Easily Fooling Deep Neural Networks

What if you could simply tell a recommendation system what you want instead of relying on likes, dislikes, and watch history? Kyle Polich talks with Fuyuan Lyu about the DPR framework, which combines large language models and traditional recommender systems to give users direct c ...  Show more

How can researchers audit recommendation systems when the algorithms are hidden from view? Hieu Le joins Kyle Polich to discuss Auto-Like, a reinforcement learning framework that systematically explores how platforms like TikTok personalize content feeds. The conversation covers ...  Show more

Today we’re joined by Sophia Sanborn, a postdoctoral scholar at the University of California, Santa Barbara. In our conversation with Sophia, we explore the concept of universality between neural representations and deep neural networks, and how these principles of efficiency pro ...  Show more

In this episode of our podcast, we dive deep into the fascinating world of Graph Neural Networks.

First, we explore Hierarchical Networks, which allow for the efficient representation and analysis of complex graph structures by breaking them down into smaller, more mana ...

In this episode, I explore the cutting-edge technology of graph neural networks (GNNs) and how they are revolutionizing the field of artificial intelligence. I break down the complex concepts behind GNNs and explain how they work by modeling the relationships between data poin ...

Le deep learning ou l’apprentissage profond est un type d'intelligence artificielle dérivé du machine learning qui signifie lui apprentissage automatique. Ici, la machine est capable d'apprendre par elle-même, contrairement à la programmation où elle se contente d'exécuter à la l ...  Show more