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Aalborg University
- Aalborg, Denmark
- https://sarthakyadav.github.io/
- @yadav_sar
Highlights
- Pro
Stars
This is the official repo for Gradient Agreement Filtering (GAF).
A family of state-of-the-art Transformer-based audio codecs for low-bitrate high-quality audio coding.
A library for fast and efficient mLSTM Kernels.
Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable…
Repository containing experimentation platform on how to train, infer on wav2vec2 models.
idiap / coqui-ai-TTS
Forked from coqui-ai/TTS🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
[ICLR 2020] A repository for extremely fast adversarial training using FGSM
This repository implements SummaryMixing, a simpler, faster and much cheaper replacement to self-attention for automatic speech recognition (see: https://arxiv.org/abs/2307.07421). The code is read…
Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis
[Mamba-Survey-2024] Paper list for State-Space-Model/Mamba and it's Applications
Implementation of gradient-based adversarial attack(FGSM,MI-FGSM,PGD)
A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.
[ICML 2024] Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
Denoising Masked Autoencoders Help Robust Classification.
PyTorch implementation of adversarial attacks [torchattacks]
A challenge to explore adversarial robustness of neural networks on MNIST.
TRADES (TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization)
Benchmarking RL for POMDPs in Pure JAX [Code for "Structured State Space Models for In-Context Reinforcement Learning" (NeurIPS 2023)]
Uses WiFi signals 📶 and machine learning to predict where you are
Neural Networks: Zero to Hero
Gemini is a modern LaTex beamerposter theme 🖼
An implementation of soft-DTW divergences.