Papers Impact: AI-Powered Research Impact Predictor

https://discord.gg/openfreeai

📑 Import from arXiv

Click input field to use example paper or browse papers at arxiv.org

📝 Or Enter Paper Details Manually

🔬 Scientific Methodology

  • Training Data: Model trained on extensive dataset of published papers from CS.CV, CS.CL(NLP), and CS.AI fields
  • Optimization: NDCG optimization with Sigmoid activation and MSE loss function
  • Validation: Cross-validated against historical paper impact data
  • Architecture: Advanced transformer-based deep textual analysis
  • Metrics: Quantitative analysis of citation patterns and research influence

📊 Rating Scale

Grade Score Range Description Indicator
AAA 0.900-1.000 Exceptional Impact 🌟
AA 0.800-0.899 Very High Impact ⭐
A 0.650-0.799 High Impact ✨
BBB 0.600-0.649 Above Average Impact 🔵
BB 0.550-0.599 Moderate Impact 📘
B 0.500-0.549 Average Impact 📖
CCC 0.400-0.499 Below Average Impact 📝
CC 0.300-0.399 Low Impact ✏️
C < 0.299 Limited Impact 📑

📋 Example Papers

Attention Is All You Need

Score: 0.982 | Grade: AAA 🌟 The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. 💫 Revolutionary paper that introduced the Transformer architecture, fundamentally changing NLP and deep learning.

Language Models are Few-Shot Learners

Score: 0.956 | Grade: AAA 🌟 Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. 🚀 Groundbreaking GPT-3 paper that demonstrated the power of large language models.

An Empirical Study of Neural Network Training Protocols

Score: 0.623 | Grade: BBB 🔵 This paper presents a comparative analysis of different training protocols for neural networks across various architectures. We examine the effects of learning rate schedules, batch size selection, and optimization algorithms on model convergence and final performance. Our experiments span multiple datasets and model sizes, providing practical insights for deep learning practitioners. 📚 Solid research paper with useful findings but more limited scope and impact.