Random-Set Neural Network (ICLR)

PooDLe | NYU Meta FAIR

OpenAI MLE-Bench for Machine Learning Agents Evaluation

Kaiming He: A Decade's Battle on Dataset Bias

Livebench - Contamination-Free LLM Benchmark

Meta AI x NYU: Hierarchical World Models

V-JEPA from Meta FAIR Yann LeCun, Xinlei Chen et al.

Yann LeCun: I'm right after all!

NeurIPS: The Marriage of Noise and Simplicity

Batch Normalization (ICML 2025 Test Of Time Award)

GlassBox Learning with Tabular Data #AI

When do Adversarial Attacks against Language Model matter? #ai

This is not a Multimodal Presentation at NeurIPS :)

Style Memorization in Diffusion Models

Realistic Threat Model for LLM Jailbreaks

Infecting Multi Agents via Self Propagating Adversarial Attack

Never Ending Reinforcement Learning!

FAILURES!! Transferable Image Jailbreaks between Vision Language Model

Nvidia NEW Multimodal LLM - Describe Anything Model (DAM)

Algorithms & Applications of Programmatic Reinforcement Learning and Code Generation

Everything about the best LLM on Earth Grok 3 w/ Elon Musk

GROK 3 in 26 Minutes w/ Elon Musk on X [4K60]

Out-of-Distribution Generalization: Shortcuts, Spuriousness, and Stability

The Shape of AI to Come! Yann LeCun at AI Summit

State Space Models (SSMs) and the return of RNNs | ICML

xLSTM: Extended Long Short Term Memory

AI Action Summit Firechat w/ Yann LeCun, Schölkopf and Stéphane Mallat

Language Models meet World Models (and Agent Models)

The Shape of AI to Come! Yann LeCun at AI Action Summit 2025

Language Modeling: A Tutorial on Data Preparation, Model Training, and Adaptation

Advancing Data Selection for Foundation Models: From Heuristics to Principled Methods

Beyond Decoding: Meta-Generation Algorithms for Large Language Models | NeurIPS

Albert Gu: Structured State Space Models for Deep Sequence Modeling at NeurIPS

Yejin Choi: Possible Impossibilities and Impossible Possibilities | MLSys 2024

Andrew Ng: Application Development using Large Language Models at NeurIPS

Jeff Dean (Google Research & Deep Mind): Advances in ML for Systems and Systems for ML | MLSys 2024

Keming Lu (Alibaba Research) - Qwen: Towards a Generalist Model at NeurIPS | Open Source LLM

Jeff Dean (Google Research & DeepMind): Exciting Directions in ML for Computer Systems

Greg Corrado, Jeff Dean: Distributed Representations of Words and Phrases & their Compositionality

Yoshua Bengio et al: What is out of distribution (OOD) generalization & why is it important? NeurIPS

Q&A for Ilya Sutskever at NeurIPS 2024 (Test of Time Award)

Ilya Sutskever: Sequence to sequence learning with neural networks | NeurIPS 2024 Test of Time Award

Yoshua Bengio sharing on his Deep Learning Journey

Deep Learning | A Deep Learning Journey | Yoshua Bengio | NeurIPS

Applied AI | Insights from NVIDIA Research | Bill Dally

CV | Raytracing for Deep Neural Networks Training

NLP | Faster Transformer

3D | Deep Learning for 3D Vision

Data Visualization | High-Performance, Remote Scientific Visualization in Jupyter Notebooks

Tabular Data | Google TabNet: Interpretable Tabular Data Learning | AAAI

Meta-Learning | AAAI Tutorial

Deep Learning | Learning with Small Data

Applied AI | Drug Discovery

Deep Learning | Deep Randomized Neural Network

Time Series | Informer - Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Self-Supervised Learning | Curiosity Driven Exploration by Self-Supervised Prediction

Self-Supervised Learning | Self-supervision for Learning from the Bottom Up by Alexei A. Efros

Representation Learning | Compression with Implicit Neural Representations

General AI | Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator

Time Series | TS Classification at Scale Geoff Webb | UoA Machine Learning Seminar