classifiers for safety-critical tasks.
method Nadaraya-Watson estimator with frequentist bounds.
result Competitive accuracy and uncertainty bounds at reduced computational cost.
We treat the problem of estimation of orientation parameters whose values are invariant to transformations from a spherical symmetry group. Previous work has shown that any such group-invariant distribution must satisfy a restricted finite mixture representation, which allows the orientation parameter to be estimated u…
TAP transfers knowledge from unlabeled data to improve cross-modal learning.
problem Improving supervised learning performance using unlabeled data from a different modality.
method Probabilistic approach for missing information estimation, kernel regression, cross-attention module, TAP neural network.
result TAP significantly improves generalization across different domains and neural network architectures.
The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart and singul…
This study improves estimation of locally stationary functional time series using NW method.
problem Accurately capturing time-dependence in locally stationary functional time series with time-varying covariates.
method Nadaraya-Watson (NW) estimation procedure for the conditional distribution of LSFTS.
result Established convergence rates of NW estimator for LSFTS with respect to Wasserstein distance.
New research shows IBM's GDX algorithm outperforms Vytelingum's Adaptive-Aggressive strategy in market simulations.
problem Comparing the performance of adaptive-aggressive trading algorithms in various market scenarios.
method Exhaustive testing across a wide range of market environments using large-scale compute facilities.
result Vytelingum's Adaptive-Aggressive strategy is consistently outperformed by IBM's GDX algorithm in simple market conditions.
We show that the resulting manifold by -surgery on a large class of two-bridge knots has left-orderable fundamental group if the slope satisfies certain conditions. This result gives a supporting evidence to a conjecture of Boyer, Gordon and Watson that relates -spaces and the left-orderability of their funda…
The paper explores statistical limits for detecting correlation in tree structures.
problem Detecting correlation between two tree structures.
method Investigates conditions for existence of one-sided tests in the limit of large tree depth.
result Identifies a phase transition at correlation parameter , where tests exist for .
Quantum models use complex Hilbert spaces for uncertainty.
problem Modeling dynamics in continuous-valued features.
method Quantum Graphical Models (QGMs) and Hilbert Space Embedding (HSE).
result HSE-HQMMs are competitive with state-of-the-art models.
ABIForest improves anomaly detection using attention weights.
problem Anomaly detection in datasets.
method Attention mechanism integrated into Isolation Forest.
result ABIForest outperforms standard Isolation Forest on synthetic and real datasets.
New theory shows how multi-head attention reduces variance and decorrelates outputs.
problem Understanding and optimizing multi-head attention in neural networks.
method Developed a statistical theory linking multi-head attention to ensemble Nadaraya-Watson estimators.
result MHA variance reduction depends on head decorrelation, not just head count.
We show that the fundamental group of the double branched cover of an infinite family of homologically thin, non-quasi-alternating knots is not left-orderable, giving further support for a conjecture of Boyer, Gordon, and Watson that an irreducible rational homology 3-sphere is an L-space if and only if its fundamental…
Transformers can approximate Kalman Filtering in linear systems with small error.
problem Approximating Kalman Filtering using Transformers for linear dynamical systems.
method Two-step reduction: 1) Softmax self-attention block approximates Nadaraya-Watson kernel smoothing, 2) This estimator approximates Kalman Filter.
result Constructs a Transformer that implements the Kalman Filter with small additive error, uniformly bounded in time.
We present the recent advances along with an error analysis of the IBM speaker recognition system for conversational speech. Some of the key advancements that contribute to our system include: a nearest-neighbor discriminant analysis (NDA) approach (as opposed to LDA) for intersession variability compensation in the i-…