Publications associées à Automated Taxonomy Induction and its Applications

Publications associées (46)

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

Machine translation (MT) systems and language models (LMs) have demonstrated impressive performance in recent years, driven by advances in architecture, algorithms, and the scaling of parameters and training data. Despite the ability of these models to int ...

EPFL2025

Programming Light Propagation for Efficient Artificial Intelligence

Ilker Oguz

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

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

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

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

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

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

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

Modeling Structured Data in Attention-based Models

Alireza Mohammadshahi

Natural language processing has experienced significant improvements with the development of Transformer-based models, which employ self-attention mechanism and pre-training strategies. However, these models still present several obstacles. A notable issue ...

EPFL2023

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

Unsupervised Term Extraction for Highly Technical Domains

Diego Matteo Antognini

Term extraction is an information extraction task at the root of knowledge discovery platforms. Developing term extractors that are able to generalize across very diverse and potentially highly technical domains is challenging, as annotations for domains r ...

2022

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

End-to-End Task-Oriented Dialog Modeling With Semi-Structured Knowledge Management

Antoine Bosselut, Silin Gao

Current task-oriented dialog (TOD) systems mostly manage structured knowledge (e.g. databases and tables) to guide the goal-oriented conversations. However, they fall short of handling dialogs which also involve unstructured knowledge (e.g. reviews and doc ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

How We Use Wikipedia: Studying Readers' Behavior with Navigation Traces

Tiziano Piccardi

In the information age, the Web and the growing global connectivity drastically simplified our access to information. Learning and fact-checking from online resources is nowadays part of our daily routine. Studying the dynamic associated with online conten ...

EPFL2022

GenIE: Generative Information Extraction

Robert West, Maxime Jean Julien Peyrard, Martin Josifoski

Structured and grounded representation of text is typically formalized by closed information extraction, the problem of extracting an exhaustive set of (subject, relation, object) triplets that are consistent with a predefined set of entities and relations ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2022

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

Mechanical intelligence for learning embodied sensor-object relationships

Ahalya Prabhakar

Intelligence involves processing sensory experiences into representations useful for prediction. Understanding sensory experiences and building these contextual representations without prior knowledge of sensor models and environment is a challenging unsup ...

Nature Portfolio2022

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

Strong Heuristics for Named Entity Linking

Robert West, Akhil Arora, Andreas Oliver Spitz

Named entity linking (NEL) in news is a challenging endeavour due to the frequency of unseen and emerging entities, which necessitates the use of unsupervised or zero-shot methods. However, such methods tend to come with caveats, such as no integration of ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2022