Paper learns data-driven organ matching rules from observational data.
problem Tackles organ transplantation compatibility using observational data.
method Representation learning to cluster donors and apply recipient transformations.
result Model outperforms human experts in predicting transplant outcomes.
Paper develops a model to predict kidney transplant success.
problem Mismatched deceased donor-recipient kidney leads to post-transplant death.
method Data analysis of 584 imported kidneys from 12 transplant centers.
result Predicting model reduces mortality rate due to kidney mismatch.
Improved graph generation model for small organic molecules.
problem Graph generation models struggle with matching training distributions and require expensive graph matching.
method Introduced a message passing neural network into the GVAE's encoder and decoder.
result Demonstrated improved graph generation for small organic molecules.
Reinforcement learning improves online matching by combining expert policies.
problem Efficient decision-making in complex systems like cloud services and marketplaces.
method Combines reinforcement learning with expert policies, using advantage-based weight updates.
result The orchestrated policy converges faster and yields higher efficiency than individual experts and conventional RL.
BLOB combines organic and bandit signals for better user interest estimation.
problem Combining organic and bandit signals for improved user interest estimation.
method Bayesian Latent Organic Bandit (BLOB) model using variational auto-encoders and local re-parametrization.
result BLOB outperforms organic and bandit-based methods in both organic and bandit-rich environments.
The supplement proves the existence and properties of a dynamical system related to asset price bubbles.
problem Modeling asset price bubbles using liquidity and random matching.
method Proves the existence and properties of a dynamical system D.
result Existence and properties of the dynamical system D are proven.
This paper develops a learning framework for optimal strategies in multi-stage decentralized matching markets.
problem Optimal strategies in multi-stage decentralized matching markets with uncertain preferences.
method Nonparametric statistical approach and variational analysis.
result Participants can be better off with multi-stage matching compared to single-stage matching.
Study dynamic matching in heterogeneous networks using ODE model.
problem Dynamic matching in heterogeneous networks with compatibility restrictions.
method Introduced an ODE model to evaluate matching algorithms.
result Demonstrated trade-off between quick matching and optimal matching.
Paper proposes a new method for population-wise matching of sulcal graphs.
problem Challenges in matching cortical fold variations across individuals.
method Population-wise multi-graph matching of sulcal graphs.
result Effectiveness of multi-graph matching in obtaining consistent labeling of sulcal basins.
Translating potential disease biomarkers between multi-species 'omics' experiments is a new direction in biomedical research. The existing methods are limited to simple experimental setups such as basic healthy-diseased comparisons. Most of these methods also require an a priori matching of the variables (e.g., genes o…
Machine learning improves invoice matching speed and accuracy.
problem Efficiently matching similar invoices from various businesses and organizations.
method Combining machine learning with string similarity functions on invoice datasets.
result Neural network and boosted decision tree outperformed existing solutions.
SOCP uses SOM to find groups and local calibration buffers for better regional coverage.
problem Heterogeneous regional coverage gaps in conformal prediction.
method Self-Organizing Map (SOM) for group discovery; local calibration buffers at BMU or fixed grid.
result Reduces regional coverage gaps on 7/8 benchmarks by 7.1%.
Cellular Electron CryoTomography (CECT) is a 3D imaging technique that captures information about the structure and spatial organization of macromolecular complexes within single cells, in near-native state and at sub-molecular resolution. Although template matching is often used to locate macromolecules in a CECT imag…
We study the statistical meaning of the minimization of distortion measure and the relation between the equilibrium points of the SOM algorithm and the minima of distortion measure. If we assume that the observations and the map lie in an compact Euclidean space, we prove the strong consistency of the map which almost …
This paper conditions non-linear infinite-dimensional diffusion processes.
problem Conditioning non-linear and infinite-dimensional diffusion processes.
method Infinite-dimensional Girsanov's theorem to condition function-valued stochastic processes.
result Conditioning of non-linear infinite-dimensional diffusion processes is achieved.
Framework allows organizations to collaborate on learning tasks securely.
problem Limited collaboration due to security constraints.
method Assisted Learning framework for supervised learning tasks.
result Near-oracle learning performance achieved without revealing sensitive information.
A new method controls synthesizers with normalizing flows for easier exploration and creation.
problem Complexity and parameters in modern synthesizers make them hard to master.
method Formalizing synthesizer control as latent audio space mapping with VAE and NF, disentangling flows.
result Superior performance in parameter inference and audio reconstruction, disentangling major audio variations.
Graphs predict reaction conditions for organic chemistry.
problem Predicting specific reaction conditions in organic chemistry.
method Graph Neural Networks (GNNs) for modeling reaction graphs.
result GNNs can identify specific graph features affecting reaction conditions.
DiffeoCFM efficiently generates realistic brain connectivity matrices using pullback metrics.
problem Generating realistic brain connectivity matrices for population heterogeneity analysis.
method Conditional flow matching on matrix manifolds via pullback metrics induced by global diffeomorphisms.
result DiffeoCFM achieves state-of-the-art performance on large-scale fMRI and EEG datasets.
A simple model economy with locally interacting producers and consumers is introduced. When driven by extremal dynamics, the model self-organizes {\em not} to an attractor state, but to an asymptote, on which the economy has a constant rate of deflation, is critical, and exhibits avalanches of activity with power-law d…
A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high-dimensional input space of the training samples into a low-dimensional space with the topology relations preserved. This makes SOMs supportive of organizing and visualizing complex data sets and have been pervasively…
Researchers organize and analyze a large public safety imagery dataset.
problem Efficiently organizing and analyzing a vast public safety imagery dataset.
method Hierarchical organization approach using Lincoln Laboratory Supercomputing Cluster for compute and storage.
result Successfully organized and evaluated the dataset with large-scale imagery inference across terabytes of data.
Reaction prediction remains one of the major challenges for organic chemistry, and is a pre-requisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural netwo…
The paper identifies collaborations in codebases using commit activity and language usage.
problem Identifying organic team interactions and collaborations in large codebases.
method Embedding and clustering commit activity, language usage, and code identifier topics.
result Restores engineering organization and reveals hidden collaborations.
RCNs match and exceed MLPs and SCNs in reinforcement learning tasks.
problem Efficiently learning rhythmic motion in reinforcement learning.
method Combining RNNs and SCN structures to create RCNs.
result RCNs outperform MLPs and SCNs across all environment tasks.
The kind of realized mission inflows the sensitivity to risk. Among other factors, the risk results from decision about liquid assets investment level and liquid assets financing. The higher the risk exposure, the higher the level of liquid assets. If the specific risk exposure is smaller, the more aggressive could be …
Pruning FCNs reveals sub-networks that match CNNs' performance.
problem Understanding the inductive bias of pruning in neural networks.
method Iterative magnitude pruning of a simple FCN followed by analysis of the resulting architecture.
result Pruned FCNs exhibit key features of CNNs, suggesting new architectural biases.
The paper explores how organisms and machines learn from their environment using machine learning, information theory, and thermodynamics.
problem How organisms and machines adapt to their environment.
method Machine learning, information theory, and thermodynamics approaches to understanding and modeling adaptation.
result Unified view of adaptation in organisms and machines based on principles of machine learning, information theory, and thermodynamics.
New modeling approach for self-organizing complex systems.
problem Identifying general principles of self-organizing complex systems.
method Self-deploying system structure and activities for goal-driven agents.
result Self-organization emerges from rational activity algorithm based on goals dependency network.
In this paper, we build an organization of high-dimensional datasets that cannot be cleanly embedded into a low-dimensional representation due to missing entries and a subset of the features being irrelevant to modeling functions of interest. Our algorithm begins by defining coarse neighborhoods of the points and defin…
Study characterizes bladder motion using dynamic MRI and statistical analysis.
problem Limited volume coverage in dynamic MRI sequences hinders 3D shape reconstruction.
method 3D dense velocity measurements, LDDMM framework, statistical characterization, mean curvature changes, surface deformation analysis.
result Stable shape descriptor for characterizing bladder surface dynamics.
We present a detailed analysis of the self-organization phenomenon in which the stylized facts originate from finite size effects with respect to the number of agents considered and disappear in the limit of an infinite population. By introducing the possibility that agents can enter or leave the market depending on th…
In self-organizing networks, topology and dynamics coevolve in a continuous feedback, without exogenous driving. The World Trade Network (WTN) is one of the few empirically well documented examples of self-organizing networks: its topology strongly depends on the GDP of world countries, which in turn depends on the str…
Study tackles workplace injury prediction and prevention.
problem Rare and imbalanced data makes injury risk prediction challenging.
method Ensemble resampling, transfer learning, and variable analysis.
result Improved injury risk prediction and prevention techniques.
Product Kanerva Machines dynamically combine smaller models for better memory organization.
problem Limited organization in the Kanerva Machine.
method Introducing Product Kanerva Machines that dynamically combine multiple smaller Kanerva Machines.
result Product Kanerva Machines can discover spatial tunings that approximately factorize simple images by object.
Mathematical study connects curve counting to quantum topology.
problem Counting holomorphic curves with Lagrangian boundary in Calabi-Yau 3-folds.
method Organized 1-parameter families of holomorphic curves and matched them to HOMFLYPT skein relations.
result Holomorphic curve counts match HOMFLYPT polynomial coefficients of links in the 3-sphere.
New method detects organized eCommerce fraud by clustering orders.
problem Detecting coordinated fraud by groups of fraudsters.
method Scalable categorical clustering using agglomerative clustering and sampling.
result Groups 35-45% of fraudulent orders together, detects 26.2% of fraud with low false alarms.
Unified framework matches equity and bond yields.
problem Inconsistency in pricing zero-coupon bonds and equity markets.
method Unified term structure of interest rates framework using put-call parity.
result Option-implied yield curves closely match treasury par yield curves.
This study investigates self-organizing dynamics in a stochastic exponential DAM model using Temporal Complexity.
problem Understanding self-organizing behavior in artificial neural systems.
method Investigation of a stochastic exponential DAM model through Temporal Complexity analysis.
result The model exhibits regimes of complex intermittency with nontrivial temporal correlations and scale-free behavior.
Unified view of SOMs and SNE from a common framework.
problem Comparing and understanding SOMs and SNE.
method Unified mathematical framework, quantitative comparison on datasets.
result SOMs and SNE can be derived from a common framework.
Often, large, high dimensional datasets collected across multiple modalities can be organized as a higher order tensor. Low-rank tensor decomposition then arises as a powerful and widely used tool to discover simple low dimensional structures underlying such data. However, we currently lack a theoretical understanding …
Automated discovery of diverse self-organized patterns in complex systems.
problem Automated identification of interesting spatially localized patterns in self-organizing systems.
method Intrinsically motivated machine learning algorithms (POP-IMGEPs) combined with deep auto-encoders and CPPN primitives.
result Efficiency and effectiveness of the proposed method in discovering diverse patterns compared to baselines.
New trading model uses self-organization for real-world complexity.
problem Traditional trading models are too analytical for real-world conditions.
method Embraces self-organization, complex systems theory, and event-based time.
result Example Delta Engine algorithm demonstrates effectiveness.
The self-organizing methods were used for the investigation of financial market. As an example we consider data time-series of Dow Jones index for the years 2002-2003 (R. Mantegna, cond-mat/9802256). In order to reveal new structures in stock market behavior of the companies drawing up Dow Jones index we apply SOM (Sel…
In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolut…
Deep learning solves imaging inverse problems without ground truth.
problem Solving imaging problems without perfect data.
method Taxonomy of deep learning approaches for imaging inverse problems.
result Trade-offs and failure modes identified for different methods.
A methodology is developed to identify, as units of study, each decrease in the value of a stock from a given maximum price level. A critical level in the amount of price declines is found to separate a segment operating under a random walk from a segment operating under a power law. This level is interpreted as a poin…
SORSCNs improve nonstationary data modeling by self-organizing and adjusting network parameters.
problem Nonstationary data challenges traditional models in continuous learning.
method SORSCNs autonomously adjust network parameters and structure in real-time using adaptive algorithms.
result SORSCNs outperform other models in generalizing to nonstationary data.