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.
CDSPP learns domain-specific projections for heterogeneous domain adaptation.
FinGPT uses LLMs for real-time market sentiment analysis.
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…
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 paper improves classification accuracy by leveraging a shared signal across domains in high-dimensional classification.
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…
CSD learns a common component for domain generalization, outperforming existing methods.
EQD model improves domain-specific QA by 0.6% to 10.5%.
Interventional domain adaptation improves feature transferability by removing spurious correlations.
Enhanced AI analysis predicts S&P 500 stock dynamics using various financial metrics.
LLMs translate natural language trading intents into correct option strategies using a domain-specific language.
Learn to automatically plug domain-specific modules into a common network.
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.
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.
Method learns domain-specific representations without supervision.
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.
A new model for imputing missing values in time series data across domains.
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…
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…
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 …
Improves Active Learning fairness and comparability across domains.
We address the problem of domain generalization where a decision function is learned from the data of several related domains, and the goal is to apply it on an unseen domain successfully. It is assumed that there is plenty of labeled data available in source domains (also called as training domain), but no labeled dat…
Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, information retrieval, computer vision, social and health informatics, ubiquitous computing, etc. Two essential problems of machine learning are …
Study evaluates how much knowledge LLMs have by comparing their prediction accuracy to flexible models.
FinCast is a foundation model for financial time-series forecasting that outperforms existing methods.
We develop a family of reformulations of an arbitrary consistent linear system into a stochastic problem. The reformulations are governed by two user-defined parameters: a positive definite matrix defining a norm, and an arbitrary discrete or continuous distribution over random matrices. Our reformulation has several e…
Study uses ML to predict currency and bond returns from news sentiment.
In this paper, we propose a novel unsupervised clustering approach exploiting the hidden information that is indirectly introduced through a pseudo classification objective. Specifically, we randomly assign a pseudo parent-class label to each observation which is then modified by applying the domain specific transforma…
Data augmentation is a ubiquitous technique for increasing the size of labeled training sets by leveraging task-specific data transformations that preserve class labels. While it is often easy for domain experts to specify individual transformations, constructing and tuning the more sophisticated compositions typically…
Consider a Bayesian inference problem where a variable of interest does not take values in a Euclidean space. These "non-standard" data structures are in reality fairly common. They are frequently used in problems involving latent discrete factor models, networks, and domain specific problems such as sequence alignment…
Neural networks outperform conventional filters in inertial sensor-based attitude estimation.
Paper improves Frank-Wolfe algorithm's efficiency bounds.
This paper introduces a new transfer learning method for regression.
A new framework pretrains a single GNN model for diverse graphs, overcoming domain-specific challenges.
In compressed sensing, a small number of linear measurements can be used to reconstruct an unknown signal. Existing approaches leverage assumptions on the structure of these signals, such as sparsity or the availability of a generative model. A domain-specific generative model can provide a stronger prior and thus allo…