Search Results for author:
Found 13 papers, 3 papers with code
MASS: MoErging through Adaptive Subspace Selection
Model merging has recently emerged as a lightweight alternative to ensembling, combining multiple fine-tuned models into a single set of parameters with no additional training overhead.
The Majority Vote Paradigm Shift: When Popular Meets Optimal
Reliably labelling data typically requires annotations from multiple human workers.
Robustness of Graph Classification: failure modes, causes, and noise-resistant loss in Graph Neural Networks
Graph Neural Networks (GNNs) are powerful at solving graph classification tasks, yet applied problems often contain noisy labels.
Task Singular Vectors: Reducing Task Interference in Model Merging
In this paper, we study task vectors at the layer level, focusing on task layer matrices and their singular value decomposition.
ATM: Improving Model Merging by Alternating Tuning and Merging
Given this parallel between task vectors and gradients, we propose viewing model merging as a single step in an iterative process that alternates between tuning and merging (ATM).
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM).
$\nabla τ$: Gradient-based and Task-Agnostic machine Unlearning
In this study, we introduce Gradient-based and Task-Agnostic machine Unlearning ($\nabla \tau$), an optimization framework designed to remove the influence of a subset of training data efficiently.
Link Prediction with Physics-Inspired Graph Neural Networks
In this article, we focus on the valuable task of link prediction under heterophily, an interesting problem for recommendation systems, social network analysis, and other applications.
A topological description of loss surfaces based on Betti Numbers
In the context of deep learning models, attention has recently been paid to studying the surface of the loss function in order to better understand training with methods based on gradient descent.
Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design
Most of the current hypergraph learning methodologies and benchmarking datasets in the hypergraph realm are obtained by lifting procedures from their graph analogs, leading to overshadowing specific characteristics of hypergraphs.
Learning with Noisy Labels through Learnable Weighting and Centroid Similarity
We introduce a novel method for training machine learning models in the presence of noisy labels, which are prevalent in domains such as medical diagnosis and autonomous driving and have the potential to degrade a model's generalization performance.
Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels
In practical settings, classification datasets are obtained through a labelling process that is usually done by humans.
NEWRON: A New Generalization of the Artificial Neuron to Enhance the Interpretability of Neural Networks
In this work, we formulate NEWRON: a generalization of the McCulloch-Pitts neuron structure.
