24 citations · 52 across the 16 of their papers we have counts for
9 papers · 1 filter
Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning
Yatin Dandi, Matteo Vilucchio, Luca Arnaboldi +2
Understanding how deep neural networks learn useful internal representations from data remains a central open problem in the theory of deep learning. We introduce Neural Low-Degree…
Fundamental computational limits of weak learnability in high-dimensional multi-index models
Emanuele Troiani, Yatin Dandi, Leonardo Defilippis +3
Multi-index models - functions which only depend on the covariates through a non-linear transformation of their projection on a subspace - are a useful benchmark for investigating…
A Gentle Introduction to Gradient-Based Optimization and Variational Inequalities for Machine Learning
Neha S. Wadia, Yatin Dandi, Michael I. Jordan
The rapid progress in machine learning in recent years has been based on a highly productive connection to gradient-based optimization. Further progress hinges in part on a shift i…
Data-heterogeneity-aware Mixing for Decentralized Learning
Yatin Dandi, Anastasia Koloskova, Martin Jaggi +1
Decentralized learning provides an effective framework to train machine learning models with data distributed over arbitrary communication graphs. However, most existing approaches…
Understanding Layer-wise Contributions in Deep Neural Networks through Spectral Analysis
Yatin Dandi, Arthur Jacot
Spectral analysis is a powerful tool, decomposing any function into simpler parts. In machine learning, Mercer's theorem generalizes this idea, providing for any kernel and input d…
Implicit Gradient Alignment in Distributed and Federated Learning
Yatin Dandi, Luis Barba, Martin Jaggi
A major obstacle to achieving global convergence in distributed and federated learning is the misalignment of gradients across clients, or mini-batches due to heterogeneity and sto…
Model-Agnostic Learning to Meta-Learn
Arnout Devos, Yatin Dandi
In this paper, we propose a learning algorithm that enables a model to quickly exploit commonalities among related tasks from an unseen task distribution, before quickly adapting t…
Generalized Adversarially Learned Inference
Yatin Dandi, Homanga Bharadhwaj, Abhishek Kumar +1
Allowing effective inference of latent vectors while training GANs can greatly increase their applicability in various downstream tasks. Recent approaches, such as ALI and BiGAN fr…
Jointly Trained Image and Video Generation using Residual Vectors
Yatin Dandi, Aniket Das, Soumye Singhal +2
In this work, we propose a modeling technique for jointly training image and video generation models by simultaneously learning to map latent variables with a fixed prior onto real…