Melanie improves predictive performance in non-stationary data streams by transferring knowledge between multiple sources.
problem Concept drift in data streams leads to poor predictive performance.
method Melanie uses multiple sub-classifiers to learn different aspects from various sources and compose an ensemble for the target concept.
result Melanie improves predictive performance over existing algorithms by leveraging multiple sources.
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.
New method tackles concept shifts in nonparametric regression using robust and adaptive transfer learning.
problem Concept shifts and sample scarcity in target domains hinder nonparametric regression.
method Robust and adaptive transfer learning procedure leveraging fixed bandwidth Gaussian kernels.
result Spectral algorithms with fixed bandwidth Gaussian kernels attain minimax convergence rates for nonparametric regression.
Algorithm transfers visual concepts to answer out-of-vocabulary questions.
problem Leveraging off-the-shelf visual and linguistic data for out-of-vocabulary answers in visual question answering.
method Unsupervised task discovery for learning task conditional visual classifier, then transferring to visual question answering models.
result Algorithm generalizes to out-of-vocabulary answers successfully.
Paper shows pre-training and transfer learning reduce sample complexity for neural networks.
problem Training high-dimensional supervised learning with limited labeled data.
method Study of single-layer neural networks via online stochastic gradient descent, considering concept shift.
result Pre-training and transfer learning reduce sample complexity by polynomial factors under general assumptions.
VUSFA improves transfer learning for target-driven navigation in AI2THOR.
problem Improving transfer reinforcement learning for complex visual navigation tasks.
method Introducing SFDP and Variational Information Bottlenecks to A3C agent.
result VUSFA achieves state-of-the-art performance and generalizability.
Paper introduces a probabilistic framework for k-shot image classification.
problem Generalizing from a large-scale classification task to a new task with new classes and few examples.
method Leverages feature-based representation and concept information from a neural network, encapsulated in a probabilistic model.
result Simple probabilistic model achieves state-of-the-art performance on k-shot learning datasets.
Transfer learning adapted for hybrid classical-quantum neural networks.
problem Optimizing data preprocessing and feature embedding for quantum processors.
method Adapting transfer learning to hybrid networks, using a pre-trained classical network augmented by a quantum circuit.
result Demonstrated the effectiveness of quantum transfer learning for image recognition and quantum state classification.
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.
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.
MIXGAN combines concepts from different domains for new image generation.
problem Generating new images with mixed content and style from different domains.
method MIXGAN is a mixture generative adversarial network that learns content and style from two domains and generates new images combining them.
result MIXGAN effectively generates new images with mixed content and style from different domains.
A new 1-transfer space construction for hyperbolic groups.
problem Proving the Farrell-Jones conjecture for hyperbolic groups.
method Explicit construction of a 1-transfer space.
result A new method to prove the Farrell-Jones conjecture for hyperbolic groups.
Extends micro-price concept to RFQ markets for fair pricing.
problem Valuing securities in illiquid RFQ markets.
method Bidimensional Markov-modulated Poisson processes for liquidity.
result Introduces Fair Transfer Price for fair securities valuation.
Unified framework supports lifelong learning properties.
problem Capturing desirable properties of human learning in machine learning.
method Unified framework with one central mechanism.
result Supports almost all desirable properties of human learning.
AlphaZero reveals new chess concepts learnable by top experts.
problem Extracting and understanding hidden knowledge from AI systems.
method Proposed method to extract new chess concepts from AlphaZero.
result Top chess grandmasters show improvements in learning new concepts.
Bayesian methods improve learning from related domains.
problem Finding optimal information to transfer between domains.
method Bayesian approaches to transfer learning.
result Bayesian methods outperform frequentist competitors in simulations.
New method discovers concepts in hidden feature layers using sparse subspace clustering.
problem Local attribution methods fail to identify coherent model behavior across samples.
method Sparse Subspace Clustering (SSCC) for concept discovery.
result Empirically validated method for various image classification tasks.
Transfer learning improves understanding of users on new Web platforms.
problem Lack of knowledge about novel phenomena on new Web platforms due to data sparsity.
method TraNet, a transfer learning-based approach, adapts knowledge from one domain to another.
result TraNet outperforms other approaches in transferring knowledge about users across different Web platforms.
Proposes a new method to better understand complex system interactions.
problem Current methods like Granger causality and transfer entropy fail to capture higher-order interactions.
method Introduces a generalized approach to capture multivariate causal interactions.
result The method can distinguish causal roles in synergetic interactions.
Enhances generative model accuracy through knowledge transfer.
problem Improving generative model precision across different tasks.
method Introduces a novel framework for transfer learning using shared structures.
result Demonstrates enhanced performance in diffusion and normalizing flows models.
This paper develops geometric tools for causal inference using information flow concepts.
problem Developing a geometric interpretation of causal inference from probabilistic measures.
method Introducing a new measure, GeoC_{y
ightarrow x}, based on fractal correlation dimension.
result Avoids boundedness issues in transfer entropy, providing a more robust measure of causal inference.
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.
DERWENT learns paths for distant transfer learning via deep random walk.
problem Transfer learning between distant domains is challenging.
method DERWENT uses deep random walk to explicitly learn paths between source and target domains.
result DERWENT achieves state-of-the-art performance on benchmark datasets.
Improved sales forecasting for new products using transfer learning.
problem Insufficient training data for new products leads to inaccurate sales forecasts.
method Network-based Transfer Learning approach for deep neural networks.
result Deep neural networks' prediction accuracy for food sales forecasting can be effectively increased.
ATL learns from many streaming processes without labeled data.
problem Knowledge transfer across many streaming processes with covariate shift and drifts.
method Autonomous transfer learning with generative and discriminative phases, KL divergence optimization, and elastic network structure.
result Improved performance and faster training speed compared to existing methods.
The purpose of this paper is to introduce a concept of equivalence between machine learning algorithms. We define two notions of algorithmic equivalence, namely, weak and strong equivalence. These notions are of paramount importance for identifying when learning prop erties from one learning algorithm can be transferre…
DARec adapts rating patterns across domains without auxillary info.
problem Cross-domain recommendation challenges.
method Deep domain adaptation model (DARec) that transfers rating patterns.
result Best performance on public datasets.
HOUDINI learns algorithms across domains using program synthesis.
problem Lifelong learning of algorithmic tasks mixing perception and reasoning.
method Combining gradient descent with combinatorial search over programs.
result HOUDINI transfers high-level concepts more effectively than traditional methods.
Transfer learning improves HVAC fault detection with minimal labeled data.
problem Lack of labeled data for HVAC system faults.
method Bayesian classifier trained on large data, then transferred to new buildings with few normal operation samples.
result Few samples are sufficient to maintain classifier precision and recall.
Paper proposes DDA for better transfer learning performance.
problem Domain discrepancy between source and target distributions.
method Dynamic Distribution Adaptation (DDA) to evaluate and adapt distribution importance.
result DDA improves transfer learning performance on various tasks.
ONLAD Core detects anomalies in edge devices with fast learning and low power.
problem Anomaly detection in edge devices with concept drift and data transfers.
method Highly optimized neural network-based anomaly detection on edge devices.
result ONLAD Core achieves fast anomaly detection and low power consumption.
Anti-transfer learning prevents misleading representations for speech tasks.
problem Misleading representations learned from orthogonal tasks in speech processing.
method Penalizes similarity between activations of a network and another trained on an orthogonal task.
result Improves classification accuracy and invariance to the orthogonal task.
PCBMs turn any neural network into interpretable models without dense annotations.
problem Restrictive nature of CBMs and lack of dense concept annotations in training data.
method Introduce PCBMs that can turn any neural network into interpretable models without dense annotations.
result PCBMs can turn any neural network into interpretable models without dense annotations, improving interpretability and performance.
Time-warping improves RNN transfer learning for diverse time scales.
problem Transfer learning for RNNs with varying time scales.
method Time-warping rescales time in LSTM models for better transfer.
result Time-warping maintains accuracy in transferring RNNs between different time scales.
Model learns multiple tasks using visual and textual representations.
problem Training visual navigation agents for multiple tasks.
method Dual-Attention unit for task-invariant alignment of visual and textual representations.
result Model outperforms baselines on semantic goal navigation and embodied question answering.
New framework transfers latent knowledge from weak to strong models.
problem Aligning superhuman LLMs with human feedback.
method Transfer learning framework using refinement approach.
result Proves weak-to-strong generalization is possible.
Proposes a method to measure model parameter similarity for visual tasks.
problem Estimating relations between different visual tasks.
method LPS method using a second-order neural network to align model parameters and learn second-order similarity.
result Extensive experiments validate the effectiveness of the proposed method.
Existing approaches to combine both additive and multiplicative neural units either use a fixed assignment of operations or require discrete optimization to determine what function a neuron should perform. This leads either to an inefficient distribution of computational resources or an extensive increase in the comput…
SORTE optimizes systemic performance over individual rationality.
problem Systemic risk and optimal risk transfer.
method Endogenous determination of budget constraints through systemic utility maximization.
result Existence, uniqueness, and Pareto optimality of SORTE.
New approach extracts AI model representations for steering and monitoring.
problem Understanding and steering AI models' internal knowledge representation.
method Feature learning advances for scalable linear concept representations.
result Concept representations enable steering and monitoring of AI models.
Weyl and Cartan proposed different but related ways to handle infinitesimal geometry in the early 1920s.
problem How to apply transformation groups in differential geometry.
method Both used connections and parallel transfer, with Cartan aiming for a more general framework.
result They reached an agreement on handling Cartan's infinitesimal geometric structures by the 1930s.
Algorithm identifies and transfers unstable features to create robust classifiers.
problem Developing unbiased classifiers from input-label pairs alone.
method Contrast different data environments in source tasks to encode unstable features, then cluster target task data and minimize worst-case risk.
result Our method maintains robustness across synthetic and real-world environments.
BeST metric selects best source models for transfer learning.
problem Efficiently selecting top source models for a new target task.
method Quantization-level optimization procedure for similarity measure.
result BeST consistently identifies best source models for target tasks.
Neural painters learn to generate brushstrokes from a non-deterministic painting program.
problem Training an agent to generate realistic brushstrokes from a non-differentiable painting program.
method A differentiable neural painter model trained on brushstrokes, optimizing for human-like strokes and intrinsic style transfer.
result Direct optimization of brushstrokes can visualize ImageNet categories and generate ideal paintings.
Online learning rbfnet improves multi-horizon returns forecasts for financial time series.
problem Nonstationarity and concept drift in financial time series.
method Combines feature representation transfer with sequential optimisation.
result Online learning rbfnet outperforms random-walk and batch learners.
MSOL learns hierarchical policies for multitask tasks with soft options.
problem Training hierarchical policies for multiple tasks with stability and flexibility.
method MSOL uses separate variational posteriors for each task, regularized by a shared prior, to avoid instabilities and fine-tune options for new tasks.
result MSOL significantly outperforms hierarchical and flat transfer-learning baselines.
GNNs generalize CNNs for graph data, showing equivariance and stability.
problem Processing signals on graphs.
method Graph convolutional filters, nonlinearities, stacked layers.
result GNNs converge to graphon neural networks under graph convergence.
This paper proposes a method to train multiple neural networks with shared parameters using a reconstruction loss.
problem Training multiple neural networks for correlated tasks separately is inefficient.
method Introduces a novel approach with a reconstruction loss to encourage shared features across multiple tasks.
result The proposed method achieves efficient transfer learning with competitive performance.