Publications associées à Non-Structured DNN Weight Pruning--Is It Beneficial in Any Platform?

Publications associées (22)

Stop Wasting your Cache! Bringing Machine Learning into Cache Computing

The rapid evolution of Machine Learning (ML) workloads, particularly Deep Neural Networks (DNNs) and Transformer-based models, has intensified demands on computing architectures, highlighting the limitations of traditional von Neumann systems due to the me ...

ACM2025

Global Group Fairness in Federated Learning via Function Tracking

Daniel Kuhn, Yves Rychener, Yifan Hu

We investigate group fairness regularizers in federated learning, aiming to train a globally fair model in a distributed setting. Ensuring global fairness in distributed training presents unique challenges, as fairness regularizers typically involve probab ...

2025

Behaviorally Relevant Information Content in Interneuron Activity

Mahsa Barzegarkeshteli

The nematode Caenorhabditis elegans offers a uniquely tractable model for studying the relationships between neuronal activity, connectivity, and behavior. Despite its simplicity, key questions remain about how information flows through its nervous system ...

EPFL2025

Advancing Distributed AC Optimal Power Flow for Integrated Transmission-Distribution Systems

Colin Neil Jones, Yuning Jiang

This paper introduces a distributed operational solution for integrated transmission-distribution (ITD) system management. A fundamental challenge in designing distributed approaches for AC optimal power flow (OPF) problems in ITD systems is the nonconvexi ...

IEEE COMPUTER SOC2025

Speech Data Selection for Efficient ASR Fine-Tuning using Domain Classifier and Pseudo-Label Filtering

Juan Pablo Zuluaga Gomez

In real-world speech data processing, the scarcity of annotated data and the abundance of unlabelled speech data present a significant challenge. To address this, we propose an efficient data selection pipeline for fine-tuning ASR models by generating pseu ...

Institute of Electrical and Electronics Engineers Inc.2025

A multi-modal nocturnal radiative cooling and solar-assisted heating system with model predictive control

Colin Neil Jones, Manuel Pascal Koch

Heating and cooling of buildings account for around 15% of all global energy consumption. In light of the ongoing energy transition, we aim to improve the efficiency of both in a European single-family house by using photovoltaic-thermal collectors with a ...

2025

Engineering of stable and functional metalloproteins using evolutionary computation, deep learning and simulation

Simon Leonard Dürr

Metal ions are indispensable cofactors for many proteins and play crucial roles in numerous biological processes, yet the computational design of metalloproteins and the optimization of metal coordination sites poses significant challenges due to the comp ...

EPFL2025

The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents

Lenka Zdeborová, Florent Gérard Krzakala, Yatin Dandi, Emanuele Troiani, Luca Pesce, Luca Arnaboldi

We investigate the training dynamics of two-layer neural networks when learning multi-index target functions. We focus on multi-pass gradient descent (GD) that reuses the batches multiple times and show that it significantly changes the conclusion about wh ...

JMLR2024

Gaussian universality of perceptrons with random labels

Lenka Zdeborová, Florent Gérard Krzakala, Bruno Loureiro, Ludovic Théo Stephan, Federica Gerace

While classical in many theoretical settings-and in particular in statistical physics-inspired works-the assumption of Gaussian i.i.d. input data is often perceived as a strong limitation in the context of statistics and machine learning. In this study, we ...

Amer Physical Soc2024

In-Memory Sensing and Computing for Cancer Diagnostics: A Perspective Paper

Sandro Carrara, Ata Jedari Golparvar, Gian Luca Barbruni, Junrui Chen

During the past two decades, a number of two-terminal switching devices have been demonstrated in the literature. They typically exhibit hysteric behavior in the current-to-voltage characteristics. These devices have often been also referred to as memristi ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Brief Announcement: A Case for Byzantine Machine Learning

Rachid Guerraoui, Nirupam Gupta, Rafaël Benjamin Pinot, Sadegh Farhadkhani

The success of machine learning (ML) has been intimately linked with the availability of large amounts of data, typically collected from heterogeneous sources and processed on vast networks of computing devices (also called workers). Beyond accuracy, the u ...

ACM2024

LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition

Yung-Cheng Chiang

The Vision Transformer (ViT) excels in accuracy when handling high-resolution images, yet it confronts the challenge of significant spatial redundancy, leading to increased computational and memory requirements. To address this, we present the Localization ...

Association for the Advancement of Artificial Intelligence2024

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

Euclid: Testing photometric selection of emission-line galaxy targets?

Frédéric Courbin, Georges Meylan

Multi-object spectroscopic galaxy surveys typically make use of photometric and colour criteria to select their targets. That is not the case of Euclid, which will use the NISP slitless spectrograph to record spectra for every source over its field of view ...

2024

Measuring Linkability of Protected Biometric Templates Using Maximal Leakage

Hatef Otroshi Shahreza, Yanina Yurina Shkel

As the applications of biometric recognition systems are increasing rapidly, there is a growing need to secure the sensitive data used within these systems. Considering privacy challenges in such systems, different biometric template protection (BTP) schem ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis

Olga Fink, Florent Evariste Forest

Intelligent Fault Diagnosis (IFD) based on deep learning has proven to be an effective and flexible solution, attracting extensive research. Deep neural networks can learn rich representations from vast amounts of representative labeled data for various ap ...

2023

Discovering Influencers in Opinion Formation Over Social Graphs

Ali H. Sayed, Mert Kayaalp, Valentina Shumovskaia

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observation ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

Marilyne Andersen, Sabine Süsstrunk, Caroline Karmann, Kynthia Chamilothori, Seungryong Kim, Bahar Aydemir

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the pe ...

2023

Robust Analog Multisensory Array System for Lossy Capacitive Sensors Over Long Distances

Sandro Carrara, Amar Kapic

A robust capacitive multisensory readout system is presented in this article. The system consists of a few thousand lossy capacitive-based sensors that can be modeled as a parallel connection of sensor capacitor and undesirable leakage resistance. The real ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

An Image-Based Computational Fluid Dynamics Study of Mitral Regurgitation in Presence of Prolapse

Alfio Quarteroni

PurposeIn this work we performed an imaged-based computational study of the systolic fluid dynamics in presence of mitral valve regurgitation (MVR). In particular, we compared healthy and different regurgitant scenarios with the aim of quantifying differen ...

SPRINGER2023