Search Results for author:
Found 18 papers, 6 papers with code
Learning Associative Memories with Gradient Descent
This work focuses on the training dynamics of one associative memory module storing outer products of token embeddings.
Mode Estimation with Partial Feedback
The combination of lightly supervised pre-training and online fine-tuning has played a key role in recent AI developments.
Touring sampling with pushforward maps
The number of sampling methods could be daunting for a practitioner looking to cast powerful machine learning methods to their specific problem.
Scaling Laws for Associative Memories
Learning arguably involves the discovery and memorization of abstract rules.
Open Problem: Learning with Variational Objectives on Measures
The theory of statistical learning has focused on variational objectives expressed on functions.
Out-of-Distribution Generalization
Weakly-supervised Learning
The Galerkin method beats Graph-Based Approaches for Spectral Algorithms
Historically, the machine learning community has derived spectral decompositions from graph-based approaches.
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need
Third, it provides a proper active learning framework yielding low-cost solutions to annotate datasets, arguably bringing the gap between theory and practice of active learning that is based on simple-to-answer-by-non-experts queries of semantic relationships between inputs.
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
Self-supervised learning (SSL) has emerged as a powerful framework to learn representations from raw data without supervision.
On minimal variations for unsupervised representation learning
Unsupervised representation learning aims at describing raw data efficiently to solve various downstream tasks.
From Weakly Supervised Learning to Active Learning
This thesis is motivated by the following question: can we derive a more generic framework than the one of supervised learning in order to learn from clutter data?
Active Labeling: Streaming Stochastic Gradients
The workhorse of machine learning is stochastic gradient descent.
A Case of Exponential Convergence Rates for SVM
Classification is often the first problem described in introductory machine learning classes.
Disambiguation of weak supervision with exponential convergence rates
Machine learning approached through supervised learning requires expensive annotation of data.
Fast rates in structured prediction
Discrete supervised learning problems such as classification are often tackled by introducing a continuous surrogate problem akin to regression.
Diptychs of human and machine perceptions
We propose visual creations that put differences in algorithms and humans \emph{perceptions} into perspective.
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning
As annotations of data can be scarce in large-scale practical problems, leveraging unlabelled examples is one of the most important aspects of machine learning.
Structured Prediction with Partial Labelling through the Infimum Loss
Annotating datasets is one of the main costs in nowadays supervised learning.
