Study introduces KorFinMTEB for Korean financial texts, revealing model limitations.
problem Limited evaluation benchmarks for low-resource domains, especially Korean.
method Developed KorFinMTEB, a tailored benchmark for Korean financial texts.
result Models perform better on translated benchmarks than on domain-specific ones.
Study reveals XAI methods fail in neuroimaging, suggesting domain-specific adaptation.
problem Systematic failures of XAI methods in neuroimaging applications.
method Systematic comparison of XAI methods on 45,000 structural brain MRIs using a novel validation framework.
result Two widely used XAI methods (GradCAM and Layer-wise Relevance Propagation) fail to accurately explain neuroimaging data.
A new model for imputing missing values in time series data across domains.
problem Imputing missing values in time series data across domains with domain shifts and high missing rates.
method A diffusion-based imputation model that integrates shared spectral components and domain-specific temporal structures, with cross-domain consistency alignment.
result Our model effectively handles missing values and domain shifts, outperforming existing methods.
Improves machine learning performance with domain-specific embeddings.
problem Tuning word embeddings for specific use cases and domains.
method Combines multiple domain-specific embeddings using a ranking function and dimensionality reduction.
result Effective domain-specific embeddings improve machine learning performance.
Unified QuesNet learns comprehensive representations for diverse test questions.
problem Lack of labeled data for test questions in online learning systems.
method Unified framework and two-level hierarchical pre-training algorithm for unsupervised learning of heterogeneous question representations.
result QuesNet effectively learns comprehensive question representations and outperforms existing methods.
CUQ-GNN adapts uncertainty quantification for graph data, improving on GPN.
problem Defining meaningful uncertainty on graph data with domain-specific characteristics.
method Combines Graph Neural Networks with Posterior Networks using Normalizing Flows.
result CUQ-GNN produces more flexible and effective uncertainty estimates.
One of the defining characteristics of human creativity is the ability to make conceptual leaps, creating something surprising from typical knowledge. In comparison, deep neural networks often struggle to handle cases outside of their training data, which is especially problematic for problems with limited training dat…
DSSM separates domain-invariant dynamics from domain-specifics in sequential data.
problem Learning cross-domain sequence representations from diverse data domains.
method Introduce disentangled state space models (DSSM) using unsupervised VAE-based training.
result Improves knowledge transfer and robust prediction across domains.
CosML combines domain-specific meta-learners for cross-domain few-shot classification.
problem Generalizing to unseen domains while meta-learning on multiple seen domains.
method CosML trains domain-specific meta-learners and combines their meta-parameters in the parameter space.
result CosML outperforms state-of-the-art methods and achieves strong cross-domain generalization.
Method constructs finance LLMs without instruction data using pretraining and model merging.
problem Developing domain-specific LLMs for finance is resource-intensive.
method Continual pretraining on financial data + model merging of instruction-tuned and domain-specific pretrained vectors.
result Successfully constructs instruction-tuned LLMs for finance without additional instruction data.
The paper improves classification accuracy by leveraging a shared signal across domains in high-dimensional classification.
problem Improving classification accuracy in high-dimensional data with shared signals across domains.
method Transfer learning for linear discriminant analysis, decomposing mean differences into common and domain-specific components.
result Deterministic limits for transfer performance, leading to optimal weights and corrections for bias.
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.
problem Training models to generalize across unseen domains.
method CSD decomposes the model into a common and specific component, discarding the latter.
result CSD outperforms state-of-the-art domain generalization methods.
EQD model improves domain-specific QA by 0.6% to 10.5%.
problem Challenges in domain-specific quantitative reasoning for LLMs.
method Two-step fine-tuning framework guided by a reward function.
result EQD outperforms state-of-the-art models and prompting strategies.
In this paper, we present Russian language datasets in the digital humanities domain for the evaluation of word embedding techniques or similar language modeling and feature learning algorithms. The datasets are split into two task types, word intrusion and word analogy, and contain 31362 task units in total. The chara…
Interventional domain adaptation improves feature transferability by removing spurious correlations.
problem Improper feature transferability due to spurious correlations in domain adaptation.
method Intervention strategy using unlabeled target data to generate counterfactual features and train discriminability invariance.
result Consistent performance improvements over state-of-the-art approaches in various domain adaptation tasks.
LLMs translate natural language trading intents into correct option strategies using a domain-specific language.
problem Challenges in translating natural language trading intents into correct option strategies due to the complexity of option chain data.
method Introduce Option Query Language (OQL) as a domain-specific intermediate representation to abstract option markets into high-level primitives under grammatical rules. Use LLMs as semantic parsers and validate queries by an engine.
result Significantly improves execution accuracy and logical consistency over direct baselines.
The paper tackles learning domain-specific bias by learning many tasks from the same domain.
problem Learning appropriate domain-specific bias in machine learning models.
method Learning many related tasks from the same domain, with a theorem bounding the number of tasks needed.
result The number of examples required per task for good generalization scales like \(O(a + \frac{b}{n})\), where \(O(a)\) is the minimum number of examples per task and \(O(a + b)\) is the number of examples per task independently.
Learn to automatically plug domain-specific modules into a common network.
problem Learning inflexibility and computational intensiveness in multi-domain learning.
method Neural Architecture Search (NAS) for data-driven adapter plugging and structure design.
result NAS-driven MDL model achieves comparable performance to existing approaches.
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.
problem Domain-specific contrastive learning methods rely on data augmentation techniques that require domain knowledge.
method DACL uses Mixup noise to create similar and dissimilar examples without domain-specific data augmentation.
result DACL outperforms other domain-agnostic noising methods and combines well with domain-specific methods.
EMFs combine deep learning and probabilistic models for better density estimation.
problem Combining domain-specific knowledge with general-purpose deep learning.
method Alternating transformations with structured layers that embed domain-specific inductive biases.
result EMFs induce desirable properties like multimodality and hierarchical coupling.
Protocol for constructing tailored evaluation datasets for semantic models.
problem Evaluation of domain-specific semantic models, focusing on top ranks.
method Adaptive pairwise comparisons, relatedness-based evaluation dataset, metrics, stochastic transitivity model.
result Effectiveness of the proposed dataset construction protocol confirmed.
Framework improves CT image segmentation robustness with domain-specific cues.
problem Challenges in CT image segmentation by deep learning models.
method Combines domain-specific preprocessing and augmentation with CNN architectures.
result Framework stabilizes prediction performance on varying CT volumes.
Hybrid approach combines topic and graph embeddings for legal document clustering.
problem Challenges in classifying legal texts due to domain-specific language and limited labeled data.
method Combines unsupervised topic and graph embeddings with a supervised model.
result Improves clustering quality over text-only or graph-only embeddings.
New method transfers word embeddings from large to small datasets efficiently.
problem Challenges in learning word embeddings from new domains with limited data.
method Group-sparse matrix factorization for transfer learning.
result Efficiently learns domain-specific word embeddings with less data.
Method learns domain-specific representations without supervision.
problem Domain generalization without labeled data.
method Hierarchical variational autoencoder approach.
result Model generalizes to unseen domains without domain supervision.
Proposes a novel framework for unsupervised domain adaptation using specialized batch normalization.
problem Improves unsupervised domain adaptation in deep neural networks.
method Integrates domain-specific batch normalization layers in convolutional neural networks, estimating pseudo-labels for target domain examples and learning final models with multi-task classification loss.
result Achieves state-of-the-art accuracy in standard and multi-source domain adaptation scenarios.
Paper proposes a unified time series forecasting model with adaptive transfer.
problem General forecasting models for diverse time series data.
method Unified representations through Decomposed Frequency Learning and adaptive domain-specific features via Time Series Register.
result State-of-the-art forecasting performance on seven real-world benchmarks.
The discovery of adversarial examples has raised concerns about the practical deployment of deep learning systems. In this paper, we demonstrate that adversarial examples are capable of manipulating deep learning systems across three clinical domains. For each of our representative medical deep learning classifiers, bo…
A new CNN architecture tackles domain shifts with a dictionary approach.
problem Handling domain shifts in deep learning models.
method Decompose CNN layers into domain-specific and shared parts using a dictionary of atoms.
result The approach promotes shared semantics across domains with minimal additional parameters.
Algorithm uncovers two main patterns of online content popularity: bursty and steady.
problem Understanding how online content gains popularity over time.
method Multi-faceted temporal analysis using dipm-SC algorithm.
result Two main patterns of popularity: bursty and steady temporal behaviors.
This paper examines inductive biases in deep reinforcement learning and their impact on performance.
problem The trade-off between inductive biases and performance in deep reinforcement learning.
method Investigated domain-specific components and adaptive solutions in deep reinforcement learning agents.
result Adaptive components can sometimes outperform domain-specific components, but not always.
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 work improves medical image segmentation with limited annotations using contrastive learning.
problem Lack of labeled data for medical image segmentation.
method Contrastive learning framework for semi-supervised segmentation with domain-specific and problem-specific cues.
result Significant improvements in segmentation performance compared to other methods.
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.
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.
problem Inconclusive Active Learning research due to domain-specific results.
method Develops a fair comparison framework and oracle algorithm.
result Empirical results rank 6 algorithms across 3 domains.
New graph embedding method uses domain-specific knowledge.
problem Lack of semantic information in existing graph embedding approaches.
method Domain-aware biased random walks to incorporate semantic information.
result Embeddings achieve equal or greater accuracy than domain-independent methods.
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 …
This study tackles mutual fund portfolio prediction, focusing on novel items.
problem Predicting novel items in mutual fund portfolios is challenging and less explored.
method Created a comprehensive benchmark dataset and evaluated various recommender system models.
result Autoencoder-based approaches outperform state-of-the-art models in predicting novel items.
Study evaluates how much knowledge LLMs have by comparing their prediction accuracy to flexible models.
problem Evaluating the predictive power of LLMs without access to their training data.
method Equivalent sample size measure, comparing LLM's prediction error to flexible models trained on varying amounts of domain-specific data.
result LLMs encode varying amounts of predictive information across different economic variables.
FinCast is a foundation model for financial time-series forecasting that outperforms existing methods.
problem Challenges in financial time-series forecasting due to temporal non-stationarity, multi-domain diversity, and varying temporal resolutions.
method FinCast is a foundation model specifically designed for financial time-series forecasting, trained on large-scale financial datasets.
result FinCast exhibits robust zero-shot performance, effectively capturing diverse patterns without domain-specific fine-tuning.
CDSPP learns domain-specific projections for heterogeneous domain adaptation.
problem Heterogeneous domain adaptation problems where source and target domains have different modalities or feature dimensions.
method Cross-Domain Structure Preserving Projection (CDSPP) algorithm that learns domain-specific projections to map features into a common subspace.
result CDSPP achieves superior performance in both supervised and semi-supervised HDA compared to state-of-the-art methods.
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…