SMOTE: Synthetic Minority Over-sampling Technique
Paper
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1106.1813
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1
papers
machine learning and neural network papers π
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Scikit-learn: Machine Learning in Python
Paper β’ 1201.0490 β’ Published β’ 1 -
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
Paper β’ 1406.1078 β’ Published -
Distributed Representations of Sentences and Documents
Paper β’ 1405.4053 β’ Published -
Sequence to Sequence Learning with Neural Networks
Paper β’ 1409.3215 β’ Published β’ 3 -
Neural Machine Translation by Jointly Learning to Align and Translate
Paper β’ 1409.0473 β’ Published β’ 5 -
Text Understanding from Scratch
Paper β’ 1502.01710 β’ Published -
XGBoost: A Scalable Tree Boosting System
Paper β’ 1603.02754 β’ Published β’ 1 -
Attention Is All You Need
Paper β’ 1706.03762 β’ Published β’ 55 -
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Paper β’ 1804.07461 β’ Published β’ 4 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper β’ 1810.04805 β’ Published β’ 17 -
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper β’ 1907.11692 β’ Published β’ 7 -
Energy and Policy Considerations for Deep Learning in NLP
Paper β’ 1906.02243 β’ Published β’ 2 -
XLNet: Generalized Autoregressive Pretraining for Language Understanding
Paper β’ 1906.08237 β’ Published -
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Paper β’ 1910.01108 β’ Published β’ 14 -
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Paper β’ 1910.10683 β’ Published β’ 11 -
AR-Net: A simple Auto-Regressive Neural Network for time-series
Paper β’ 1911.12436 β’ Published β’ 1 -
GLU Variants Improve Transformer
Paper β’ 2002.05202 β’ Published β’ 3 -
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Paper β’ 2003.10555 β’ Published -
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Paper β’ 1606.05250 β’ Published β’ 3 -
Mish: A Self Regularized Non-Monotonic Activation Function
Paper β’ 1908.08681 β’ Published β’ 1 -
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Paper β’ 2101.00027 β’ Published β’ 6 -
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
Paper β’ 2101.03961 β’ Published β’ 13 -
LoRA: Low-Rank Adaptation of Large Language Models
Paper β’ 2106.09685 β’ Published β’ 35 -
Evaluating Large Language Models Trained on Code
Paper β’ 2107.03374 β’ Published β’ 8 -
NeuralProphet: Explainable Forecasting at Scale
Paper β’ 2111.15397 β’ Published β’ 1 -
LLaMA: Open and Efficient Foundation Language Models
Paper β’ 2302.13971 β’ Published β’ 14 -
Searching for Activation Functions
Paper β’ 1710.05941 β’ Published β’ 1 -
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paper β’ 1912.01703 β’ Published β’ 1 -
TensorFlow: A system for large-scale machine learning
Paper β’ 1605.08695 β’ Published β’ 1 -
Theano: A Python framework for fast computation of mathematical expressions
Paper β’ 1605.02688 β’ Published β’ 1 -
Caffe: Convolutional Architecture for Fast Feature Embedding
Paper β’ 1408.5093 β’ Published β’ 1 -
Teaching Machines to Read and Comprehend
Paper β’ 1506.03340 β’ Published β’ 2 -
TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents
Paper β’ 1901.08149 β’ Published β’ 3 -
Annotated History of Modern AI and Deep Learning
Paper β’ 2212.11279 β’ Published β’ 1 -
KAN: Kolmogorov-Arnold Networks
Paper β’ 2404.19756 β’ Published β’ 111 -
Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models
Paper β’ 2405.01535 β’ Published β’ 121 -
Your Transformer is Secretly Linear
Paper β’ 2405.12250 β’ Published β’ 153 -
An Introduction to Vision-Language Modeling
Paper β’ 2405.17247 β’ Published β’ 88 -
Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Paper β’ 2312.00752 β’ Published β’ 143 -
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Paper β’ 2405.21060 β’ Published β’ 66 -
The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale
Paper β’ 2406.17557 β’ Published β’ 93 -
Were RNNs All We Needed?
Paper β’ 2410.01201 β’ Published β’ 51 -
s1: Simple test-time scaling
Paper β’ 2501.19393 β’ Published β’ 111 -
SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
Paper β’ 2405.15793 β’ Published β’ 5