SMOTE: Synthetic Minority Over-sampling Technique
Paper
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1106.1813
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Published
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1
Papers
Machine Learning and Neural Network papers 📜
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Scikit-learn: Machine Learning in Python
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Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
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Distributed Representations of Sentences and Documents
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Sequence to Sequence Learning with Neural Networks
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Neural Machine Translation by Jointly Learning to Align and Translate
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Text Understanding from Scratch
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XGBoost: A Scalable Tree Boosting System
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Attention Is All You Need
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GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
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Energy and Policy Considerations for Deep Learning in NLP
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XLNet: Generalized Autoregressive Pretraining for Language Understanding
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Paper • 1910.01108 • Published • 21 -
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Paper • 1910.10683 • Published • 15 -
AR-Net: A simple Auto-Regressive Neural Network for time-series
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GLU Variants Improve Transformer
Paper • 2002.05202 • Published • 4 -
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Paper • 2003.10555 • Published -
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Paper • 1606.05250 • Published • 3 -
Mish: A Self Regularized Non-Monotonic Activation Function
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The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Paper • 2101.00027 • Published • 9 -
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
Paper • 2101.03961 • Published • 13 -
LoRA: Low-Rank Adaptation of Large Language Models
Paper • 2106.09685 • Published • 55 -
Evaluating Large Language Models Trained on Code
Paper • 2107.03374 • Published • 8 -
NeuralProphet: Explainable Forecasting at Scale
Paper • 2111.15397 • Published • 1 -
LLaMA: Open and Efficient Foundation Language Models
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Searching for Activation Functions
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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TensorFlow: A system for large-scale machine learning
Paper • 1605.08695 • Published • 1 -
Theano: A Python framework for fast computation of mathematical expressions
Paper • 1605.02688 • Published • 2 -
Caffe: Convolutional Architecture for Fast Feature Embedding
Paper • 1408.5093 • Published • 1 -
Teaching Machines to Read and Comprehend
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TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
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Annotated History of Modern AI and Deep Learning
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KAN: Kolmogorov-Arnold Networks
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Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models
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Your Transformer is Secretly Linear
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An Introduction to Vision-Language Modeling
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Mamba: Linear-Time Sequence Modeling with Selective State Spaces
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Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Paper • 2405.21060 • Published • 68 -
The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale
Paper • 2406.17557 • Published • 98 -
Were RNNs All We Needed?
Paper • 2410.01201 • Published • 53 -
s1: Simple test-time scaling
Paper • 2501.19393 • Published • 124 -
SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
Paper • 2405.15793 • Published • 7