arXiv Machine Learning
Jul 9
LEMUR 2: Unlocking Neural Network Diversity for AI
arXiv:2607. 06839v1 Announce Type: new Abstract: Existing NAS benchmarks (e.
Learnware for CSI Feedback: Scene-specific Small Models Can Do Big
The paper proposes a Learnware-based framework for deploying scene‑specific CSI feedback models in 6G systems. A centralized AI data center maintains a catalog of pre‑trained models, each tagged with semantic and statistical specifications. Base stations retrieve the most relevant model using only statistical fingerprints, which reduces data privacy risks, lowers retrieval latency, and cuts fine‑tuning effort, achieving up to 57.7% performance gains over a general model.
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arXiv AI
Jun 19
PrototypeNAS: Rapid Design of Deep Neural Networks for Microcontroller Units
arXiv:2603. 15106v2 Announce Type: replace Abstract: Enabling efficient deep neural network (DNN) inference on edge devices with different hardware constraints is a challenging task that typically requires DNN architectures to be specialized for each device separately.
arXiv Machine Learning
Sep 10
FANS: Federated Adaptive Network Search Learning for Heterogeneous Devices
arXiv:2609.06106v1 Announce Type: new Abstract: Heterogeneous Federated Learning (HFL) aims to train models across devices with diverse resource budgets while preserving data privacy. Existing HFL me...
arXiv AI
Jul 14
The Universal Language of CSI:Unifying Wireless Sensing Across Devices and Environments
arXiv:2607. 09727v1 Announce Type: cross Abstract: WiFi sensing based on Channel State Information (CSI) promises ubiquitous, device-free perception, yet current research remains trapped in a Tower of Babel - fragmented into isolated silos where models are tailored to specific hardware dialects, fixed environments, and narrow tasks.
arXiv AI
Jun 24
Tuning without Peeking: Provable Generalization Bounds and Robust LLM Post-Training
arXiv:2507. 01752v4 Announce Type: replace-cross Abstract: Gradient-based optimization is the workhorse of deep learning, offering efficient and scalable training via backpropagation.
arXiv Machine Learning
Aug 27
ONNX-Net: Towards Universal Representations and Instant Performance Prediction for Neural Architectures
ONNX-Net introduces a universal representation for neural architectures using natural language descriptions, enabling instant performance prediction across diverse search spaces. The authors present ONNX-Bench, a benchmark of over 600k architecture–accuracy pairs compiled from open‑source NAS‑bench networks in ONNX format. Experiments demonstrate strong zero‑shot predictive performance with minimal pretraining, overcoming the limitations of cell‑based, graph‑encoded approaches.
arXiv Machine Learning
Jul 2
Prototype Language Models
arXiv:2607. 00510v1 Announce Type: new Abstract: Knowing which training examples drive outputs is fundamental to auditing, correcting, and understanding language models, yet for modern LLMs this remains expensive, approximate, and largely post-hoc.
arXiv AI
Jul 2
scDataset: Scalable Data Loading for Deep Learning on Large-Scale Single-Cell Omics
arXiv:2506. 01883v3 Announce Type: replace-cross Abstract: Training deep learning models on single-cell datasets with hundreds of millions of cells requires loading data from disk, as these datasets exceed available memory.
arXiv Machine Learning
Jul 9
What's on My Network? Using Large Language Models to Identify Real-World IoT Devices at Scale
arXiv:2510. 13817v2 Announce Type: replace Abstract: The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountability.
arXiv AI
Jun 2
MiCU: End-to-End Smart Home Command Understanding with Large Language Model
arXiv:2606. 01099v1 Announce Type: cross Abstract: Command understanding systems in smart home ecosystems can automate device control and substantially improve user experience.
arXiv AI
Jun 16
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
arXiv:2606. 15079v1 Announce Type: cross Abstract: Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve, and deploy.
arXiv AI
Aug 18
Learning to Unlearn: Machine Unlearning via Learning the Unlearning Behaviors
arXiv:2608. 16700v1 Announce Type: cross Abstract: Various machine unlearning techniques have been developed in response to privacy legislation requirements, enabling individuals to exercise their legal right to have their data $D_f$ removed from a machine learning model.