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.
DeepRC uses Hopfield networks and attention to classify immune repertoires.
AdvImmune improves certifiable robustness of GNNs against adversarial attacks.
Method infers TCR effects on disease using patient data.
Many immunization strategies have been proposed to prevent infectious viruses from spreading through a network. In this study, we propose efficient immunization strategies to prevent a default contagion that might occur in a financial network. An essential difference from the previous studies on immunization strategy i…
Study on the probability of immunity and its bounds.
A modern Hopfield network improves deep learning with better memory and attention.
Paper uses PCA to analyze Chinese sovereign bonds and discusses bond immunization.
We information-theoretically reformulate two measures of capacity from statistical learning theory: empirical VC-entropy and empirical Rademacher complexity. We show these capacity measures count the number of hypotheses about a dataset that a learning algorithm falsifies when it finds the classifier in its repertoire …
New foundation for Shapley value immune to coalitional manipulations.
In their seminal work Carr and Lee (2008) show how to robustly price and replicate a variety of claims written on the quadratic variation of a risky asset under the assumption that the asset's volatility process is independent of the Brownian motion that drives the asset's price. Additionally, they propose a correlatio…
Estimates vaccine effectiveness and immune correlates in TND studies with missing data.
As adversarial attacks pose a serious threat to the security of AI system in practice, such attacks have been extensively studied in the context of computer vision applications. However, few attentions have been paid to the adversarial research on automatic path finding. In this paper, we show dominant adversarial exam…
Paper uses bond pricing and convexity adjustments to explain herd immunity paradox.
MINN-SA enhances cancer detection using TCR sequences with better interpretability.
Italy's vaccine coverage fell, leading to political debates and online social media discussions.
Feature selection predicts immune state changes in RA mouse model.
Bayesian optimization of antibodies learns from immune system evolution.
Hidden Markov Models (HMMs) are a ubiquitous tool to model time series data, and have been widely used in two main tasks of Automatic Music Transcription (AMT): note segmentation, i.e. identifying the played notes after a multi-pitch estimation, and sequential post-processing, i.e. correcting note segmentation using tr…
In this paper we outline initial concepts for an immune inspired algorithm to evaluate price time series data. The proposed solution evolves a short term pool of trackers dynamically through a process of proliferation and mutation, with each member attempting to map to trends in price movements. Successful trackers fee…
ImmuNeCS uses AI immune system to build neural committees for efficient deep learning model creation.
Learning algorithms are enabling robots to solve increasingly challenging real-world tasks. These approaches often rely on demonstrations and reproduce the behavior shown. Unexpected changes in the environment may require using different behaviors to achieve the same effect, for instance to reach and grasp an object in…
Autonomous agents that must exhibit flexible and broad capabilities will need to be equipped with large repertoires of skills. Defining each skill with a manually-designed reward function limits this repertoire and imposes a manual engineering burden. Self-supervised agents that set their own goals can automate this pr…
A new PGA algorithm ensures stable, robust, and noise-immune solutions for non-negative inverse problems.
Representing patterns as labeled graphs is becoming increasingly common in the broad field of computational intelligence. Accordingly, a wide repertoire of pattern recognition tools, such as classifiers and knowledge discovery procedures, are nowadays available and tested for various datasets of labeled graphs. However…
A time-dependent SIR model predicts COVID-19 spread and herd immunity.
A new neural network model MDRBM improves noise-robustness in classification.
Major histocompatibility complex class two (MHC-II) molecules are trans-membrane proteins and key components of the cellular immune system. Upon recognition of foreign peptides expressed on the MHC-II binding groove, helper T cells mount an immune response against invading pathogens. Therefore, mechanistic identificati…
UM-GNN improves GNN robustness against poisoning attacks.
"How much is my data worth?" is an increasingly common question posed by organizations and individuals alike. An answer to this question could allow, for instance, fairly distributing profits among multiple data contributors and determining prospective compensation when data breaches happen. In this paper, we study the…
Paper presents a statistical method for detecting adversarial inputs.
Method estimates shared and study-specific factors for multi-study data.
Machine learning algorithms can be fooled by small well-designed adversarial perturbations. This is reminiscent of cellular decision-making where ligands (called antagonists) prevent correct signalling, like in early immune recognition. We draw a formal analogy between neural networks used in machine learning and model…
We develop a novel multi-fidelity framework that goes far beyond the classical AR(1) Co-kriging scheme of Kennedy and O'Hagan (2000). Our method can handle general discontinuous cross-correlations among systems with different levels of fidelity. A combination of multi-fidelity Gaussian Processes (AR(1) Co-kriging) and …
In tropical regions, populations continue to suffer morbidity and mortality from malaria and arboviral diseases. In Kedougou (Senegal), these illnesses are all endemic due to the climate and its geographical position. The co-circulation of malaria parasites and arboviruses can explain the observation of coinfected case…
New algorithm improves GAN stability and quality.
DCENWCNet improves WBC classification with LIME-based explainability.
Computational Intelligence (CI) is a sub-branch of Artificial Intelligence paradigm focusing on the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments. There are several paradigms of CI [like artificial neural networks, evolutionary computations, swarm intelli…
New method boosts skill learning by encouraging optimistic exploration.
We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most and least impacted units.…
EAGLE-Net enhances foundation models by integrating patch-level features for better tissue understanding.
New method for GLMs under DP provides private uncertainty quantification.
LOT framework embeds high-dimensional cell data into interpretable Euclidean space.
Gated recurrent units (GRUs) are specialized memory elements for building recurrent neural networks. Despite their incredible success on various tasks, including extracting dynamics underlying neural data, little is understood about the specific dynamics representable in a GRU network. As a result, it is both difficult…
New framework boosts neural network performance and resilience.
We establish minimax optimal rates of convergence for estimation in a high dimensional additive model assuming that it is approximately sparse. Our results reveal an interesting phase transition behavior universal to this class of high dimensional problems. In the {\it sparse regime} when the components are sufficientl…
Study uses machine learning to identify IBD biomarkers from gut microbiota.
New insights into adversarial vulnerability linked to manifold separability issues.
New algorithm classifies and generates genomic sequences using RG-flow categorifier.