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Found 16 papers, 3 papers with code
VoxGenesis: Unsupervised Discovery of Latent Speaker Manifold for Speech Synthesis
This forces the model to learn a speaker distribution disentangled from the semantic content.
Asymmetric Clean Segments-Guided Self-Supervised Learning for Robust Speaker Verification
Contrastive self-supervised learning (CSL) for speaker verification (SV) has drawn increasing interest recently due to its ability to exploit unlabeled data.
SegRNN: Segment Recurrent Neural Network for Long-Term Time Series Forecasting
To address these issues, we propose two novel strategies to reduce the number of iterations in RNNs for LTSF tasks: Segment-wise Iterations and Parallel Multi-step Forecasting (PMF).
Ranked #1 on
Time Series Forecasting
on Weather (720)
PETformer: Long-term Time Series Forecasting via Placeholder-enhanced Transformer
Recently, the superiority of Transformer for long-term time series forecasting (LTSF) tasks has been challenged, particularly since recent work has shown that simple models can outperform numerous Transformer-based approaches.
Self-supervised Neural Factor Analysis for Disentangling Utterance-level Speech Representations
Self-supervised learning (SSL) speech models such as wav2vec and HuBERT have demonstrated state-of-the-art performance on automatic speech recognition (ASR) and proved to be extremely useful in low label-resource settings.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Fusion of Global and Local Knowledge for Personalized Federated Learning
Personalized federated learning, as a variant of federated learning, trains customized models for clients using their heterogeneously distributed data.
Achieving Personalized Federated Learning with Sparse Local Models
To counter this issue, personalized FL (PFL) was proposed to produce dedicated local models for each individual user.
On Heterogeneously Distributed Data, Sparsity Matters
Federated learning (FL) is particularly vulnerable to heterogeneously distributed data, since a common global model in FL may not adapt to the heterogeneous data distribution of each user.
Variation-Incentive Loss Re-weighting for Regression Analysis on Biased Data
Both classification and regression tasks are susceptible to the biased distribution of training data.
Adaptive Processor Frequency Adjustment for Mobile Edge Computing with Intermittent Energy Supply
With astonishing speed, bandwidth, and scale, Mobile Edge Computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery.
FedProf: Selective Federated Learning with Representation Profiling
The results show that the selective behaviour of our algorithm leads to a significant reduction in the number of communication rounds and the amount of time (up to 2. 4x speedup) for the global model to converge and also provides accuracy gain.
Stochastic Client Selection for Federated Learning with Volatile Clients
Federated Learning (FL), arising as a privacy-preserving machine learning paradigm, has received notable attention from the public.
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee
The client selection policy is critical to an FL process in terms of training efficiency, the final model's quality as well as fairness.
Accelerating Federated Learning over Reliability-Agnostic Clients in Mobile Edge Computing Systems
In this paper, a multi-layer federated learning protocol called HybridFL is designed for the MEC architecture.
SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead
Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence.
Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality
In light of this, we have developed a prediction-driven, unsupervised anomaly detection scheme, which adopts a backbone model combining the decomposition and the inference of time series data.
