Generative models improve pose transfer between people.
problem Transferring actions from one person to another.
method Used nearest neighbor and generative models (pix2pix) for pose transfer.
result Generative models outperform k-NN in generating corresponding frames and generalizing outside the action set.
Paper generates high-resolution fashion images based on body pose.
problem Limited variety of outfit images for shopping.
method Generative model trained on body pose and style transfer.
result Realistic high-resolution images of models in custom outfits.
Person Re-identification (re-id) faces two major challenges: the lack of cross-view paired training data and learning discriminative identity-sensitive and view-invariant features in the presence of large pose variations. In this work, we address both problems by proposing a novel deep person image generation model for…
Unsupervised mesh disentanglement separates identity and pose.
problem Geometric disentanglement for 3D deformable models.
method CFAN-VAE architecture using conformal factor and normal features.
result CFAN-VAE achieves state-of-the-art performance on unsupervised geometric disentanglement.
The paper analyzes and minimizes transferability of adversarial attacks between models in an ensemble.
problem Adversarial attacks can transfer between models, posing security risks.
method Introduces a gradient-based measure to assess and reduce transferability, and uses it during training to increase robustness.
result Demonstrates that the gradient-based measure can be used to increase an ensemble's robustness to adversarial attacks.
This paper reviews transfer learning for financial data predictions, highlighting its potential.
problem Accurate stock price prediction in financial time series is challenging due to noise and non-linear relationships.
method Transfer Learning applied to financial market predictions.
result Transfer Learning can improve financial prediction capability.
Deep generative modelling for human body analysis is an emerging problem with many interesting applications. However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility. In this work, we present deep generative models for human body analysis in which the body pose …
DVT transfers knowledge across domains using semi-supervised deep generative models.
problem Reduces labeling effort in domains with abundant labeled examples.
method Deep Variational Transfer (DVT) using a shared latent Gaussian mixture model.
result Achieves state-of-the-art classification performances across different datasets.
Enhances source domain knowledge with target data for transfer learning.
problem Limited data in target domains and rigid model assumptions in transfer learning.
method Transfer learning through Enhanced Sufficient Representation (TESR).
result TESR enhances source domain knowledge with target data, improving transfer learning performance.
Adapts Neyman-Pearson classification for both source and target distribution shifts.
problem Minimizing errors while controlling both Type-I and Type-II errors under distribution shifts.
method Derives an adaptive procedure that guarantees improved error rates and adapts to uninformative sources.
result Automatic adaptation to uninformative sources avoids negative transfer.
New insights into continual learning with task similarity.
problem Challenges in learning similar tasks without interference.
method Linear teacher-student model with latent structure.
result High input feature similarity with low readout similarity is catastrophic.
This work studies adversarial transferability and proposes ensemble methods to improve robustness.
problem Adversarial transferability in neural networks and its implications for robustness.
method Investigates the effect of various factors on adversarial transferability and proposes ensemble attack methods.
result Transferability is significantly hampered by input quantization and architectural mismatch, but not by initialization.
There are many forms of feature information present in video data. Principle among them are object identity information which is largely static across multiple video frames, and object pose and style information which continuously transforms from frame to frame. Most existing models confound these two types of represen…
Study uses deep learning to predict mycotoxin levels in Irish oats.
problem Predicting mycotoxin contamination in Irish oats to improve crop quality and safety.
method Investigated neural networks and transfer learning models for multi-response prediction.
result Transfer learning model TabPFN provided the best performance.
Generative model disentangles 3D shapes into independent factors.
problem Learning rich representations of deformable 3D shapes.
method Supervised 3D mesh-convolutional Variational AutoEncoder with latent feature disentanglement.
result Explicit disentanglement of latent factors improves shape generation and downstream tasks.
This paper tackles online strategic decision making with asymmetry and knowledge transportability.
problem Strategic decision making with information asymmetry and knowledge transportability challenges.
method Developed a sample-efficient algorithm for online learning under these conditions.
result Proved sample complexity of O(1/ε2) for learning an ε-optimal policy. Machine learning models adapt to motor learning but face challenges.
problem Adapting machine learning to handle motor variability and differentiate new movements from known ones.
method Parameter adaptation, transfer and meta-learning, reinforcement learning.
result Challenges in applying machine learning models for motor learning support systems.
Face recognition models can be inferred from student models, posing privacy risks.
problem Privacy threats in transfer learning models for face recognition.
method Membership inference attacks and attribute inference from aggregate-level information.
result Sensitive attributes can be inferred from student models, even with limited auxiliary information.
A new framework for federated continual learning reduces interference and improves performance.
problem Learning from a sequence of tasks with limited data from each client.
method Federated Weighted Inter-client Transfer (FedWeIT) framework.
result FedWeIT significantly outperforms existing methods with reduced communication cost.
It had been believed in the conventional practice that the risk of a bank going bankrupt is lessened in a straightforward manner by transferring the risk of loan defaults. But the failure of American International Group in 2008 posed a more complex aspect of financial contagion. This study presents an extension of the …
A new generator for GANs separates style and variation.
problem Improving GANs' ability to control and disentangle image attributes.
method Borrowing from style transfer, a new generator architecture.
result Improves GANs' quality and disentanglement of latent factors.
New algorithm reduces sample complexity for new tasks by leveraging prior knowledge.
problem Designing reinforcement learning agents that reduce sample complexity for new tasks.
method Designing an algorithm that quickly identifies an accurate solution by seeking informative state-action pairs from related tasks, using a generative model.
result PAC bounds on sample complexity demonstrate the benefits of using prior knowledge.
A new method improves EEG classification across subjects efficiently.
problem Challenges in adapting and retaining knowledge for EEG classifiers across different subjects.
method Meta UPdate Strategy (MUPS-EEG) for continuous EEG classification.
result Outperforms current state-of-the-art methods in adapting to new subjects and retaining knowledge of learned subjects.
Study improves risk evaluation timing with right-censored reporting delays.
problem Improving risk evaluation under short observation windows due to administrative censoring.
method Jointly models parametric hazards for event and reporting processes, uses Monte Carlo expectation-maximization algorithm, and proposes transfer-learning procedure.
result Improves accuracy of timely risk evaluation under administrative censoring.
ClimART dataset benchmarks ML emulators for atmospheric RT in climate models.
problem Lack of a comprehensive dataset and standardized practices for ML benchmarking in climate models.
method Builds ClimART, a large dataset with over 10 million samples, and presents novel baselines.
result Indicates shortcomings of prior datasets and network architectures.
SIRL recovers reward function probability distribution from expert actions.
problem Recovering reward functions from expert demonstrations in reinforcement learning.
method Monte Carlo Expectation-Maximization (MCEM) method to estimate reward function probability distribution.
result SIRL provides a robust and transferable solution to the IRL problem.
Graph neural networks predict future COVID-19 cases based on human mobility.
problem Predicting future COVID-19 cases using human mobility data.
method Created a graph with regions as nodes and human mobility as edge weights. Used graph neural networks to capture diffusion patterns and transfer learning for limited data.
result Graph neural networks outperform traditional methods in predicting future cases.
Bayesian meta-learning improves health prediction models across similar diseases.
problem Inter- and intra-task variability in healthcare predictions due to disease heterogeneity and patient differences.
method Bayesian meta-learning approach that models task similarity to mitigate negative transfer and improve generalizability.
result Significant generalizability improvements in stroke prediction tasks using electronic health record data.
Deep learning detects icebergs and ships from SAR data.
problem Detecting icebergs and ships from SAR data for Arctic navigation safety.
method Transfer Learning with a CNN, augmented data, and multiple outputs.
result Significant accuracy boost (logarithmic score 0.1463) in iceberg and ship detection.
InfoBridge uses diffusion bridges to estimate mutual information accurately.
problem Estimating mutual information between random variables.
method Formulated mutual information estimation as a domain transfer problem using diffusion bridge models.
result Demonstrated unbiased estimator for various data types.
'Sharing of statistical strength' is a phrase often employed in machine learning and signal processing. In sensor networks, for example, missing signals from certain sensors may be predicted by exploiting their correlation with observed signals acquired from other sensors. For humans, our hands move synchronously with …
Adversarial attacks pose a threat to deep neural networks, especially in safety-critical applications.
problem Adversarial attacks can misclassify deep neural networks, leading to safety issues.
method Adversarial attacks are categorized into white-box and black-box attacks based on the attacker's knowledge. They can be targeted or non-targeted.
result Adversarial attacks are effective and can transfer between different models and real-world scenarios.
Unified study of out-of-distribution generalization across 172 datasets.
problem Measuring and improving robustness of transfer learning models.
method Collect and fine-tune 31k models on 172 dataset pairs, varying architectures and settings.
result In- and out-of-distribution accuracies increase jointly but their relation is dataset-dependent.
A decentralized approach for agents to learn and optimize collectively.
problem Challenges in coordinating non-cooperative agents to solve complex sequential decision problems.
method Designing a learning environment where agents learn by trading and optimizing local objectives, leading to a Nash equilibrium.
result Decentralized reinforcement learning algorithms that can handle various decision-making scenarios.
GraphOpt learns the formation mechanism of graphs from observed structures.
problem Learning formation mechanisms from observed graphs with complex structural properties.
method GraphOpt uses maximum entropy inverse reinforcement learning to solve the link formation problem in a sequential decision-making process.
result GraphOpt discovers a latent objective function that can explain and transfer across different graphs.
New method improves deep learning model robustness and accuracy for long sequences.
problem Challenges in learning long-range sequence tasks using state-space models.
method Proposes a perturb-then-diagonalize (PTD) methodology to address ill-posed diagonalization problems in SSMs.
result Demonstrates improved robustness and accuracy of S5-PTD model on Long-Range Arena benchmark.
Informer improves financial market prediction accuracy with global time stamp features.
problem Intra-day minute-scale financial market prediction challenges.
method Informer network, a novel deep learning model with smaller computational complexity and global time stamp features.
result Informer achieves best performance on MAE, RMSE, and MAPE evaluation criteria across all datasets.
Deep learning classifies galaxies from Dark Energy Survey in just 8 minutes.
problem Classifying galaxies from large-scale surveys.
method Transfer learning from pre-trained neural networks.
result Achieved state-of-the-art accuracy of 99.6% in galaxy classification.
DeepWeightFlow generates diverse neural network weights efficiently.
problem Generating complete neural network weights efficiently and accurately.
method Flow Matching in weight space with Git Re-Basin and TransFusion.
result DeepWeightFlow generates high-accuracy neural networks without fine-tuning.
A new framework optimizes model transfer across domains with labeled data.
problem Distributional heterogeneity across domains in multi-source learning.
method Conditional Group Distributionally Robust Optimization (CG-DRO) framework with Mirror Prox algorithm and double machine learning.
result Established fast statistical convergence rates and uniformly valid inference for CG-DRO.
Physics-informed GP regression solves eigenvalue problems by identifying non-trivial eigenspaces.
problem Solving eigenvalue problems of linear operators with trivial solutions.
method Constructing a transfer function-type indicator using physics-informed Gaussian Process posterior.
result The posterior covariance is non-trivial only for eigenvalues of the operator, indicating non-trivial eigenspaces.
Transfer learning improves portfolio optimization by identifying transfer risk.
problem Financial portfolio optimization problem.
method Introduces transfer risk concept within transfer learning framework.
result Transfer risk is a significant indicator of transferability and enhances portfolio management efficiency.
Paper analyzes transfer risk in transfer learning for finance.
problem Evaluate transferability of transfer learning in finance.
method Proposes transfer risk concept and applies to stock return prediction and portfolio optimization.
result Transfer risk correlates with transfer learning performance and identifies appropriate source tasks.
This research tackles unsupervised topic extraction in noisy social media data.
problem Capturing customer insights from social media data is challenging due to noise and heterogeneity.
method The research presents three nonparametric approaches based on the Variational Autoencoder framework: Embedded Dirichlet Process, Embedded Hierarchical Dirichlet Process, and time-aware Dynamic Embedded Dirichlet Process.
result The models achieve equal to better performance than state-of-the-art methods in topic extraction from noisy social media data.
This paper explores the connection between adversarial and knowledge transferability.
problem Understanding the factors affecting knowledge transferability.
method Theoretical analysis and practical metrics for adversarial transferability.
result Adversarial transferability and knowledge transferability are closely related.
Docking is an important tool in computational drug discovery that aims to predict the binding pose of a ligand to a target protein through a combination of pose scoring and optimization. A scoring function that is differentiable with respect to atom positions can be used for both scoring and gradient-based optimization…
This work tackles domain shift in speech emotion recognition by proposing class-wise adversarial domain adaptation.
problem Domain shift between corpora poses a challenge for speech emotion recognition, especially for positive/negative emotions.
method Class-wise adversarial domain adaptation to reduce shift between different corpora.
result Our method is effective even with limited target labeled examples, as demonstrated on EMODB and Aibo corpora.
Mathematical framework for transfer learning feasibility and transfer risk.
problem Theoretical analysis of transfer learning.
method Reformulated transfer learning as an optimization problem, introduced transfer risk concept.
result Demonstrated the potential and benefits of incorporating transfer risk in transfer learning evaluation.