Nebius Token Factory
Mini toolsPapers
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LoRA: Low-Rank Adaptation of Large Language Models
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AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE
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M6: A Chinese Multimodal Pretrainer
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An autonomous debating system (Project Debater)
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Learning Transferable Visual Models From Natural Language Supervision
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Learning Transferable Visual Models From Natural Language Supervision
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Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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Denoising Diffusion Probabilistic Models
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ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
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NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
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Scaling Laws for Neural Language Models
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Dota 2 with Large Scale Deep Reinforcement Learning
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Overton: A Data System for Monitoring and Improving Machine-Learned Products
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StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
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D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
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The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
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DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
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AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
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Improving Language Understanding by Generative Pre-Training (GPT-1)
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Language Models are Unsupervised Multitask Learners
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Mask R-CNN
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Billion-scale similarity search with GPUs
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Learning with Privacy at Scale
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Proximal Policy Optimization Algorithms
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Attention Is All You Need
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Bag of Tricks for Efficient Text Classification
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Concrete Problems in AI Safety
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Deep Residual Learning for Image Recognition
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Performance of Large Language Models in Answering Critical Care Medicine Questions
