arXiv Machine Learning

Leveraging ECRAM for Edge Continual Learning

arXiv:2607. 19661v1 Announce Type: cross Abstract: Several edge computing platforms, such as autonomous vehicles and smart sensing devices, need to adapt to dynamic environments in real time by learning from new data in the field.

arXiv Machine Learning

Aug 12

GLAM: Efficient Continual Learning at Scale via Grouped LoRA Adapter Merging

arXiv:2509. 13211v4 Announce Type: replace Abstract: The ability to learn continuously over time remains a major challenge for modern machine learning systems, even in the era of Foundation Models.

By Irene Testa, Luigi Quarantiello, Eric Nuertey Coleman, Samrat Mukherjee, Julio Hurtado, Vincenzo Lomonaco

fine-tuning benchmarks

arXiv Machine Learning

Jul 30

Gated Adaptation for Continual Learning in Human Activity Recognition

arXiv:2603. 10046v2 Announce Type: replace Abstract: Wearable sensors in Internet of Things (IoT) ecosystems increasingly support applications such as remote health monitoring, elderly care, and smart home automation, all of which rely on robust human activity recognition (HAR).

By Reza Rahimi Azghan, Gautham Krishna Gudur, Mohit Malu, Edison Thomaz, Giulia Pedrielli, Pavan Turaga, Hassan Ghasemzadeh

arXiv Machine Learning

Aug 6

SHIELD: A Segmented Hierarchical Memory Architecture for Energy-Efficient LLM Inference on Edge NPUs

arXiv:2604. 07396v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) inference on edge Neural Processing Units (NPUs) is fundamentally constrained by limited on-chip memory capacity.

By Jintao Zhang, Xuanyao Fong

llms

arXiv AI

Aug 3

Versatile On-device Adaptation at the Edge by Unifying Few-shot, Zero-shot, Continual, and In-context Learning

arXiv:2607. 29353v1 Announce Type: cross Abstract: With the ever-increasing pervasiveness of smart edge devices, the demand is growing for applications that can be tailored to users (e.

By Douwe den Blanken, Martin Lefebvre, Charlotte Frenkel

llms benchmarks safety

arXiv AI

Jun 10

HydraCIL: Decoupled Class-Incremental Learning through Prototype-Guided Multi-Head Classifiers

arXiv:2606. 09960v1 Announce Type: cross Abstract: We present HydraCIL, a decoupled continual learning model based on prototype-guided multi-head classifiers, targeting sustainable deployment in embedded and resource-constrained environments.

By Daniel Vila-Cruz, Laura Mor\'an-Fern\'andez, Ver\'onica Bol\'on-Canedo

robotics benchmarks

arXiv AI

Jun 17

Position: Modular Memory is the Key to Continual Learning Agents

arXiv:2603. 01761v2 Announce Type: replace-cross Abstract: Foundation models have transformed machine learning through large-scale pretraining and increased test-time compute.

By Vaggelis Dorovatas, Malte Schwerin, Andrew D. Bagdanov, Lucas Caccia, Antonio Carta, Laurent Charlin, Barbara Hammer, Tyler L. Hayes, Timm Hess, Christopher Kanan, Dhireesha Kudithipudi, Xialei Liu, Vincenzo Lomonaco, Jorge Mendez-Mendez, Darshan Patil, Ameya Prabhu, Elisa Ricci, Tinne Tuytelaars, Gido M. van de Ven, Liyuan Wang, Joost van de Weijer, Jonghyun Choi, Martin Mundt, Rahaf Aljundi

llms agents

arXiv Machine Learning

Jul 21

Chameleon: A Multiplier-Free Temporal Convolutional Network Accelerator for End-to-End Few-Shot and Continual Learning from Sequential Data

arXiv:2505. 24852v3 Announce Type: replace-cross Abstract: On-device learning at the edge enables low-latency, private personalization with improved long-term robustness and reduced maintenance costs.

By Douwe den Blanken, Charlotte Frenkel

benchmarks

arXiv Machine Learning

Jun 16

An RRAM-based Hardware Implementation of a Radial Basis Function Neuron for Edge Classifiers

arXiv:2606. 14739v1 Announce Type: cross Abstract: The deployment of modern machine learning (ML) solutions on resource-constrained edge devices highlights implementation challenges.

By Georgios Papandroulidakis, Shady Agwa, Themis Prodromakis

agents safety

arXiv Machine Learning

Jul 28

Gradient-Free Continual Learning

arXiv:2504. 01219v2 Announce Type: replace Abstract: Neural networks are notorious for forgetting old skills when taught new ones - a problem known as catastrophic forgetting.

By Grzegorz Rype\'s\'c

benchmarks safety

arXiv AI

Jun 10

FOGO: Forgetting-aware Orthogonalization Optimizer

arXiv:2606. 10406v1 Announce Type: cross Abstract: We argue that forgetting is not confined to continual learning but is a general optimization phenomenon: during standard training, dominant mini-batch gradients suppress rare but useful update directions, causing short-term forgetting at every step.

By Toan Nguyen, Yang Liu, Trung Le, Celso de Melo, Flora D. Salim

llms fine-tuning

arXiv Machine Learning

Jul 21

Empowering On-Device Model Adaptation with an Edge AI Inference Accelerator

arXiv:2607. 18101v1 Announce Type: new Abstract: On-device model adaptation is essential to enable lifelong personalization on resource-constrained hardware, but compute, power, and memory limitations of such devices make end-to-end backpropagation impractical for modern deep neural networks.

By Mateusz Piechocki, Alessandro Capotondi, Marek Kraft

fine-tuning efficiency

Hugging Face Trending Papers

Jun 9

FOGO: Forgetting-aware Orthogonalization Optimizer

We argue that forgetting is not confined to continual learning but is a general optimization phenomenon: during standard training, dominant mini-batch gradients suppress rare but useful update directions, causing short-term forgetting at every step. When such knowledge is never revisited, these losses compound into long-term forgetting-the classical failure mode of continual learning.

llms fine-tuning

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