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Found 7 papers, 4 papers with code
Advancing Multi-Modal Sensing Through Expandable Modality Alignment
To tackle this challenge, we introduce the Babel framework, encompassing the neural network architecture, data preparation and processing, as well as the training strategies.
NeedleBench: Can LLMs Do Retrieval and Reasoning in 1 Million Context Window?
In evaluating the long-context capabilities of large language models (LLMs), identifying content relevant to a user's query from original long documents is a crucial prerequisite for any LLM to answer questions based on long text.
Penetrative AI: Making LLMs Comprehend the Physical World
Recent developments in Large Language Models (LLMs) have demonstrated their remarkable capabilities across a range of tasks.
PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud Inference
This paper presents the design of a system called PriMask, in which the mobile device uses a secret small-scale neural network called MaskNet to mask the data before transmission.
Enhancing Deep Learning Performance of Massive MIMO CSI Feedback
In this paper, we propose a jigsaw puzzles aided training strategy (JPTS) to enhance the deep learning-based Massive MIMO CSI feedback approaches by maximizing mutual information between the original CSI and the compressed CSI.
CLNet: Complex Input Lightweight Neural Network designed for Massive MIMO CSI Feedback
Numerous deep learning for massive MIMO CSI feedback approaches have demonstrated their efficiency and potential.
On-the-fly Closed-loop Autonomous Materials Discovery via Bayesian Active Learning
Active learning - the field of machine learning (ML) dedicated to optimal experiment design, has played a part in science as far back as the 18th century when Laplace used it to guide his discovery of celestial mechanics [1].
