arXiv subjects

Alicia Troncoso

Publications and source records attributed to Alicia Troncoso.

3 recordsLinked to original sources

arXiv2026

Adaptive Temporal Gating of Longitudinal Magnetic Resonance Imaging for Dementia Prediction

Predicting which people with mild cognitive impairment will develop dementia matters for early treatment. Yet structural imaging models have relied almost entirely on a single scan, so the value of measuring anatomical change over time is largely untested. We ask what a second scan adds, under a strict evaluation: conversion is defined from recorded clinical diagnoses rather than enrolment category, the pretraining pool shares no participants with the evaluation cohort, a test partition is kept out of model development, and uncertainty is estimated by resampling participants, not scans. We introduce a temporal fusion network that combines paired scans in three ways (anatomical difference, cross-temporal attention, and joint context) and mixes the three with a learned per-patient gate. We compare it with single-scan and longitudinal baselines. A follow-up scan improves discrimination substantially, and a model with an unrelated architecture gains the same, so the benefit comes from temporal information, not from a particular design. How the scans are combined still matters: simple subtraction is no better than a single scan, while learned fusion recovers the full benefit. Two results count against the proposed method. It does not beat a simpler recurrent baseline in a comparison able to detect a small difference, and its adaptive gate, meant to explain individual predictions, is unstable across independently trained models and largely restates the prediction itself. Most of the improvement comes from the pretrained encoder, not the second timepoint, which points to a ceiling on what paired structural imaging can offer. The usual 0.5 threshold is also unsuitable at this prevalence: validation-chosen operating points change how clinically useful every model appears without changing any model. Further gains are more likely to come from richer inputs than from more elaborate fusion.

cs.CV↗

arXiv2026

Bridging Training and Merging Through Momentum-Aware Optimization

Training large neural networks and merging task-specific models both exploit low-rank structure and require parameter importance estimation, yet these challenges have been pursued in isolation. Current workflows compute curvature information during training, discard it, then recompute similar information for merging--wasting computation and discarding valuable trajectory data. We introduce a unified framework that maintains factorized momentum and curvature statistics during training, then reuses this information for geometry-aware model composition. The proposed method incurs modest memory overhead (approximately 30% over AdamW) to accumulate task saliency scores that enable curvature-aware merging. These scores, computed as a byproduct of optimization, provide importance estimates comparable to post-hoc Fisher computation while producing merge-ready models directly from training. We establish convergence guarantees for non-convex objectives with approximation error bounded by gradient singular value decay. On natural language understanding benchmarks, curvature-aware parameter selection outperforms magnitude-only baselines across all sparsity levels, with multi-task merging improving 1.6% over strong baselines. The proposed framework exhibits rank-invariant convergence and superior hyperparameter robustness compared to existing low-rank optimizers. By treating the optimization trajectory as a reusable asset rather than discarding it, our approach demonstrates that training-time curvature information suffices for effective model composition, enabling a unified training-merging pipeline.

cs.LG↗

arXiv2018

Time series clustering based on the characterisation of segment typologies

Time series clustering is the process of grouping time series with respect to their similarity or characteristics. Previous approaches usually combine a specific distance measure for time series and a standard clustering method. However, these approaches do not take the similarity of the different subsequences of each time series into account, which can be used to better compare the time series objects of the dataset. In this paper, we propose a novel technique of time series clustering based on two clustering stages. In a first step, a least squares polynomial segmentation procedure is applied to each time series, which is based on a growing window technique that returns different-length segments. Then, all the segments are projected into same dimensional space, based on the coefficients of the model that approximates the segment and a set of statistical features. After mapping, a first hierarchical clustering phase is applied to all mapped segments, returning groups of segments for each time series. These clusters are used to represent all time series in the same dimensional space, after defining another specific mapping process. In a second and final clustering stage, all the time series objects are grouped. We consider internal clustering quality to automatically adjust the main parameter of the algorithm, which is an error threshold for the segmenta- tion. The results obtained on 84 datasets from the UCR Time Series Classification Archive have been compared against two state-of-the-art methods, showing that the performance of this methodology is very promising.

cs.LG↗