Row 68253
Content Data
This page contains data entry 68253 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.
Hey Reddit,
Tired of transformers? Is attention really all you need? Meet SSAMBA (Self-Supervised Audio Mamba)! 🐍✨
This attention-free, purely state-space model (SSM)-based, self-supervised marvel doesn’t just hiss—it roars! SSAMBA achieves better or similar performance to its transformer-based counterparts (SSAST) on tasks like speaker identification, keyword spotting, and audio classification. But here's the kicker: it’s much more GPU memory efficient and quicker at inference, especially with longer audio lengths.
Curious? Check out the full paper here: [SSAMBA on arXiv](https://arxiv.org/abs/2405.11831)
Thanks for tuning in!
| Field | Value |
|---|---|
| text | Hey Reddit, Tired of transformers? Is attention really all you need? Meet SSAMBA (Self-Supervised Audio Mamba)! 🐍✨ This attention-free, purely state-space model (SSM)-based, self-supervised marvel doesn’t just hiss—it roars! SSAMBA achieves better or similar performance to its transformer-based counterparts (SSAST) on tasks like speaker identification, keyword spotting, and audio classification. But here's the kicker: it’s much more GPU memory efficient and quicker at inference, e… |
| label | r/machinelearning |
| dataType | post |
| communityName | r/MachineLearning |
| datetime | 2024-05-23 |
| username_encoded | Z0FBQUFBQm5Lak1keFB5ckJ4YmZ3czl2SWJpVER3aExVUVl1MUltLVpYVVdGU1VvVmFscDJ5aGQ3OWR0VzVKdzFwdktwZl9KeGJCUndHdGgySDVQUk5hWjBPbEtadkFRbzZGZGUzUFVsdTNtX0h6dl8yRWI4WXM9 |
| url_encoded | Z0FBQUFBQm5Lak91WjJtRlJ0b0JvWTk3Z1VjSXdtNHBKSjhsTi04d0FuSVhJU0Ewa0FqbHhyZWNWZnFPNjM2alNFbEh0eW9LZ1dldHZOTzNrNDhVSmF6YWZjLXExUmdtXzdRSmEyYU5ESnBzOVJBQ1BOZ09zSldzVENEdDFTZXhXMEViaDdoUEdYSVZhRHRFOTlkTzRLNXZ4bWtoX2NYNVBqallXTXlVMERYOExHTXppaGNmeVlnb3FEX1NVR2RYVi15RmpNZHBCbkhYSDZudGpnZlFaM29VQjIwLXQ5Tl9IZz09 |
Raw Record
{
"text": "Hey Reddit, \n\nTired of transformers? Is attention really all you need? Meet SSAMBA (Self-Supervised Audio Mamba)! 🐍✨ \n\nThis attention-free, purely state-space model (SSM)-based, self-supervised marvel doesn’t just hiss—it roars! SSAMBA achieves better or similar performance to its transformer-based counterparts (SSAST) on tasks like speaker identification, keyword spotting, and audio classification. But here's the kicker: it’s much more GPU memory efficient and quicker at inference, especially with longer audio lengths. \n\nCurious? Check out the full paper here: [SSAMBA on arXiv](https://arxiv.org/abs/2405.11831) \n\nThanks for tuning in! ",
"label": "r/machinelearning",
"dataType": "post",
"communityName": "r/MachineLearning",
"datetime": "2024-05-23",
"username_encoded": "Z0FBQUFBQm5Lak1keFB5ckJ4YmZ3czl2SWJpVER3aExVUVl1MUltLVpYVVdGU1VvVmFscDJ5aGQ3OWR0VzVKdzFwdktwZl9KeGJCUndHdGgySDVQUk5hWjBPbEtadkFRbzZGZGUzUFVsdTNtX0h6dl8yRWI4WXM9",
"url_encoded": "Z0FBQUFBQm5Lak91WjJtRlJ0b0JvWTk3Z1VjSXdtNHBKSjhsTi04d0FuSVhJU0Ewa0FqbHhyZWNWZnFPNjM2alNFbEh0eW9LZ1dldHZOTzNrNDhVSmF6YWZjLXExUmdtXzdRSmEyYU5ESnBzOVJBQ1BOZ09zSldzVENEdDFTZXhXMEViaDdoUEdYSVZhRHRFOTlkTzRLNXZ4bWtoX2NYNVBqallXTXlVMERYOExHTXppaGNmeVlnb3FEX1NVR2RYVi15RmpNZHBCbkhYSDZudGpnZlFaM29VQjIwLXQ5Tl9IZz09"
}
Entry Information
- Entry ID: 68253
- Repository: Axioma AXP
- Dataset: arrmlet/reddit_dataset_36
- Total Entries: 100,000