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Found 21 papers, 10 papers with code
The Llama 3 Herd of Models
This paper presents a new set of foundation models, called Llama 3.
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Balancing Continual Learning and Fine-tuning for Human Activity Recognition
These schemes re-purpose contrastive learning for knowledge retention and, Kaizen combines that with self-training in a unified scheme that can leverage unlabelled and labelled data for continual learning.
CroSSL: Cross-modal Self-Supervised Learning for Time-series through Latent Masking
Limited availability of labeled data for machine learning on multimodal time-series extensively hampers progress in the field.
Kaizen: Practical Self-supervised Continual Learning with Continual Fine-tuning
Kaizen is able to balance the trade-off between knowledge retention and learning from new data with an end-to-end model, paving the way for practical deployment of continual learning systems.
Enhancing Efficiency in Multidevice Federated Learning through Data Selection
Federated learning (FL) in multidevice environments creates new opportunities to learn from a vast and diverse amount of private data.
Orchestra: Unsupervised Federated Learning via Globally Consistent Clustering
Federated learning is generally used in tasks where labels are readily available (e. g., next word prediction).
FLAME: Federated Learning Across Multi-device Environments
Federated Learning (FL) enables distributed training of machine learning models while keeping personal data on user devices private.
ColloSSL: Collaborative Self-Supervised Learning for Human Activity Recognition
In this paper, we extend this line of research and present a novel technique called Collaborative Self-Supervised Learning (ColloSSL) which leverages unlabeled data collected from multiple devices worn by a user to learn high-quality features of the data.
Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflows
Billions of distributed, heterogeneous and resource constrained IoT devices deploy on-device machine learning (ML) for private, fast and offline inference on personal data.
SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devices
We present SensiX++ - a multi-tenant runtime for adaptive model execution with integrated MLOps on edge devices, e. g., a camera, a microphone, or IoT sensors.
On-device Federated Learning with Flower
Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store data in the cloud.
A first look into the carbon footprint of federated learning
Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers.
Scaling Unsupervised Domain Adaptation through Optimal Collaborator Selection and Lazy Discriminator Synchronization
Breakthroughs in unsupervised domain adaptation (uDA) have opened up the possibility of adapting models from a label-rich source domain to unlabeled target domains.
SensiX: A Platform for Collaborative Machine Learning on the Edge
The emergence of multiple sensory devices on or near a human body is uncovering new dynamics of extreme edge computing.
Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data
Protective behavior exhibited by people with chronic pain (CP) during physical activities is the key to understanding their physical and emotional states.
Can Federated Learning Save The Planet?
Then, we compare the carbon footprint of FL to traditional centralized learning.
Libri-Adapt: A New Speech Dataset for Unsupervised Domain Adaptation
This paper introduces a new dataset, Libri-Adapt, to support unsupervised domain adaptation research on speech recognition models.
Flower: A Friendly Federated Learning Research Framework
Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud.
Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems
A major challenge in building systems that combine audio models with commodity microphones is to guarantee their accuracy and robustness in the real-world.
Multi-Step Decentralized Domain Adaptation
Despite the recent breakthroughs in unsupervised domain adaptation (uDA), no prior work has studied the challenges of applying these methods in practical machine learning scenarios.
Chronic-Pain Protective Behavior Detection with Deep Learning
In chronic pain rehabilitation, physiotherapists adapt physical activity to patients' performance based on their expression of protective behavior, gradually exposing them to feared but harmless and essential everyday activities.
