Publications associées à From Preprocessing to Evaluation: The Role of External Knowledge in Advancing Language Models and Machine Translation

Publications associées (19)

Programming Light Propagation for Efficient Artificial Intelligence

Contemporary artificial intelligence (AI) models' exponentially growing complexity translates to their expanding skill sets. They can create language or image content mimicking human creativity and understanding, becoming indispensable tools in education, ...

EPFL2025

Scaling the Modalities in Multimodal Foundation Models

Oguzhan Fatih Kar

Having a single neural network to handle a wide and varied range of tasks and modalities has been a long-standing goal. Such a model brings notable advantages, such as test-time computational efficiency, modality fusion, and reduced model size. Our goal in ...

EPFL2025

Towards Integrated Processing of Physiological Signals and Speech

Zohreh Mostaani

Research in speech processing has largely focused on source-system modeling, where vocal fold vibrations serve as the source and the vocal cavity's articulations as the system. Nonetheless, speech production includes a complex combination of physiological ...

EPFL2025

LIU-NET: lightweight Inception U-Net for efficient brain tumor segmentation from multimodal 3D MRI images

Zaffar Ahmed Shaikh

Segmenting brain tumors is a critical task in medical imaging that relies on advanced deep-learning methods. However, effectively handling complex tumor regions requires more comprehensive and advanced strategies to overcome challenges such as computationa ...

2025

Inherent Structural Descriptors Via Machine Learning

Antonio Tinti

Finding proper collective variables for complex systems and processes is one of the most challenging tasks in simulations, which limits the interpretation of experimental and simulated data and the application of enhanced sampling techniques. Here, we prop ...

IOP Publishing Ltd2025

Bayes-optimal learning of deep random networks of extensive-width*

Lenka Zdeborová, Florent Gérard Krzakala, Hugo Chao Cui

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

IOP Publishing Ltd2025

Optical Processors for AI Computing and Networking

Mustafa Yildirim

Computing has long been central to technological advancements, where each leap in compute power has catalyzed applications once thought impossible, such as smartphones. Recently, the exponential growth in the compute demand of artificial intelligence (AI) ...

EPFL2025

Advancing Phonology-Based Sign Language Assessment: From Learner to Machine-Generated Videos

Neha Tarigopula

Sign languages are rich visual languages that use both manual features like handshape, movement, and location and non-manual features such as facial expressions, head movements, and body posture to convey information. Sign language learning technologies ha ...

EPFL2025

Data-driven dynamic friction models based on Recurrent Neural Networks

Gaëtan Bixente Cortes, Antonio Joaquin Garcia Suarez

In this concise contribution, it is demonstrated that Recurrent Neural Networks (RNNs) based on Gated Recurrent Unit (GRU) architecture, possess the capability to learn the complex dynamics of rate-and-state friction (RSF) laws from synthetic data. The dat ...

2025

Deep learning for enhancement of low-resolution and noisy scanning probe microscopy images

Georg Fantner, Marcos Penedo Garcia

In this study, we employed traditional methods and deep learning models to improve resolution and quality of low-resolution AFM images made under standard ambient scanning. Both traditional methods and deep learning models were benchmarked and quantified r ...

Beilstein Institut2025

ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning

Kunpeng Yao

Motivated by the substantial achievements of Large Language Models (LLMs) in the field of natural language processing, recent research has commenced investigations into the application of LLMs for complex, long-horizon sequential task planning challenges i ...

Institute of Electrical and Electronics Engineers Inc.2024

On the Effect of Initialization: The Scaling Path of 2-Layer Neural Networks

Michaël Unser, Sebastian Jonas Neumayer, Lénaïc Chizat

In supervised learning, the regularization path is sometimes used as a convenient theoretical proxy for the optimization path of gradient descent initialized from zero. In this paper, we study a modification of the regularization path for infinite-width 2- ...

2024

GigaPose: Fast and Robust Novel Object Pose Estimation via One Correspondence

Mathieu Salzmann

We present GigaPose, a fast, robust, and accurate method for CAD-based novel object pose estimation in RGB images. GigaPose first leverages discriminative "templates", rendered images of the CAD models, to recover the out-of-plane rotation and then uses pa ...

IEEE2024

The autism susceptibility kinase, TAOK2, phosphorylates eEF2 and modulates translation

Nagammal Neelagandan

Genes implicated in translation control have been associated with autism spectrum disorders (ASDs). However, some important genetic causes of autism, including the 16p11.2 microdeletion, bear no obvious connection to translation. Here, we use proteomics, g ...

Amer Assoc Advancement Science2024

Enhancing Hand and Object Detection for Monitoring Patients with Upper-Limb Impairment: A Study on the Impact of Input Size in Foundation Models

Kamiar Aminian, Yasaman Izadmehr

The choice of input image size can have a significant impact on the performance of the state-of-the-art algorithms. We can always customize the algorithms by training and fine-tuning them on our datasets, but it is time consuming. Nowadays there is a trend ...

Institute of Electrical and Electronics Engineers Inc.2023

De novo protein design by inversion of the AlphaFold structure prediction network

Benedict Dieter Gregor Wolf, Bruno Emanuel Ferreira De Sousa Correia, Stéphane Rosset, Casper Alexander Goverde, Hamed Khakzad

De novo protein design enhances our understanding of the principles that govern protein folding and interactions, and has the potential to revolutionize biotechnology through the engineering of novel protein functionalities. Despite recent progress in comp ...

2023

Universal Approximation Property of Hamiltonian Deep Neural Networks

Giancarlo Ferrari Trecate, Muhammad Zakwan

This letter investigates the universal approximation capabilities of Hamiltonian Deep Neural Networks (HDNNs) that arise from the discretization of Hamiltonian Neural Ordinary Differential Equations. Recently, it has been shown that HDNNs enjoy, by design, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Evaluating federal policies using Bayesian time series models: estimating the causal impact of the hospital readmissions reduction program

Christine Choirat

Researchers are often faced with evaluating the effect of a policy or program that was simultaneously initiated across an entire population of units at a single point in time, and its effects over the targeted population can manifest at any time period aft ...

2023

Action Levers towards Sustainable Wellbeing: Re-Thinking Negative Emissions, Sufficiency, Deliberative Democracy

Sascha Nick

Systems theory defines leverage points as places to intervene in order to change a system. Points with high impact on system behavior are notoriously hard to act upon, and indeed most policy intervention is based at the lowest level (#12 in Donella Meadows ...

EPFL2023