Nicolas Hug
Employers on the program
- 2023–2025Meta4 talks
Appearances by year
- PyTorch Conference
- SciPy Conference (Scientific Computing with Python)
Talks
| Date | Event | Conference | Role | Title | Links |
|---|---|---|---|---|---|
| Oct 22, 2025 | PyTorch Conference 2025 | PyTorch Conference | poster | Poster Presentations: Ecosystem & Performance as Meta The Road To a Self-serve PyTorch Open Source Benchmark Infrastructure - Huy Do, Meta TraceLens - Democratizing AI Performance Analysis - Adeem Jassani, AMD…Show lessThe Road To a Self-serve PyTorch Open Source Benchmark Infrastructure - Huy Do, Meta TraceLens - Democratizing AI Performance Analysis - Adeem Jassani, AMD Bringing Tensor Methods To Deep Learning With TensorLy-Torch - Jean Kossaifi, NVIDIA Dmx Compressor: Empowering SOTA LLM Compression and... | |
| Sep 18, 2024 | PyTorch Conference 2024 | PyTorch Conference | lightning talk | Lightning Talk: Implementing and Using Iterable Datasets: What Could Go Wrong? as Meta PyTorch supports two kinds of datasets: Iterable datasets and indexable "map-style" datasets. Iterable datasets can be more flexible and potentially faster than…Show lessPyTorch supports two kinds of datasets: Iterable datasets and indexable "map-style" datasets. Iterable datasets can be more flexible and potentially faster than their indexable cousins. They are also much harder to use correctly, and can easily lead to silently wrong results. This talk is a quick... | |
| Oct 16, 2023 | PyTorch Conference 2023 | PyTorch Conference | speaker | Accelerating Explorations in Vision and Multimodal AI Using Pytorch Libraries as Meta PyTorch Libraries provide building blocks (data processing transforms, modeling components, loss functions, etc.) on top of PyTorch as well as examples and…Show lessPyTorch Libraries provide building blocks (data processing transforms, modeling components, loss functions, etc.) on top of PyTorch as well as examples and tutorials on how to use these building blocks for training SoTA Models. In this talk, we’ll provide insights into ongoing work to accelerate... | |
| Oct 16, 2023 | PyTorch Conference 2023 | PyTorch Conference | poster | Core Tools Poster Presentations as Meta Float8 Support in PyTorch - Vasiliy Kuznetsov, Meta Overview of the float8 dtype and op support in PyTorch. Mmap_ninja to Accelerate Machine Learning Training -…Show lessFloat8 Support in PyTorch - Vasiliy Kuznetsov, Meta Overview of the float8 dtype and op support in PyTorch. Mmap_ninja to Accelerate Machine Learning Training - Hristo Vrigazov, LABS.IO When training a model with Pytorch with a dataset that we cannot fit into memory, we want to store the dataset in... | |
| Jul 8, 2019 | SciPy 2019 | SciPy Conference (Scientific Computing with Python) | speaker | Fast Gradient Boosting Decision Trees with PyGBM and Numba |
Frequent co-speakers
| Speaker | Shared events | Shared talks | Last together |
|---|---|---|---|
| Vasiliy Kuznetsov | 3 | 1 | 2025 |
| Alban Desmaison | 3 | — | 2025 |
| Angela Yi | 3 | — | 2025 |
| Animesh Jain | 3 | — | 2025 |
| Avik Chaudhuri | 3 | — | 2025 |
| Bin Bao | 3 | — | 2025 |
| Chen Lai | 3 | — | 2025 |
| Chien-Chin Huang | 3 | — | 2025 |
| Eikan Wang | 3 | — | 2025 |
| Joe Spisak | 3 | — | 2025 |
| Kimish Patel | 3 | — | 2025 |
| Luca Antiga | 3 | — | 2025 |
| Peng Wu | 3 | — | 2025 |
| Will Constable | 3 | — | 2025 |
| Jane Xu | 2 | 1 | 2025 |
Speakers with the same name are merged into one entry, so this page may combine more than one person. Affiliations are shown as each conference listed them.