56 citations · 74 across the 14 of their papers we have counts for
22 papers · 1 filter
Grokking Finite-Dimensional Algebra
Pascal Jr Tikeng Notsawo, Guillaume Dumas, Guillaume Rabusseau
This paper investigates the grokking phenomenon, which refers to the sudden transition from a long memorization to generalization observed during neural networks training, in the c…
ClustRecNet: A Novel End-to-End Deep Learning Framework for Clustering Algorithm Recommendation
Mohammadreza Bakhtyari, Bogdan Mazoure, Renato Cordeiro de Amorim +2
Identifying an effective clustering algorithm for a given dataset remains a fundamental unsupervised learning issue. We introduce ClustRecNet, a novel end-to-end deep learning fram…
Generative Learning of Continuous Data by Tensor Networks
Alex Meiburg, Jing Chen, Jacob Miller +3
Beyond their origin in modeling many-body quantum systems, tensor networks have emerged as a promising class of models for solving machine learning problems, notably in unsupervise…
Spectral Regularization: an Inductive Bias for Sequence Modeling
Kaiwen Hou, Guillaume Rabusseau
Various forms of regularization in learning tasks strive for different notions of simplicity. This paper presents a spectral regularization technique, which attaches a unique induc…
Extracting Weighted Automata for Approximate Minimization in Language Modelling
Clara Lacroce, Prakash Panangaden, Guillaume Rabusseau
In this paper we study the approximate minimization problem for language modelling. We assume we are given some language model as a black box. The objective is to obtain a weighted…
Lower and Upper Bounds on the VC-Dimension of Tensor Network Models
Behnoush Khavari, Guillaume Rabusseau
Tensor network methods have been a key ingredient of advances in condensed matter physics and have recently sparked interest in the machine learning community for their ability to…