From the 1 of 43 linked papers with an AI index.
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S-CEReBrO: Breaking the Memory Barrier in Continuous EEG Monitoring
Glenn Anta Bucagu, Thorir Mar Ingolfsson, Yawei Li +1
The paper introduces S-CEReBrO, a streaming Transformer architecture that uses a windowed alternating attention mechanism to keep memory usage constant during continuous EEG monito…
RDKV: Rate-Distortion Bit Allocation for Joint Eviction and Quantization of the KV Cache
Junkai Zhang, Hang Guo, Luca Benini +1
Large language models (LLMs) have shown strong performance across diverse tasks, but their inference with long input contexts is bottlenecked by memory size and bandwidth. The Key-…
Revisiting Adaptive Rounding with Vectorized Reparameterization for LLM Quantization
Yuli Zhou, Qingxuan Chen, Luca Benini +2
Adaptive Rounding has emerged as an alternative to round-to-nearest (RTN) for post-training quantization by enabling cross-element error cancellation. Yet, dense and element-wise r…
LUNA: Efficient and Topology-Agnostic Foundation Model for EEG Signal Analysis
Berkay Döner, Thorir Mar Ingolfsson, Luca Benini +1
Electroencephalography (EEG) offers a non-invasive lens into human brain activity, but building large-scale models is hampered by topological heterogeneity: each public EEG data de…
PhysioWave: A Multi-Scale Wavelet-Transformer for Physiological Signal Representation
Yanlong Chen, Mattia Orlandi, Pierangelo Maria Rapa +3
Physiological signals are often corrupted by motion artifacts, baseline drift, and other low-SNR disturbances, which pose significant challenges for analysis. Additionally, these s…
CEReBrO: Compact Encoder for Representations of Brain Oscillations Using Efficient Alternating Attention
Alexandru Dimofte, Glenn Anta Bucagu, Thorir Mar Ingolfsson +4
Electroencephalograph (EEG) is a crucial tool for studying brain activity. Recently, self-supervised learning methods leveraging large unlabeled datasets have emerged as a potentia…