Paper applies Newman-Penrose formalism to ACM manifolds.
problem Classifying compact ACM manifolds with η-Einstein metrics. method Newman-Penrose formalism applied to ACM manifolds.
result Classification of compact ACM manifolds with η-Einstein metrics. Study of ACM(3)S structures on manifolds with G2 structure.
problem Understanding ACM(3)S structures on manifolds with G2 structure.
method Review and computation of torsion of associated SU(3) structure, study of ACM3S space, and observation of links with submanifolds.
result Initiation of study on the space of ACM3Ss with interesting topological features and links to submanifolds.
After presenting Actor Critic Methods (ACM), we show ACM are control variate estimators. Using the projection theorem, we prove that the Q and Advantage Actor Critic (A2C) methods are optimal in the sense of the L2 norm for the control variate estimators spanned by functions conditioned by the current state and acti…
Enhanced EEG classification improves motor imagery detection with less computation.
problem Improving classification accuracy of motor imagery EEG signals.
method Integrates Block-Toeplitz structure into augmented covariance matrices and uses Siegel metric.
result Significantly reduces computational time without compromising classification accuracy.
Capsule Networks improve autonomous navigation in sparse environments.
problem Challenges in reinforcement learning for sparse reward environments.
method Caps-EM pairs CapsNets with Advantage Actor Critic, using fewer parameters.
result Caps-EM achieves significant time improvements and fewer parameters compared to competing methods.
Paper improves financial trading models using GPU parallelism.
problem Challenges in policy instability and sampling bottlenecks in reinforcement learning for financial tasks.
method Revisits ensemble methods with massively parallel simulations on GPUs.
result Significantly improved computational efficiency and robustness of financial decision-making strategies.
Co-designing efficient machine learning based systems across the whole hardware/software stack to trade off speed, accuracy, energy and costs is becoming extremely complex and time consuming. Researchers often struggle to evaluate and compare different published works across rapidly evolving software frameworks, hetero…
Word embeddings are a powerful approach for unsupervised analysis of language. Recently, Rudolph et al. (2016) developed exponential family embeddings, which cast word embeddings in a probabilistic framework. Here, we develop dynamic embeddings, building on exponential family embeddings to capture how the meanings of w…
Contextual policy search (CPS) is a class of multi-task reinforcement learning algorithms that is particularly useful for robotic applications. A recent state-of-the-art method is Contextual Covariance Matrix Adaptation Evolution Strategies (C-CMA-ES). It is based on the standard black-box optimization algorithm CMA-ES…
Algorithm learns CNF formulas from random solutions under specific conditions.
problem Learning a CNF formula from uniform random solutions.
method Revisits Valiant's algorithm and applies Lovász local lemma conditions.
result Significantly reduces sample complexity for learning CNFs.
FinRLlama wins FinRL Challenge 2024 by fine-tuning LLMs with market data.
problem Lack of contextual alignment for financial market applications in traditional LLMs.
method Fine-tuning LLaMA-3.2-3B-Instruct model with custom RLMF prompt design integrating historical data and reward feedback.
result RLMF-tuned FinRLlama framework outperforms baseline methods in signal consistency and trading outcomes.
TQFT invariants are either easy or hard to compute, depending on the TQFT type.
problem Computing TQFT invariants on closed 3-manifolds.
method Application of a dichotomy result for weighted constraint satisfaction problems over C.
result TQFT invariants are either solvable in polynomial time or #P-hard. Cyber-physical systems often consist of entities that interact with each other over time. Meanwhile, as part of the continued digitization of industrial processes, various sensor technologies are deployed that enable us to record time-varying attributes (a.k.a., time series) of such entities, thus producing correlated …
Graphs with fat minors have a limited large-scale structure.
problem Understanding the large-scale structure of graphs excluding certain minors.
method Introduced the concept of Baker-treewidth and used it to prove asymptotic dimension bounds.
result Every hereditary class of bounded-degree graphs excluding some graph as a fat minor has asymptotic dimension at most 2.
Twitter releases dataset to study user engagement on Home Timeline, focusing on privacy.
problem Limited exploration of user feedback on Twitter's Home Timeline.
method Release of a large-scale dataset of 160 million Tweets with engagement information.
result Demonstrates challenges in predicting user engagements and privacy concerns.
GUIDE detects anomalies in attributed networks by reconstructing node attributes and higher-order structures.
problem Lack of effective mechanisms for detecting anomalies in complex network interactions.
method GUIDE uses attribute and structure autoencoders, graph attention, and reconstruction errors to identify anomalies.
result GUIDE significantly outperforms state-of-the-art methods on multiple real-world datasets.
CHEETAH speeds up secure MLaaS by 100x over fastest existing schemes.
problem Privacy-preserving machine learning on end devices.
method Ultra-fast secure MLaaS framework using secret sharing.
result More than 100x speedup over fastest existing schemes.
New algorithms SVCA and SSPA improve robustness to noise in nonnegative matrix factorization.
problem Estimating vertices from noisy data points in convex hull.
method Smoothed VCA (SVCA) and Smoothed SPA (SSPA) algorithms.
result Improved robustness to noise compared to existing methods.
New deep architecture improves head motion prediction in 360° videos.
problem Predicting user head motion in 360-degree videos using past positions and video content.
method Re-examined existing deep-learning approaches, identified flaws, and designed a new TRACK architecture.
result TRACK achieves state-of-the-art performance, outperforming competitors by up to 20 percent.
In this paper, we focus on the stochastic block model (SBM),a probabilistic tool describing interactions between nodes of a network using latent clusters. The SBM assumes that the networkhas a stationary structure, in which connections of time varying intensity are not taken into account. In other words, interactions b…
Develops model-free methods for event history analysis and efficient covariate adjustment.
problem Estimating treatment effects while accounting for confounding and understanding event history.
method Model-free prediction techniques, Local Covariance Measure (LCM), Debiased Outcome-adapted Propensity Estimator (DOPE), Aalen Covariance Measure (ACM).
result Demonstrates the effectiveness and robustness of the proposed methods in various settings.