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Found 26 papers, 7 papers with code
Personalized Federated Learning with Adaptive Batchnorm for Healthcare
There is a growing interest in applying machine learning techniques to healthcare.
FLUTE: A Scalable, Extensible Framework for High-Performance Federated Learning Simulations
We compare the platform with other state-of-the-art platforms and describe available features of FLUTE for experimentation in core areas of active research, such as optimization, privacy, and scalability.
GPT-FL: Generative Pre-trained Model-Assisted Federated Learning
Through comprehensive ablation analysis, we discover that the downstream model generated by synthetic data plays a crucial role in controlling the direction of gradient diversity during FL training, which enhances convergence speed and contributes to the notable accuracy boost observed with GPT-FL.
Distribution inference risks: Identifying and mitigating sources of leakage
We identify three sources of leakage: (1) memorizing specific information about the $\mathbb{E}[Y|X]$ (expected label given the feature values) of interest to the adversary, (2) wrong inductive bias of the model, and (3) finiteness of the training data.
MARS: Meaning-Aware Response Scoring for Uncertainty Estimation in Generative LLMs
In this work, we propose Meaning-Aware Response Scoring (MARS) as an alternative to length-normalized scoring for UE methods.
Counterfactual Augmentation for Multimodal Learning Under Presentation Bias
In real-world machine learning systems, labels are often derived from user behaviors that the system wishes to encourage.
Progressive Neural Networks for Transfer Learning in Emotion Recognition
Many paralinguistic tasks are closely related and thus representations learned in one domain can be leveraged for another.
Improving End-of-turn Detection in Spoken Dialogues by Detecting Speaker Intentions as a Secondary Task
This work focuses on the use of acoustic cues for modeling turn-taking in dyadic spoken dialogues.
Capturing Long-term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition
The goal of continuous emotion recognition is to assign an emotion value to every frame in a sequence of acoustic features.
English Conversational Telephone Speech Recognition by Humans and Machines
This then raises two issues - what IS human performance, and how far down can we still drive speech recognition error rates?
Ranked #3 on
Speech Recognition
on Switchboard + Hub500
Low-Latency Speaker-Independent Continuous Speech Separation
Speaker independent continuous speech separation (SI-CSS) is a task of converting a continuous audio stream, which may contain overlapping voices of unknown speakers, into a fixed number of continuous signals each of which contains no overlapping speech segment.
Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings
Involvement hot spots have been proposed as a useful concept for meeting analysis and studied off and on for over 15 years.
Advances in Online Audio-Visual Meeting Transcription
This increases marginally to 1. 6% when 50% of the attendees are unknown to the system.
Federated Transfer Learning with Dynamic Gradient Aggregation
The target scenario is Acoustic Model training based on this platform.
A Review of Speaker Diarization: Recent Advances with Deep Learning
Speaker diarization is a task to label audio or video recordings with classes that correspond to speaker identity, or in short, a task to identify "who spoke when".
Dynamic Gradient Aggregation for Federated Domain Adaptation
The proposed scheme is based on a weighted gradient aggregation using two-step optimization to offer a flexible training pipeline.
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis
As such, these models are often unable to produce personalized responses for individual users, based on their data.
Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal
Federated Learning is a fast growing area of ML where the training datasets are extremely distributed, all while dynamically changing over time.
Sequence-level self-learning with multiple hypotheses
For untranscribed speech data, the hypothesis from an ASR system must be used as a label.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
In this work, we propose a novel ensemble knowledge transfer method named Fed-ET in which small models (different in architecture) are trained on clients, and used to train a larger model at the server.
Invariant Aggregator for Defending against Federated Backdoor Attacks
Federated learning enables training high-utility models across several clients without directly sharing their private data.
Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout
Asynchronous learning protocols have regained attention lately, especially in the Federated Learning (FL) setup, where slower clients can severely impede the learning process.
Federated Multilingual Models for Medical Transcript Analysis
Federated Learning (FL) is a novel machine learning approach that allows the model trainer to access more data samples, by training the model across multiple decentralized data sources, while data access constraints are in place.
FedJETs: Efficient Just-In-Time Personalization with Federated Mixture of Experts
Our gating function harnesses the knowledge of a pretrained model common expert to enhance its routing decisions on-the-fly.
Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels
For both cross-device and cross-silo settings, we show that FedLabel outperforms other semi-supervised FL baselines by $8$-$24\%$, and even outperforms standard fully supervised FL baselines ($100\%$ labeled data) with only $5$-$20\%$ of labeled data.
