Predict player lifetime value based on engagement metrics.
problem User profiling in video games.
method Survival curves, deep learning, long short-term memory.
result Deep learning predicts player lifetime value.
On-device research index
arXiv research
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.
169,291 papers · 148 categories
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48 results for “Memory Profiling”
Generative profiling improves real-time task timing for varied resource contexts.
problem Inaccurate task timing analysis for complex hardware architectures.
method Nonparametric, conditional multi-marginal Schrödinger Bridge (MSB) formulation for synthesizing context-dependent timing profiles.
result Maximum likelihood accurate execution profiles for unseen resource contexts.
Random layer-wise pruning profiles are as effective as metric-based ones for various datasets.
problem Reduction of model size and computational resources in neural networks.
method Conducted baseline experiments, developed RL-based search algorithm for finding transferable layer-wise pruning profiles.
result RL-based layer-wise pruning profiles are as good or better than best profiles found on the original dataset via exhaustive search.
We give a stochastic microscopic modelling of stock markets driven by continuous double auction. If we take into account the mimetic behavior of traders, when they place limit order, our virtual markets shows the power-law tail of the distribution of returns with the exponent outside the Levy stable region, the short m…
Checkmate optimizes DNN training time and memory usage.
problem Optimizing DNN training time and memory usage.
method Formalizes tensor rematerialization as an optimization problem, uses MILP solvers or approximation algorithms to find optimal schedules, and accelerates training with hardware-aware cost models.
result Reduces training cost and enables training with larger input sizes.
The study reduces neural network training memory by up to 60.7x on CIFAR-10.
problem Reducing memory requirements for neural network training.
method Profiled memory usage, evaluated four techniques: sparsity, low precision, microbatching, gradient checkpointing.
result Appropriate combinations of techniques can reduce memory usage up to 60.7x on CIFAR-10.
NEST optimizes deep learning training by placing devices efficiently across networks and memory.
problem Inefficient device placement in distributed deep learning leads to high communication and memory overhead.
method NEST uses network-, compute-, and memory-aware dynamic programming to optimize device placement.
result NEST achieves up to 2.43 times higher throughput and better memory efficiency.
AlphaGrad optimizes memory usage in RL algorithms by normalizing gradients.
problem Memory overhead and hyperparameter complexity in adaptive optimizers.
method Tensor-wise L2 normalization followed by a smooth hyperbolic tangent transformation controlled by a single parameter.
result AlphaGrad provides enhanced training stability and competitive performance in various RL algorithms.
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Datacs.LG
This work analyzes industrial IoT data for security using machine learning.
problem Security vulnerabilities in industrial IoT networks.
method Transformed industrial network data into time series and analyzed with three algorithms.
result Matrix Profiles outperform other methods with minimal parameterization.
Echo optimizes LSTM RNN training on GPUs by reducing memory footprint.
problem Memory bottleneck in LSTM RNN training on GPUs.
method Compiler-based feature map recomputation to estimate and balance memory and execution time.
result Average 1.89X reduction in GPU memory footprint.
Proposes a new benchmark for deep neural network training.
problem Limited focus on DNN training efficiency.
method Develops TBD benchmark covering various applications and frameworks.
result Highlights inefficiencies in DNN training across different models and hardware.
FinMem enhances LLM trading agents with layered memory and character design.
problem Developing purpose-driven LLM agents for financial decision-making.
method Integrates layered memory and character design modules into an LLM framework.
result Significantly enhanced trading performance in financial markets.
Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrievalstat.ML
Linear memory stores associations up to a logarithmic scale, but listwise retrieval can handle a quadratic scale.
problem How many key-value associations can a linear memory store?
method Analyzed linear memory models for top-1 and listwise retrieval, proving phase transitions and developing asymptotic theories.
result Linear memory has a logarithmic capacity for top-1 retrieval and a quadratic capacity for listwise retrieval.
Classifies Tinder dating profiles using facial embeddings.
problem Automatically review and classify online dating profiles.
method Uses FaceNet facial embeddings to extract features and train logistic regression models.
result Simple logistic regression on 20 profiles achieved 65% validation accuracy.
Paper warns against assuming all unreported compound-target binding profiles are negative.
problem Assumption of all unreported compound-target binding profiles being negative is unreliable.
method Proposes a framework to jointly recover unreported profiles and learn predictive models.
result Explicit recovery of unreported profiles improves prediction performance.
A Matrix--free Likelihood Method for Exploratory Factor Analysis of High-dimensional Gaussian Datastat.ME
A new method speeds up factor analysis for high-dimensional data.
problem Estimating covariance parameters in high-dimensional Gaussian data with limited observations.
method Matrix-free likelihood method using implicitly restarted Lanczos and limited-memory quasi-Newton algorithms.
result Our method is faster than EM without sacrificing accuracy.
The paper introduces Poincaré profiles for metric spaces and groups, linking them to conformal dimension.
problem Understanding the properties of metric measure spaces and groups with polynomial growth.
method Introducing and analyzing Poincaré profiles for groups and hyperbolic spaces.
result Connection between Poincaré profiles and conformal dimension, leading to non-existence of coarse embeddings.
Profile entropy measures learnability and compressibility of discrete distributions.
problem Understanding the learnability and compressibility of discrete distributions.
method Investigates profile entropy, showing its role in estimation, inference, and compression.
result Profile entropy is a fundamental measure unifying estimation, inference, and compression.
A method to describe Riemann surfaces using graph profiles is proposed.
problem Describing Riemann surfaces in a graph-theoretic framework.
method Graph profiles to represent Riemann surfaces, establishing necessary and sufficient conditions for their existence.
result A criterion for the existence of Riemann surfaces with a given signature.
Method controls extrapolation in prediction profiles for statistical and machine learning models.
problem Avoiding invalid predictions due to extrapolation in prediction profiles.
method Genetic algorithm optimization over constrained factor regions.
result Optimal factor settings without constraint are often invalid and extrapolated.
Study identifies subphenotypes of pediatric sepsis to improve ML predictive performance.
problem Enhance machine learning predictive performance in pediatric sepsis.
method Latent profile analysis of clinical data to identify subphenotypes, followed by ML experiments.
result Improved predictive performance of ML models targeting specific subphenotypes of pediatric sepsis.
XSP profiles ML models across hardware and software stacks.
problem Challenges in profiling ML model performance across different layers of the stack.
method XSP uses distributed tracing to aggregate data from various sources and introduces a leveled, iterative measurement approach.
result XSP provides insights into ML model execution not easily discernible otherwise.
We equip many non compact non simply connected surfaces with smooth Riemannian metrics whose isoperimetric profile is smooth, a highly non generic property. The computation of the profile is based on a calibration argument, a rearrangement argument, the Bol-Fiala curvature dependent inequality, together with new result…
Logarithmic separation profile in hyperbolic groups shows hierarchical structure.
problem Understanding hierarchical structure in hyperbolic groups with logarithmic separation.
method Proving groups with logarithmic separation split over cyclic groups and providing counterexamples.
result Not all groups with hierarchical structure have logarithmic separation profile.
Framework detects shape shifts in functional profiles using Fréchet mean and shape invariant model.
problem Detecting shape shifts in functional profiles.
method Combining Fréchet mean and shape invariant model for interpretable parameterization of profile deviations.
result Potential shifts in shape deformation process distinguished by significant shifts in amplitude and/or phase.
Study compares isoperimetric profiles on manifolds with integral Ricci curvature bounds.
problem Comparing isoperimetric profiles on manifolds with integral Ricci curvature bounds.
method Extending previous work, the study uses integral bounds on Ricci curvature to prove comparison results for isoperimetric profile functions.
result Comparison results for the Isoperimetric profile function in manifolds with integral bounds on Ricci curvature.
A math surface has varying perimeter rules.
problem Finding a surface with a non-continuous isoperimetric profile.
method Constructed a 2D Riemannian manifold.
result Discovered a surface with a discontinuous isoperimetric profile.
AutoML enhances clinical metabolic profiling by adjusting for confounders.
problem Identifying and adjusting for clinical confounders in AutoML for metabolic profiling.
method Tandem rank-accuracy measure for feature selection, residual training adjustment for confounders.
result Increased homocysteine concentration associated with long-term metformin exposure.
Estimates lower bounds for isoperimetric profiles and improves on previous estimates for specific manifolds.
problem Estimating lower bounds for isoperimetric profiles of specific Riemannian manifolds.
method Explicit lower bounds for isoperimetric profiles of Riemannian product manifolds.
result Improved lower bounds for isoperimetric profiles and Yamabe constants.
In the context of sub-Riemannian Heisenberg groups Hn, n \geq 1, we shall study Isoperimetric Profiles, which are closed compact hypersurfaces having constant horizontal mean curvature, very similar to ellipsoids. Our main goal is to study the stability of Isoperimetric Profiles.
Paper describes profiles of multivariate normal distributions and novel estimators for mutual information.
problem Estimating mutual information for complex distributions.
method Analytical description of profiles, introduction of Bend and Mix Models, Monte Carlo estimation.
result Bend and Mix Models accurately estimate mutual information profiles and provide Bayesian estimates.
Non-Negative Matrix Factorization, NMF, attempts to find a number of archetypal response profiles, or parts, such that any sample profile in the dataset can be approximated by a close profile among these archetypes or a linear combination of these profiles. The non-negativity constraint is imposed while estimating arch…
Study lampshuffler groups' isoperimetric profiles, refining previous estimates.
problem Relate isoperimetric profiles of lampshuffler groups to their base groups.
method Use lamplighter subgraphs and halo products to find optimal upper bounds.
result Sharp estimates for exponential growth groups, including Brieussel-Zheng groups.
New algorithms for efficient matrix profile computation using various Euclidean distances.
problem Efficiently computing matrix profile for all-pairs-similarity search on time series.
method Proposed AAMP, ACAMP, and extended algorithms for p-norm distance.
result AAMP and ACAMP algorithms outperform existing methods for specific Euclidean distances.
The study analyzes how large language models form and express investor risk profiles.
problem Understanding how large language models (LLMs) form and express investor risk profiles.
method Examined three LLMs (GPT, Gemini, and Llama) and assessed their responses to a standardized risk questionnaire under varying prompts.
result LLMs generally form long-term investment profiles, but they exhibit different risk tolerance levels.
A new tool, matrix profile, finds all pair similarities in time series data.
problem Finding all pair similarities in time series data.
method Near universal time series data mining tool called matrix profile.
result Matrix profile solves the all-pairs-similarity-search problem for time series subsequences.
We propose a dynamical theory of market liquidity that predicts that the average supply/demand profile is V-shaped and {\it vanishes} around the current price. This result is generic, and only relies on mild assumptions about the order flow and on the fact that prices are (to a first approximation) diffusive. This natu…
A new framework for adaptive behavior using reusable value profiles.
problem Adaptive behavior in changing environments requires switching among value-control regimes, but maintaining separate parameters for each situation is impractical.
method Introduces value profiles: reusable bundles of parameters assigned to hidden states, allowing for state-conditional strategy recruitment without independent parameters for each context.
result Profile-based models outperform simpler alternatives in probabilistic reversal learning, suggesting belief-dependent control of adaptive behavior.
Uniform Lipschitz continuity of isoperimetric profiles in evolving surfaces.
problem Uniform Lipschitz continuity of isoperimetric profiles in evolving surfaces.
method Normalized Ricci flow on compact surfaces.
result Uniform Lipschitz continuity of isoperimetric profiles under normalized Ricci flow.
It is shown that, in dimensions , isoperimetric profiles of compact real analytic Riemannian manifolds are semi-analytic.
The hypercube's perimeter is significantly larger than expected near half volume.
problem Understanding the isoperimetric profile of the hypercube.
method Analytical proof of perimeter bounds and comparison to Gaussian isoperimetric profile.
result The isoperimetric profile of the hypercube does not converge to the Gaussian profile as dimension increases.
apk2vec builds profiles of Android apps using multiple views, outperforming existing methods.
problem Building comprehensive behavior profiles of Android apps for better analytics.
method Semi-supervised multi-view representation learning combining RL and feature hashing.
result apk2vec's profiles significantly outperform state-of-the-art techniques in app analytics tasks.
The first known example of a complete Riemannian manifold whose isoperimetric profile is discontinuous is given.
Develops a method to disaggregate aerosol optical depth into vertical extinction profiles.
problem Uncertainty in measuring aerosol vertical distributions due to limited observations.
method Bayesian nonparametric Gaussian process modeling using meteorological predictors.
result Model reconstructs realistic extinction profiles with well-calibrated uncertainty, outperforming idealized baselines.
We study the shape of inflated surfaces introduced in \cite{B1} and \cite{P1}. More precisely, we analyze profiles of surfaces obtained by inflating a convex polyhedron, or more generally an almost everywhere flat surface, with a symmetry plane. We show that such profiles are in a one-parameter family of curves which w…
Order submission and cancellation are two constituent actions of stock trading behaviors in order-driven markets. Order submission dynamics has been extensively studied for different markets, while order cancellation dynamics is less understood. There are two positions associated with a cancellation, that is, the price…
A comparison theorem for the isoperimetric profile on the universal cover of surfaces evolving by normalised Ricci flow is proven. For any initial metric, a model comparison is constructed that initially lies below the profile of the initial metric and which converges to the profile of the constant curvature metric. Th…
Heat-Kernel Entropy Profiles and Geometric Effective Sample Size for Weighted Measures on Manifoldsstat.ML
New summary measures reveal geometric structure in weighted measures on manifolds.
problem Lack of geometric information in standard weight-only summaries.
method Heat-kernel entropy profiles, tracking nonuniformity across scales.
result Geometric effective sample size discounts nearby or duplicate particles.