Speaker: Daniel Ramos Castro. Abstract: Probabilistic predictions are vital for decision-making in many applications of machine learning and AI, including medicine, forensics, security, and safety. However, many multiclass classifiers produce poorly calibrated outputs, leading to suboptimal decisions with potentially high… Read More
Titans: Learning to Memorize at Test Time
Speaker: Adrián Aranda Márquez. Abstract: This presentation provides an in-depth analysis of the paper Titans: Learning to Memorize at Test Time, which proposes a novel neural architecture designed to enhance long-term contextual learning in sequence modeling. The authors introduce the Titans… Read More
Calibration and Fusion of End-to-End Neural Diarization Models: A Comprehensive Framework
Speaker: Sergio Álvarez Balanya Abstract: End-to-End Neural Diarization (EEND) systems produce frame-level probabilistic speaker activity estimates, yet the reliability of these confidence scores remains largely unexplored. Unlike hard-decision fusion approaches such as DOVER-Lap, working with continuous probability outputs enables more… Read More
YOLO-based Transfer Learning for Acoustic Event Detection using Visual Object Detection Techniques
Speaker: Sergio Segovia González. Abstract: Traditional SED approaches are based on either specialized models or on these models in combination with general audio embedding extractors. In this article we propose to reframe SED as an object detection task in the… Read More
Auditory General Intelligence (JSALT-2025)
Speaker: Laura Herrera Alarcón. Abstract: The emergence of Large Audio Language Models (LALMs) has expanded the ability of LLMs to understand and reason over audio. In response, new benchmarks have been introduced to measure these capabilities. Yet, most rely on… Read More
Fitting Protein Language Models (PLMs) for the prediction of protein functionality using zero-shot and few-shot techniques.
Speaker: Juan Antonio Gordillo Gayo. Abstract: The unprecedent success of deep learning has driven unprecedented progress across many scientific domains, solving tasks long considered intractable with traditional methods. A remarkable example is AlphaFold, which made it possible to predict protein… Read More
Open science in the service of conservation: An accessible, user-friendly machine learning workflow for automated anuran monitoring in complex Neotropical soundscapes
Speaker: Gabriel Bidart Abstract: Amphibian populations worldwide are declining, particularly in biodiversity hotspots such as the Neotropics, posing urgent conservation challenges. Acoustic monitoring offers a non-invasive tool for tracking amphibian presence and activity, but large-scale audio datasets pose bottlenecks. We… Read More
Introduction to Protein Language Models: biological concepts and computational tools
Speaker: Juan Antonio Gordillo Gayo. Abstract: Proteins are the main executors of life: they catalyze reactions, transmit signals, structure tissues, and regulate essential cellular processes. Their function is intimately determined by the sequence of amino acids that compose them, which… Read More
Optimization of a Deep Learning Model for DNA Analysis under Hypoxemic Conditions
Speaker: Paloma Villanueva Fuster. Abstract: This study focuses on predicting hypoxia-inducible factor (HIF) binding sites, which are critical regulators of the cellular response to low oxygen levels and are implicated in various diseases, including cancer. Using DNABERT-2, a Transformer-based language… Read More
Detection and classification of plants and their condition based on ultrasound patterns generated under abiotic stress
Speaker: Fernando David Modrego Arceo Abstract: Plant bioacoustics is an emerging field that suggests that plants not only respond to sound stimuli but also emit detectable acoustic signals, particularly under stress conditions. Recent studies have revealed airborne ultrasonic emissions produced… Read More