New neural network approach using mutual information.
problem Training neural networks for imbalanced datasets.
method Converts neural network classifiers to mutual information evaluators.
result New form of softmax leads to better classification accuracy, especially for imbalanced datasets.
ADGAN improves risk tolerance prediction by aligning cross-domain data.
problem Lack of professional knowledge and domain-specific models in risk tolerance studies.
method Asymmetric cross-Domain Generative Adversarial Network (ADGAN) for domain scale inequality.
result ADGAN better handles class imbalance and unqualified data than state-of-the-art methods.
Graph cross network improves graph classification accuracy.
problem Improving graph classification accuracy.
method Graph cross network (GXN) with vertex infomax pooling (VIPool) and feature-crossing layer.
result Improves graph classification accuracy by 2.12% and 1.15%.
This paper investigates the effect of cross-shareholding on stock price synchronicity, as a measure of price informativeness, of the listed firms in the Chinese stock market. We gauge firms' levels of cross-shareholdings in terms of centrality in the cross-shareholding network. It is confirmed that it is through a nois…
Even knots with more than 30 crossings are not fertile.
problem Understanding fertility in knots with specific crossing numbers.
method Analyzing minimum crossing number diagrams and changing over-under information.
result Even knots with more than 30 crossings cannot be obtained from a minimum crossing number diagram by changing over-under information.
Characterizes sequences from two-component link diagrams.
problem Understanding information from non-self crossing sequences of link diagrams.
method Investigated and characterized pairs of non-self OU sequences of two-component link diagrams.
result Completely characterized pairs of non-self OU sequences of diagrams of two-component links.
Unified framework linking firm signals and cross-asset spillovers for SDF estimation.
problem Estimating SDF with cross-asset spillovers and firm-level predictive signals.
method Maximizing Sharpe ratio to jointly estimate signals and spillovers, yielding interpretable SDF.
result SDF consistently outperforms benchmarks across various investment universes and market states.
Cross-entropy loss linked to metric learning, outperforming complex pairwise losses.
problem Improving metric learning performance without complex optimization schemes.
method Theoretical analysis linking cross-entropy to pairwise losses, showing cross-entropy as an upper bound and equivalent to mutual information maximization.
result Minimizing cross-entropy is equivalent to maximizing mutual information, leading to state-of-the-art performance.
Optimal crypto order execution using cross-exchange signals.
problem Maximizing order execution efficiency in cryptocurrency markets.
method Reinforcement learning applied to cross-exchange data.
result Cross-exchange signals improve optimal execution outcomes.
DNN-based cross-modal retrieval has become a research hotspot, by which users can search results across various modalities like image and text. However, existing methods mainly focus on the pairwise correlation and reconstruction error of labeled data. They ignore the semantically similar and dissimilar constraints bet…
Mutual information is widely applied to learn latent representations of observations, whilst its implication in classification neural networks remain to be better explained. We show that optimising the parameters of classification neural networks with softmax cross-entropy is equivalent to maximising the mutual informa…
Cross-domain recommendation can alleviate the data sparsity problem in recommender systems. To transfer the knowledge from one domain to another, one can either utilize the neighborhood information or learn a direct mapping function. However, all existing methods ignore the high-order connectivity information in cross-…
Paper uses bipartite graph to forecast cross-market returns, revealing asymmetry.
problem Cross-market return predictability and asymmetry between U.S. and Chinese markets.
method Directed bipartite graph capturing time-ordered linkages, hypothesis testing for edge selection, regularized and ensemble machine learning models.
result U.S. returns predict Chinese intraday returns, but not vice versa, revealing asymmetry.
Every link in the 3-sphere has a projection to the plane where the only singularities are pairwise transverse triple points. The associated diagram, with height information at each triple point, is a triple-crossing diagram of the link. We give a set of diagrammatic moves on triple-crossing diagrams analogous to the Re…
CrossAD detects anomalies in time series data by considering cross-scale associations and cross-window modeling.
problem Anomaly detection in time series data is challenging due to varying patterns at different scales and fixed window sizes.
method CrossAD incorporates cross-scale reconstruction and a query library to capture dynamic cross-scale associations and comprehensive context.
result CrossAD achieves state-of-the-art performance in anomaly detection across multiple real-world datasets.
Paper proposes a new unsupervised method for cross-modality data translation without requiring direct mappings.
problem Tackles the challenge of zero-shot cross-modality data translation with fidelity.
method Mutual Information guided Diffusion cross-modality data translation Model (MIDiffusion) using score-matching and stochastic diffusion.
result Empirically shows advanced performance compared to other generative models.
Enhances investment performance by leveraging cross-market information.
problem Maximizing portfolio performance in asset markets with shared characteristics.
method Transfer learning applied to portfolio optimization.
result Achieves maximum Sharpe ratio asymptotically.
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.
Researchers find a class/cross-class structure in deep learning spectra.
problem Understanding the structure of deep learning classifiers.
method Empirical spectral analysis of deepnet classifiers.
result The cross-class structure explains various features in deepnet spectra.
Cross-balancing improves causal inference by balancing features with outcome data.
problem Balancing features for valid causal inference when outcome data is available.
method Cross-balancing using sample splitting to separate feature construction and weight estimation errors.
result Cross-balancing produces consistent, asymptotically normal, and efficient estimators under mild conditions.
New framework improves multivariate time series forecasting by minimizing redundant information.
problem Improving multivariate time series forecasting with deep learning techniques.
method Cross-variable Decorrelation Aware feature Modeling (CDAM) and Temporal correlation Aware Modeling (TAM) to refine Channel-mixing and exploit temporal correlations.
result Significantly surpasses existing models in comprehensive tests.
A new method improves recommendation accuracy by learning from multiple networks and time-dependent user preferences.
problem Incomplete user profiles and dynamic user preferences degrade recommender quality.
method A cross-network time-aware recommender that learns from multiple source networks and develops current user models.
result The proposed solution achieves superior performance in accuracy, novelty, and diversity.
Dual-structured method improves cross-domain imitation learning.
problem Learning policies in a target domain from source domain expert demonstrations.
method Extracts domain and policy expertness features, synthesizes new features.
result Superior performance compared to other algorithms in MuJoCo tasks.
A time-varying network reveals community structure in cryptocurrencies.
problem Investing in cryptocurrencies from different communities can diversify risk.
method Dynamic covariate-assisted spectral clustering method.
result Investors can earn 1.08% daily return by diversifying across communities.
Cross-dimensional neural networks improve AI in Catan game.
problem Challenging to build AI agents for Catan game using RL.
method Introduced cross-dimensional neural networks to handle game complexities.
result RL agent outperforms best heuristic agent in Catan.
BERT model improves cross-lingual document retrieval.
problem Improving cross-lingual information retrieval between English queries and foreign documents.
method Introduced a deep relevance matching model based on BERT, trained with weak supervision using CLIR data.
result Model outperforms competitive baseline approaches in retrieving Lithuanian documents against short English queries.
Algorithm constructs and classifies weaving diagrams using combinatorial methods.
problem Classifying unique weaving diagrams with over/under information.
method Systematic algorithm based on tiling and crossing matrices.
result Classification of periodic structures based on minimum crossings.
Platform uses queries to elicit investor preferences for portfolio trades, improving allocation efficiency.
problem Hidden-information problem in institutional crossing markets where investors value trades as portfolios but liquidity discovery is organized by individual securities.
method Modeling portfolio crossing as preference elicitation, using price-directed demand queries and value queries to verify selected packages.
result Hybrid procedure using demand and value queries recovers 88-95% of full-information welfare with a limited query budget.
New insights into encoder-decoder structures using information measures.
problem Understanding the role of encoder-decoder design in machine learning.
method Using information sufficiency and mutual information loss concepts.
result Characterizes the expressiveness loss in encoder-decoder designs.
Paper proposes mutual information learning for deep learning classifiers.
problem Overfitting in deep learning models.
method Mutual information learning framework to train classifiers.
result MILCs achieve better generalization than conditional entropy classifiers.
Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are based on hand-crafted or raw level features of objects, which may not be optimally compatible with the coding process. Besides, these hashi…
Cryptocurrencies return cross-predictability and technological similarity yield information on risk propagation and market segmentation. To investigate these effects, we build a time-varying network for cryptocurrencies, based on the evolution of return cross-predictability and technological similarities. We develop a …
Probabilistic pseudo knots model uncertain knot diagrams.
problem Modeling knots with unresolved crossings.
method Assign probabilities to undetermined crossings; define probabilistic equivalence and extend classical knot invariants.
result Capture uncertainty in physical, biological, and computational contexts.
Study uses LLM to extract and compare segment disclosures from financial filings.
problem Challenges in completeness and comparability of segment disclosures in financial reports.
method Developed a large language model framework to extract and preserve segment information from Form 10-K filings.
result The LLM accurately extracts segment-level information and addresses cross-period knowledge questions.
PRISM-VQ combines financial priors with vector quantization for better stock prediction.
problem Predicting cross-sectional stock returns is hard due to low signal-to-noise ratios and changing market conditions.
method Integrates expert priors, vector-quantized latent factors, and dynamic factor loadings.
result Consistent improvements in cross-sectional return prediction and portfolio performance.
Quantum machine learning uses quantum cross entropy to minimize loss, but measurement loss affects this process.
problem Quantum machine learning's loss minimization through cross entropy is affected by measurement outcomes.
method Defined quantum cross entropy, proved its lower bounds, and investigated its relation to quantum fidelity and likelihood.
result Quantum cross entropy is lower-bounded by negative log-likelihood when derived from quantum data, but measurement outcomes can cause loss.
InfoOT improves data alignment by maximizing mutual information.
problem Optimal transport's limitations in handling clusters, outliers, and new data.
method InfoOT extends optimal transport by maximizing mutual information while minimizing distances.
result InfoOT outperforms optimal transport in domain adaptation, cross-domain retrieval, and single-cell alignment.
A pseudodiagram is a diagram of a knot with some crossing information missing. We review and expand the theory of pseudodiagrams introduced by R. Hanaki. We then extend this theory to the realm of virtual knots, a generalization of knots. In particular, we investigate how much crossing information must be known to conc…
This paper studies knots in a thickened surface and introduces a new way to label crossings.
problem Analyzing knots in a thickened surface with a new labeling system.
method Introducing diagrams, moves, and a new labeling system for knots in SgimesS1. result Developed a new method to label crossings in knots in SgimesS1. Paper introduces NICc for fast cluster-based validation of prediction models.
problem Validation of prediction models on clustered data.
method Derived NICc to approximate leave-one-cluster-out deviance for standard regression models.
result NICc provides more accurate model size and variable selection, especially with strong clustering.
ANTIDOTE reduces noisy labels influence during learning.
problem Learning with noisy labels.
method Information-divergence neighborhood relaxation and adversarial training.
result ANTIDOTE outperforms standard cross-entropy loss in noisy label settings.
Gathering the most information by picking the least amount of data is a common task in experimental design or when exploring an unknown environment in reinforcement learning and robotics. A widely used measure for quantifying the information contained in some distribution of interest is its entropy. Greedily minimizing…
We consider a natural model of random knotting- choose a knot diagram at random from the finite set of diagrams with n crossings. We tabulate diagrams with 10 and fewer crossings and classify the diagrams by knot type, allowing us to compute exact probabilities for knots in this model. As expected, most diagrams with 1…
Classical knots in R3 can be represented by diagrams in the plane. These diagrams are formed by curves with a finite number of transverse crossings, where each crossing is decorated to indicate which strand of the knot passes over at that point. A pseudodiagram is a knot diagram that may be missing crossing…
New algorithm improves asset ranking for better cross-sectional portfolios.
problem Sub-optimal ranking of assets in cross-sectional systematic strategies.
method Learning-to-rank algorithms to enhance portfolio construction.
result Modern machine learning ranking algorithms boost Sharpe Ratios by approximately threefold.
This paper improves cross-domain learning using random forests for manifold alignment.
problem Improving cross-domain learning and feature integration.
method Semi-supervised manifold alignment using random forest proximities.
result Random forest proximities enhance downstream classification accuracy.
This paper classifies a specific weave type by their crossing number.
problem Classifying doubly periodic untwisted (p,q)-weaves.
method Classification by crossing number, introducing crossing matrix for equivalence.
result Classification of untwisted (p,q)-weaves by their crossing number.
LSTM improves cross-network recommendations by capturing user preference changes and irregular time intervals.
problem Offline cross-network recommender solutions fail to capture user preference changes and dynamic environments.
method Proposes a multi-layered LSTM network with attention mechanisms, higher order interactions, and time-aware gates.
result The model consistently outperforms state-of-the-art in accuracy, diversity, and novelty.