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.
CSD learns a common component for domain generalization, outperforming existing methods.
The paper improves classification accuracy by leveraging a shared signal across domains in high-dimensional classification.
We address the problem of tuning word embeddings for specific use cases and domains. We propose a new method that automatically combines multiple domain-specific embeddings, selected from a wide range of pre-trained domain-specific embeddings, to improve their combined expressive power. Our approach relies on two key c…
Many deep reinforcement learning algorithms contain inductive biases that sculpt the agent's objective and its interface to the environment. These inductive biases can take many forms, including domain knowledge and pretuned hyper-parameters. In general, there is a trade-off between generality and performance when algo…
We propose a method to infer domain-specific models such as classifiers for unseen domains, from which no data are given in the training phase, without domain semantic descriptors. When training and test distributions are different, standard supervised learning methods perform poorly. Zero-shot domain adaptation attemp…
Generative model identifies temporal count data components with regime-dependent contributions.
Anchor PCA improves robustness in multi-domain PCA.
A new model for imputing missing values in time series data across domains.
CosML combines domain-specific meta-learners for cross-domain few-shot classification.
Study introduces KorFinMTEB for Korean financial texts, revealing model limitations.
Method constructs finance LLMs without instruction data using pretraining and model merging.
The process of designing neural architectures requires expert knowledge and extensive trial and error. While automated architecture search may simplify these requirements, the recurrent neural network (RNN) architectures generated by existing methods are limited in both flexibility and components. We propose a domain-s…
Part-of-speech (POS) tagging is a fundamental component for performing natural language tasks such as parsing, information extraction, and question answering. When POS taggers are trained in one domain and applied in significantly different domains, their performance can degrade dramatically. We present a methodology f…
EQD model improves domain-specific QA by 0.6% to 10.5%.
Interventional domain adaptation improves feature transferability by removing spurious correlations.
LLMs translate natural language trading intents into correct option strategies using a domain-specific language.
Algorithms are increasingly common components of high-impact decision-making, and a growing body of literature on adversarial examples in laboratory settings indicates that standard machine learning models are not robust. This suggests that real-world systems are also susceptible to manipulation or misclassification, w…
Learn to automatically plug domain-specific modules into a common network.
In this paper, we address customer review understanding problems by using supervised machine learning approaches, in order to achieve a fully automatic review aspects categorisation and sentiment analysis. In general, such supervised learning algorithms require domain-specific expert knowledge for generating high quali…
Study reveals XAI methods fail in neuroimaging, suggesting domain-specific adaptation.
This paper introduces Sigma, a domain-specific computational representation for collaboration in large-scale for the field of economics. A computational representation is not a programming language or a software platform. A computational representation is a domain-specific representation system based on three specific …
DACL tackles domain-specific contrastive learning by using Mixup noise.
We propose a novel unsupervised domain adaptation framework based on domain-specific batch normalization in deep neural networks. We aim to adapt to both domains by specializing batch normalization layers in convolutional neural networks while allowing them to share all other model parameters, which is realized by a tw…
EMFs combine deep learning and probabilistic models for better density estimation.
Protocol for constructing tailored evaluation datasets for semantic models.
CbMLC improves multi-label classification with noisy labels.
A new method models user-specific parameters as a low-rank plus sparse component for efficient personalization.
In this paper, we consider domain-invariant deep learning by explicitly modeling domain shifts with only a small amount of domain-specific parameters in a Convolutional Neural Network (CNN). By exploiting the observation that a convolutional filter can be well approximated as a linear combination of a small set of dict…
Hybrid approach combines topic and graph embeddings for legal document clustering.
New method transfers word embeddings from large to small datasets efficiently.
G-FuNK learns solutions for nonlinear PDEs on multiple domains and parameters.
Method learns domain-specific representations without supervision.
Many real-world data sets, especially in biology, are produced by complex nonlinear dynamical systems. In this paper, we focus on brain calcium imaging (CaI) of different organisms (zebrafish and rat), aiming to build a model of joint activation dynamics in large neuronal populations, including the whole brain of zebra…
Sequential data often originates from diverse domains across which statistical regularities and domain specifics exist. To specifically learn cross-domain sequence representations, we introduce disentangled state space models (DSSM) -- a class of SSM in which domain-invariant state dynamics is explicitly disentangled f…
Paper proposes a unified time series forecasting model with adaptive transfer.
SOR framework improves transfer learning efficiency and adaptability.
FinEAS models financial sentiment using BERT embeddings.
Ontology learning is a critical task in industry, dealing with identifying and extracting concepts captured in text data such that these concepts can be used in different tasks, e.g. information retrieval. Ontology learning is non-trivial due to several reasons with limited amount of prior research work that automatica…
Classifies Bitcoin addresses based on their balance functions.
By introducing several improvements to the AlphaZero process and architecture, we greatly accelerate self-play learning in Go, achieving a 50x reduction in computation over comparable methods. Like AlphaZero and replications such as ELF OpenGo and Leela Zero, our bot KataGo only learns from neural-net-guided Monte Carl…
A long standing problem in visual object categorization is the ability of algorithms to generalize across different testing conditions. The problem has been formalized as a covariate shift among the probability distributions generating the training data (source) and the test data (target) and several domain adaptation …
New method learns from subgroup feedback in complex systems.
More than 200 generic drugs approved by the U.S. Food and Drug Administration for non-cancer indications have shown promise for treating cancer. Due to their long history of safe patient use, low cost, and widespread availability, repurposing of generic drugs represents a major opportunity to rapidly improve outcomes f…
This study evaluates the performances of an LSTM network for detecting and extracting the intent and content of com- mands for a financial chatbot. It presents two techniques, sequence to sequence learning and Multi-Task Learning, which might improve on the previous task.
This work improves medical image segmentation with limited annotations using contrastive learning.
Equation discovery methods enable modelers to combine domain-specific knowledge and system identification to construct models most suitable for a selected modeling task. The method described and evaluated in this paper can be used as a nonlinear system identification method for gray-box modeling. It consists of two int…
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. This is especially important in the task of molecular graph generation, whose goal is to discover novel molecules with desired properties such as …
Method improves clarity in forecasting spatio-temporal data.