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University of Tübingen
- Tübingen
- ashishpapanai.github.io
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Robust Speech Recognition via Large-Scale Weak Supervision
A natural language interface for computers
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Fast and memory-efficient exact attention
Convert Machine Learning Code Between Frameworks
This repository contains the codes of "A Lip Sync Expert Is All You Need for Speech to Lip Generation In the Wild", published at ACM Multimedia 2020. For HD commercial model, please try out Sync Labs
Source code for Twitter's Recommendation Algorithm
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"
[CVPR 2024] Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Foundation Model for Monocular Depth Estimation
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic…
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
Leveraging BERT and c-TF-IDF to create easily interpretable topics.
Not Suitable for Work (NSFW) classification using deep neural network Caffe models.
A flexible framework of neural networks for deep learning
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
Intel® Nervana™ reference deep learning framework committed to best performance on all hardware
Converts text input or URL into knowledge graph and displays
Plenoxels: Radiance Fields without Neural Networks