New framework for multi-domain translation using autoencoders.
problem Learning probabilistic coupling between different domains.
method Learning multiple uncoupled autoencoders under shared latent distribution.
result New autoencoders can be added sequentially without retraining.
Proposes a model to improve multi-domain recommender systems.
problem Challenges in transferring knowledge between domains in recommender systems.
method Generative adversarial networks (GANs), Variational Autoencoders (VAEs), and Cycle-Consistency (CC) for weight-sharing.
result Improves performance of multi-domain recommender systems by capturing both similarities and differences among domains.
Robust image translation model for noisy labels.
problem Learning mappings among multiple domains with noisy labeled data.
method Proposes a novel loss function and techniques to handle noisy labeled data.
result Demonstrates robustness in various settings including synthetic and real-world noise.
We present a method for translating music across musical instruments, genres, and styles. This method is based on a multi-domain wavenet autoencoder, with a shared encoder and a disentangled latent space that is trained end-to-end on waveforms. Employing a diverse training dataset and large net capacity, the domain-ind…
Precision medicine aims for personalized prognosis and therapeutics by utilizing recent genome-scale high-throughput profiling techniques, including next-generation sequencing (NGS). However, translating NGS data faces several challenges. First, NGS count data are often overdispersed, requiring appropriate modeling. Se…
CollaGAN uses GANs to impute missing image data.
problem Missing data bias in applications requiring multiple inputs.
method Collaborative Generative Adversarial Network (CollaGAN) for multi-domain image imputation.
result CollaGAN produces higher quality imputed images than existing methods.
Adaptive multi-domain learning reduces parameter count for efficient deep learning.
problem Different domains have varying complexity, leading to inefficient model training.
method Proposes adaptive parameterization to reduce model complexity without sacrificing performance.
result Efficient multi-domain learning solutions with far fewer parameters.
Dual adversarial co-learning improves multi-domain text classification.
problem Improving text classification across multiple domains.
method Dual adversarial co-learning with shared-private networks and dual adversarial regularizations.
result Achieves state-of-the-art performance on multi-domain sentiment classification datasets.
MuLANN tackles multi-domain learning with adversarial approach.
problem Automated microscopy data with domain bias.
method Semi-supervised multi-domain learning with MuLANN.
result Improves state of the art on image benchmarks and bioimage dataset.
MWGAN tackles multi-marginal matching problem with Wasserstein GAN.
problem Learning mappings to match a source domain to multiple target domains with cross-domain correlations.
method Develops a novel Multi-marginal Wasserstein GAN (MWGAN) with inner- and inter-domain constraints to minimize Wasserstein distance.
result Theoretical and empirical evaluations show MWGAN's effectiveness on balanced and imbalanced translation tasks.
Proposes MD-LiNA for multi-domain latent factor causal discovery.
problem Discovering causal structures among latent factors from multi-domain data.
method Multi-Domain Linear Non-Gaussian Acyclic Models (MD-LiNA) with an integrated two-phase algorithm.
result Locally consistent estimators of causal structure among shared latent factors.
MetalGAN synthesizes images across multiple domains without labels.
problem Synthesizing images across multiple domains using a single network.
method Combines cGAN for image generation and Meta-Learning for domain switch.
result MetalGAN successfully produces multi-domain images without hard-coded labels.
New method identifies stable latent variables across different domains using weak distributional invariances.
problem Learning causal representations for multi-domain datasets.
method Autoencoders incorporating weak distributional invariances.
result Autoencoders can identify stable latent variables across different domains.
We address the problems of multi-domain and single-domain regression based on distinct and unpaired labeled training sets for each of the domains and a large unlabeled training set from all domains. We formulate these problems as a Bayesian estimation with partial knowledge of statistical relations. We propose a worst-…
Improves dialogue state tracking across multiple domains.
problem Incomplete domain ontology limits DST models' adaptability.
method Model DST as Q&A, using evolving knowledge graph.
result 5.80% and 12.21% relative improvement on datasets.
Estimates causal effect using proxies in multi-domain settings.
problem Estimating causal effect in settings with unobserved confounders across domains.
method Proposes estimation techniques using proxy variables for discrete or categorical data.
result Proves identifiability and consistency of causal effect estimation.
Unified framework for multi-domain learning and data imputation.
problem Improving performance across different domains with missing data.
method Adversarial autoencoder for domain-invariant embeddings and data imputation.
result Superior performance compared to state-of-the-art methods in various settings.
Proposes adversarial normalization for multi-domain image segmentation.
problem Current image normalization is per-dataset, limiting multi-domain segmentation.
method Adversarial training to learn common normalizing functions across multiple datasets.
result Optimal normalizer improves segmentation accuracy and realism.
Paper develops a new method to improve model calibration under distribution shifts.
problem Challenges in uncertainty quantification with different training and test distributions.
method Develops multi-domain temperature scaling to handle distribution shifts.
result Outperforms existing methods on in-distribution and out-of-distribution test sets.
Domain Fusion uses GANs to augment data for low-volume target datasets.
problem High costs in data development for deep learning applications.
method Multi-domain learning GANs to generate new samples.
result Domain Fusion achieves better classification accuracy with less data.
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.
Enhances performance on downstream tasks using multi-domain data.
problem Improving performance on tasks with limited downstream data.
method Deep transfer learning framework leveraging shared and domain-specific features.
result Significantly improves convergence rate for learning Lipschitz functions.
Framework integrates mental disorder measurements for personalized treatment.
problem Optimizing treatment for mental disorders with latent mental states and heterogeneity.
method Measurement theory and multi-layer neural network for complex treatment effects.
result Learned treatment policies outperform alternatives on heterogeneous treatment effects.
Parallel texts are a relatively rare language resource, however, they constitute a very useful research material with a wide range of applications. This study presents and analyses new methodologies we developed for obtaining such data from previously built comparable corpora. The methodologies are automatic and unsupe…
TESTED improves multi-domain stance detection with topic-guided sampling and contrastive learning.
problem Challenges in multi-domain stance detection due to domain-specific variations and imbalanced annotations.
method Topic-guided diversity sampling and contrastive learning objective.
result Significant improvement in F1 scores, up to 10.2 points out-of-domain.
Empirical Bayes improves causal representation learning across multiple domains.
problem Estimating causal representations from data across multiple domains.
method Developed an EB f-modeling algorithm for linearly-mixed causal representations. result Our method achieves more accurate estimation of causal variables than other methods.
Single CNN removes multiple ultrasound artifacts.
problem Efficiently remove multiple ultrasound artifacts.
method OT-driven multi-domain unsupervised deep learning.
result Single neural network removes various artifacts.
Anchor PCA improves robustness in multi-domain PCA.
problem PCA on pooled data can focus on spurious directions.
method Anchor PCA focuses on shared directions of variation.
result Anchor PCA outperforms pooling and worst-case alternatives.
Proposes a unified normalization method for multi-domain medical images.
problem Inadequate joint information across multiple datasets hinders image segmentation performance.
method Adversarial and task-driven normalization approach to learn a common normalizing function across multiple datasets.
result Jointly normalized images improve segmentation accuracy by up to 57.5%.
In this paper, we provide a new neural-network based perspective on multi-task learning (MTL) and multi-domain learning (MDL). By introducing the concept of a semantic descriptor, this framework unifies MDL and MTL as well as encompassing various classic and recent MTL/MDL algorithms by interpreting them as different w…
Unpaired multi-domain causal representation learning is possible with sufficient conditions.
problem Learning shared causal representation from unpaired data across domains.
method Identify sufficient conditions for joint distribution and shared causal graph recovery.
result Practical method to recover shared latent causal graph from marginal distributions.
Novel method adapts MRI brain images across multiple domains.
problem Generalization failure in medical image learning across different acquisition parameters.
method Consistency loss combined with adversarial learning.
result Significantly outperforms other domain adaptation methods in MRI lesion segmentation.
New method learns to generalize across different data domains efficiently.
problem Learning across different data domains with varying distributions.
method A theoretical model with multiple datasets from different domains, focusing on polynomial-sample complexity.
result Computational efficiency and polynomial-sample domain generalization are achievable.
This paper aims to classify a single PCG recording as normal or abnormal for computer-aided diagnosis. The proposed framework for this challenge has four steps: preprocessing, feature extraction, training and validation. In the preprocessing step, a recording is segmented into four states, i.e., the first heart sound, …
GOLOMB improves dialogue state tracking for unseen services.
problem Improving dialogue state tracking for multiple services and APIs.
method GOLOMB uses a BERT-based model that queries dialogue history with slot descriptions and values.
result GOLOMB achieves a joint goal accuracy of 53.97% on the SGD dataset.
TIMeSynC combines financial service interactions for intent prediction.
problem Aligning and learning from multi-domain, multi-resolution sequences for accurate intent prediction.
method An encoder-decoder transformer model addressing sequence alignment, temporal dynamics, and dynamic/static sequence combination.
result Significant improvement in intent prediction over existing methods.
Dynamic residual adapters improve performance across multiple latent domains without domain labels.
problem Overfitting to large domains and ignoring smaller ones in multi-domain learning.
method Dynamic residual adapters and augmentation strategies inspired by style transfer.
result Dynamic residual adapters significantly outperform standard models on multiple latent domains.
Automated dialogue quality evaluation using user satisfaction estimates across multiple domains.
problem Lack of automated and domain-independent dialogue quality evaluation metrics.
method Created a new Response Quality annotation scheme, introduced five domain-independent feature sets, and experimented with six machine learning models.
result Gradient Boosting Regression model achieved best prediction performance, with a 16% relative improvement in binary satisfaction class prediction accuracy.
URT layer improves few-shot image classification across diverse domains.
problem Few-shot image classification in multi-domain settings.
method Meta-learns to dynamically re-weight and compose domain-specific representations.
result Sets new state-of-the-art on Meta-Dataset.
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.
MA-DST improves multi-domain dialog state tracking.
problem Accurate multi-domain dialog state tracking in natural language interfaces.
method Multi-attention based architecture to encode conversation history and slot semantics.
result Improves joint goal accuracy by 5% in full-data setting and up to 2% in zero-shot setting.
CFA improves model's ability to generalize across unseen domain-class combinations.
problem Challenges in real-world machine learning applications due to data distribution shifts and limited training data.
method Developed Compositional Feature Alignment (CFA) technique to improve CG ability of pretrained models.
result CFA outperforms common finetuning techniques in compositional generalization.
Study uses ML to predict cancer patient mortality from FN onset.
problem Predicting mortality in cancer patients with FN to improve survival.
method Multi-domain machine learning models using HCUP data.
result Clinical diagnoses have highest predictive power for FN mortality.
Improved voice conversion model using single generator.
problem Challenges in non-parallel multi-domain voice conversion.
method Revised conditional methods in StarGAN-VC2, including new loss functions and network architectures.
result Significant improvement in speech quality and naturalness.
Deep CNN classifies EEG-based brain connectivity in schizophrenia.
problem Classifying neuropsychiatric disorders using EEG connectivity.
method Multi-domain connectome CNN framework integrating time and frequency-domain metrics.
result MDC-CNN achieves 93.06% accuracy in schizophrenia classification. Empirical law predicts accuracy of Google Translate's translation chains.
problem Predicting accuracy in machine translation with multiple hops.
method Empirical testing of Google Translate's sequential translation.
result Accuracy decreases with the number of translating hops, following a power law.
The authors of (Cho et al., 2014a) have shown that the recently introduced neural network translation systems suffer from a significant drop in translation quality when translating long sentences, unlike existing phrase-based translation systems. In this paper, we propose a way to address this issue by automatically se…
New linear models improve time series classification efficiency and interpretability.
problem Complex and inefficient classifiers limit interpretability and applicability to variable-length time series.
method Symbolic representations, multi-resolution, multi-domain, linear models.
result mtSS-SEQL+LR achieves similar accuracy to state-of-the-art methods but with lower time and memory usage.