Neural networks and fuzzy systems

Geoffrey E. Hinton: Deep learning with multiplicative interactions

Yann LeCun: Learning Hierarchies of Features

Tara Sainath: The Latest in DNN Research at IBM: DNN-based features, Low-Rank Matrices for Hybrid...

Francesco Martinuzzi: Learning Single and Multiple Chaotic Systems with Minimal Reservoir Computing

[ИАД, осень 2025] Методы глубокого обучения. Лекция 12: Multimodality, CLIP, BLIP, LLaVA

[IAD, fall 2025] Deep Learning Methods. Lecture 12: Multimodality, CLIP, BLIP, LLaVA

Live from NeurIPS: Meet the Researchers | Nemotron Labs

Become an AI Researcher Course – LLM, Math, PyTorch, Neural Networks, Transformers

Google quiere cambiar la IA para siempre (Nested Learning)

Google wants to change AI forever (Nested Learning)

Mohammad Tabish: Data-driven operator learning: Dictionary optimization and randomized neural nets

Deep Learning for intrusion detection in emerging technologies with Dr. Euclides Neto

He Co-Invented the Transformer. Now: Continuous Thought Machines [Llion Jones / Luke Darlow]

Why you should see the world like a large language model | Dan Shipper: Full Interview

Recursive Reasoning with Tiny Networks

Dana Angluin: Trying to Understand Transformers

Understanding PINN’s Failure Modes || Progressive Sharpening from Jacobian Alignment|| Nov 14, 2025

MLP Live session 25T3 Week8

[M2L 2025] 4.2 A Broad Introduction to Topological Deep Learning - Mathilde Papillon

De vidrios de espín a redes neuronales de grafos: lo que a física le enseñó inteligencia artificial

From spin glasses to graph neural networks: what artificial intelligence taught physics

AI Physics Deep Dive | Industrial Engineering Live Stream Series

[M2L 2025] 1.2 Modularity and Compositionality for Collaborative, Efficient ... - Ivan Vulić

[M2L 2025] 1.1 Introduction to Deep Learning and Transformers - Skanda Koppula

Alphafold2 principios capacidades y limitaciones

Alphafold2 principles capabilities and limitations

Weight Initialization | Xavier | He | Zero | Symmetry Problem | Deep Learning Part 7

Geordie Williamson: Neural Networks for Mathematical Discovery (October 29, 2025)

Hands-on Machine Learning -- Introduction to Artificial Neural Networks

[Paper Analysis] The Free Transformer (and some Variational Autoencoder stuff)

MLP Live session Week 6

Differential Equations as Neural Network Representations || Oct 31, 2025

MLP 25 T3 Week6

Mystères mathématiques d’intelligences pas si artificielles - Stéphane Mallat

Mathematical mysteries of not-so-artificial intelligences - Stéphane Mallat

MLP_Week5_LiveSession

Séminaire DIC-ISC-CRIA - 23 octobre 2025 par Terry SEJNOWSKI

[ИАД, осень 2025] Foundation models for spatial-time series. Занятие 5

[IAD, Fall 2025] Foundation models for spatial-time series. Session 5

Bin Yu — Understanding Deep Learning Models via Interaction Importance (Sept. 25, 2025)

Alejandro Ribeiro — Spectral Analyses of Graph Neural Networks (Sept. 26, 2025)

Stanford CS230 | Autumn 2025 | Lecture 4: Adversarial Robustness and Generative Models

Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 2 - Transformer-Based Models & Tricks

Stanford CME295 Transformers & LLMs | Autumn 2025 | Lecture 1 - Transformer

Stabilizing PDE-ML system || How Does Neural Network Training Work || Oct 17, 2025

Regularization | L1 & L2 | Dropout | Data Augmentation | Early Stopping | Deep Learning Part 4