Publications related to Artificial neural networks

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Pengbo Yu, Flavio Ponzina

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

2024

Meta-learning to address diverse Earth observation problems across resolutions

Devis Tuia, Benjamin Alexander Kellenberger, Marc Conrad Russwurm

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

London2024

KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch

David Atienza Alonso, José Angel Miranda Calero, Jonathan Dan, Christodoulos Kechris

Accurate extraction of heart rate from photoplethysmography (PPG) signals remains challenging due to motion artifacts and signal degradation. Although deep learning methods trained as a data-driven inference problem offer promising solutions, they often un ...

2024

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

David Atienza Alonso, Giovanni Ansaloni, José Angel Miranda Calero, Jonathan Dan, Amirhossein Shahbazinia, Flavio Ponzina

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

2024

Data-Driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Wenlong Liao, Qi Liu, Zhe Yang

Reactive power optimization of distribution networks is traditionally addressed by physical model based methods, which often lead to locally optimal solutions and require heavy online inference time consumption. To improve the quality of the solution and r ...

State Grid Electric Power Research Inst2024

Social Opinion Formation and Decision Making Under Communication Trends

Ali H. Sayed, Mert Kayaalp, Virginia Bordignon

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

Piscataway2024

Ethological computational psychiatry: Challenges and opportunities

Mackenzie Mathis

Studying the intricacies of individual subjects' moods and cognitive processing over extended periods of time presents a formidable challenge in medicine. While much of systems neuroscience appropriately focuses on the link between neural circuit functions ...

Current Biology Ltd2024

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

David Atienza Alonso, Giovanni Ansaloni, Alireza Amirshahi

The increasing complexity of transformer models in artificial intelligence expands their computational costs, memory usage, and energy consumption. Hardware acceleration tackles the ensuing challenges by designing processors and accelerators tailored for t ...

2024

Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language Learning

Vinitra Swamy, Jibril Albachir Frej, Paola Mejia Domenzain, Luca Zunino, Tommaso Martorella, Elena Grazia Gado

Intelligent Tutoring Systems (ITS) enhance personalized learning by predicting student answers to provide immediate and customized instruction. However, recent research has primarily focused on the correctness of the answer rather than the student's perfor ...

2024

Efficient Continual Finite-Sum Minimization

Volkan Cevher, Efstratios Panteleimon Skoulakis, Leello Tadesse Dadi

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

2024

Artificial neural networks

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DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Meta-learning to address diverse Earth observation problems across resolutions

KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Data-Driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Social Opinion Formation and Decision Making Under Communication Trends

Ethological computational psychiatry: Challenges and opportunities

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language Learning

Efficient Continual Finite-Sum Minimization

Meta-learning to address diverse Earth observation problems across resolutions

KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Accelerator-driven Data Arrangement to Minimize Transformers Run-time on Multi-core Architectures

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Social Opinion Formation and Decision Making Under Communication Trends

Ethological computational psychiatry: Challenges and opportunities

Student Answer Forecasting: Transformer-Driven Answer Choice Prediction for Language Learning

Data-Driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

Efficient Continual Finite-Sum Minimization