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.
Bayesian neural networks improve knowledge sharing across networks.
Enhances generative model accuracy through knowledge transfer.
Enhancing spectral embedding for low-dimensional embeddings in rare disease cohorts
Boosts share routing for multi-task learning with flexible sparse connections.
Bayesian model transfers knowledge across different engineering fleets.
We propose Deep Asymmetric Multitask Feature Learning (Deep-AMTFL) which can learn deep representations shared across multiple tasks while effectively preventing negative transfer that may happen in the feature sharing process. Specifically, we introduce an asymmetric autoencoder term that allows reliable predictors fo…
GDA-HIN adapts across heterogeneous networks by aligning shared and private node types.
This paper proposes a new VoI analysis framework for complex decision problems.
Unified Bayesian framework for clustered federated learning improves model performance.
Intelligent behaviour in the real-world requires the ability to acquire new knowledge from an ongoing sequence of experiences while preserving and reusing past knowledge. We propose a novel algorithm for unsupervised representation learning from piece-wise stationary visual data: Variational Autoencoder with Shared Emb…
This paper introduces self-paced task selection to multitask learning, where instances from more closely related tasks are selected in a progression of easier-to-harder tasks, to emulate an effective human education strategy, but applied to multitask machine learning. We develop the mathematical foundation for the appr…
Purveyors of malicious network attacks continue to increase the complexity and the sophistication of their techniques, and their ability to evade detection continues to improve as well. Hence, intrusion detection systems must also evolve to meet these increasingly challenging threats. Machine learning is often used to …
This paper proposes a novel learning method for multi-task applications. Multi-task neural networks can learn to transfer knowledge across different tasks by using parameter sharing. However, sharing parameters between unrelated tasks can hurt performance. To address this issue, we propose a framework to learn fine-gra…
The paper analyzes how shared priors affect Bayesian data fusion performance.
SPEQ improves quantized neural networks by stochastic precision sharing and cosine similarity loss.
Unsupervised domain adaptation (UDA) aims to learn the unlabeled target domain by transferring the knowledge of the labeled source domain. To date, most of the existing works focus on the scenario of one source domain and one target domain (1S1T), and just a few works concern the scenario of multiple source domains and…
Financial economic models often assume that investors know (or agree on) the fundamental value of the shares of the firm, easing the passage from the individual to the collective dimension of the financial system generated by the Share Exchange over time. Our model relaxes that heroic assumption of one unique "true val…
Transfer learning aims to faciliate learning tasks in a label-scarce target domain by leveraging knowledge from a related source domain with plenty of labeled data. Often times we may have multiple domains with little or no labeled data as targets waiting to be solved. Most existing efforts tackle target domains separa…
PerPCA separates unique and shared features from heterogeneous data.
Sharing knowledge between tasks is vital for efficient learning in a multi-task setting. However, most research so far has focused on the easier case where knowledge transfer is not harmful, i.e., where knowledge from one task cannot negatively impact the performance on another task. In contrast, we present an approach…
Cronus securely transfers model parameters to protect federated learning from poisoning attacks.
This research shows how to learn shared representations from unpaired data.
Deep learning models for semantic segmentation of images require large amounts of data. In the medical imaging domain, acquiring sufficient data is a significant challenge. Labeling medical image data requires expert knowledge. Collaboration between institutions could address this challenge, but sharing medical data to…
Recently there has been an increasing interest in the multivariate Gaussian process (MGP) which extends the Gaussian process (GP) to deal with multiple outputs. One approach to construct the MGP and account for non-trivial commonalities amongst outputs employs a convolution process (CP). The CP is based on the idea of …
In order to learn quickly with few samples, meta-learning utilizes prior knowledge learned from previous tasks. However, a critical challenge in meta-learning is task uncertainty and heterogeneity, which can not be handled via globally sharing knowledge among tasks. In this paper, based on gradient-based meta-learning,…
Proposes TFCL to mitigate negative transfer in MTL by collaborating across features and tasks.
PIMA autoencoders discover shared features in multimodal scientific data.
The incorporation of prior knowledge into learning is essential in achieving good performance based on small noisy samples. Such knowledge is often incorporated through the availability of related data arising from domains and tasks similar to the one of current interest. Ideally one would like to allow both the data f…
Graph-based RKD improves knowledge distillation for resource-constrained systems.
A method learns matrix factorization from diverse matrices and applies the knowledge to unseen matrices.
This paper improves ASR performance by aligning frames more accurately.
Multi-view subspace learning (MSL) aims to find a low-dimensional subspace of the data obtained from multiple views. Different from single view case, MSL should take both common and specific knowledge among different views into consideration. To enhance the robustness of model, the complexity, non-consistency and simil…
Paper proposes sharing models instead of data for smart health predictions.
Hierarchical Modular Reinforcement Learning (HMRL), consists of 2 layered learning where Profit Sharing works to plan a prey position in the higher layer and Q-learning method trains the state-actions to the target in the lower layer. In this paper, we expanded HMRL to multi-target problem to take the distance between …
ARML learns task relations to improve meta-learning efficiency.
KEEN Universe provides reproducible and transferable knowledge graph embeddings.
Pea-KD improves BERT student models by 4.4% on average in GLUE tasks.
PoDiRe learns long-term rewards in multi-task recommendations.
Novel framework for data sharing and coordinated exploration in concurrent RL with non-identical environments.
In real-world applications, it is often expensive and time-consuming to obtain labeled examples. In such cases, knowledge transfer from related domains, where labels are abundant, could greatly reduce the need for extensive labeling efforts. In this scenario, transfer learning comes in hand. In this paper, we propose D…
A framework for multilayer networks predicts links without shared structures.
PFedRL-Rep learns shared and personalized policies for heterogeneous environments.
Unified scoring model improves efficiency and performance across multiple tasks.
Role-wise data augmentation improves knowledge distillation effectiveness.
Deep RL policies share adversarial features across different MDPs.
AlphaNet improves supernets training with alpha-divergence.
Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems. In this paper, we consider knowledge graphs as the source of side info…
With the large volume of new information created every day, determining the validity of information in a knowledge graph and filling in its missing parts are crucial tasks for many researchers and practitioners. To address this challenge, a number of knowledge graph completion methods have been developed using low-dime…