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Found 18 papers, 13 papers with code
ColA: Collaborative Adaptation with Gradient Learning
We introduce Collaborative Adaptation (ColA) with Gradient Learning (GL), a parameter-free, model-agnostic fine-tuning approach that decouples the computation of the gradient of hidden representations and parameters.
Large Deviation Analysis of Score-based Hypothesis Testing
We analyze the performance of this score-based hypothesis testing procedure and derive upper bounds on the probabilities of its Type I and II errors.
Semi-Supervised Federated Learning for Keyword Spotting
Keyword Spotting (KWS) is a critical aspect of audio-based applications on mobile devices and virtual assistants.
Pruning Deep Neural Networks from a Sparsity Perspective
Our extensive experiments corroborate the hypothesis that for a generic pruning procedure, PQI decreases first when a large model is being effectively regularized and then increases when its compressibility reaches a limit that appears to correspond to the beginning of underfitting.
Quickest Change Detection for Unnormalized Statistical Models
This paper develops a new variant of the classical Cumulative Sum (CUSUM) algorithm for the quickest change detection.
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders
Recommender Systems (RSs) have become increasingly important in many application domains, such as digital marketing.
A Physics-Informed Vector Quantized Autoencoder for Data Compression of Turbulent Flow
In this study, we apply a physics-informed Deep Learning technique based on vector quantization to generate a discrete, low-dimensional representation of data from simulations of three-dimensional turbulent flows.
Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models
We use a data-driven approach to model a three-dimensional turbulent flow using cutting-edge Deep Learning techniques.
Decentralized Multi-Target Cross-Domain Recommendation for Multi-Organization Collaborations
Recommender Systems (RSs) are operated locally by different organizations in many realistic scenarios.
GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations
However, the underlying organizations may have little interest in sharing their local data, models, and objective functions.
SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training
Most existing results on Federated Learning (FL) assume the clients have ground-truth labels.
On Statistical Efficiency in Learning
We propose a generalized notion of Takeuchi's information criterion and prove that the proposed method can asymptotically achieve the optimal out-sample prediction loss under reasonable assumptions.
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
In this work, we propose a new federated learning framework named HeteroFL to address heterogeneous clients equipped with very different computation and communication capabilities.
Multimodal Controller for Generative Models
In the absence of the controllers, our model reduces to non-conditional generative models.
Deep Clustering of Compressed Variational Embeddings
Motivated by the ever-increasing demands for limited communication bandwidth and low-power consumption, we propose a new methodology, named joint Variational Autoencoders with Bernoulli mixture models (VAB), for performing clustering in the compressed data domain.
Speech Emotion Recognition with Dual-Sequence LSTM Architecture
Speech Emotion Recognition (SER) has emerged as a critical component of the next generation human-machine interfacing technologies.
Restricted Recurrent Neural Networks
Recurrent Neural Network (RNN) and its variations such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), have become standard building blocks for learning online data of sequential nature in many research areas, including natural language processing and speech data analysis.
DRASIC: Distributed Recurrent Autoencoder for Scalable Image Compression
We propose a new architecture for distributed image compression from a group of distributed data sources.
