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
Paper • 1201.0490 • Published • 1 -
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Paper • 1406.1078 • Published -
Distributed Representations of Sentences and Documents
Paper • 1405.4053 • Published -
Sequence to Sequence Learning with Neural Networks
Paper • 1409.3215 • Published • 3 -
Neural Machine Translation by Jointly Learning to Align and Translate
Paper • 1409.0473 • Published • 5 -
Text Understanding from Scratch
Paper • 1502.01710 • Published -
XGBoost: A Scalable Tree Boosting System
Paper • 1603.02754 • Published • 1 -
Attention Is All You Need
Paper • 1706.03762 • Published • 49 -
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Paper • 1804.07461 • Published • 4 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 16 -
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 7 -
Energy and Policy Considerations for Deep Learning in NLP
Paper • 1906.02243 • Published • 2 -
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Paper • 1906.08237 • Published -
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Paper • 1910.01108 • Published • 14 -
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Paper • 1910.10683 • Published • 10 -
AR-Net: A simple Auto-Regressive Neural Network for time-series
Paper • 1911.12436 • Published • 1 -
GLU Variants Improve Transformer
Paper • 2002.05202 • Published • 1 -
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
Paper • 1908.08681 • Published • 1 -
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Paper • 2101.00027 • Published • 6 -
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
Paper • 2101.03961 • Published • 14 -
LoRA: Low-Rank Adaptation of Large Language Models
Paper • 2106.09685 • Published • 30 -
Evaluating Large Language Models Trained on Code
Paper • 2107.03374 • Published • 7 -
NeuralProphet: Explainable Forecasting at Scale
Paper • 2111.15397 • Published • 1 -
LLaMA: Open and Efficient Foundation Language Models
Paper • 2302.13971 • Published • 13 -
Searching for Activation Functions
Paper • 1710.05941 • Published • 1 -
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paper • 1912.01703 • Published • 1 -
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 • 1 -
Caffe: Convolutional Architecture for Fast Feature Embedding
Paper • 1408.5093 • Published • 1 -
Teaching Machines to Read and Comprehend
Paper • 1506.03340 • Published • 2 -
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
Paper • 1901.08149 • Published • 2 -
Annotated History of Modern AI and Deep Learning
Paper • 2212.11279 • Published • 1 -
KAN: Kolmogorov-Arnold Networks
Paper • 2404.19756 • Published • 108 -
Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models
Paper • 2405.01535 • Published • 119 -
Your Transformer is Secretly Linear
Paper • 2405.12250 • Published • 149 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 86 -
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper • 2312.00752 • Published • 138 -
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Paper • 2405.21060 • Published • 63 -
The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale
Paper • 2406.17557 • Published • 87 -
Were RNNs All We Needed?
Paper • 2410.01201 • Published • 47