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Found 12 papers, 6 papers with code
Scikit-learn: Machine Learning in Python
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
MAP Inference in Chains using Column Generation
Linear chains and trees are basic building blocks in many applications of graphical models.
Learning Soft Linear Constraints with Application to Citation Field Extraction
Accurately segmenting a citation string into fields for authors, titles, etc.
Lexicon Infused Phrase Embeddings for Named Entity Resolution
Most state-of-the-art approaches for named-entity recognition (NER) use semi supervised information in the form of word clusters and lexicons.
Efficient Non-parametric Estimation of Multiple Embeddings per Word in Vector Space
There is rising interest in vector-space word embeddings and their use in NLP, especially given recent methods for their fast estimation at very large scale.
Large scale distributed neural network training through online distillation
Two neural networks trained on disjoint subsets of the data can share knowledge by encouraging each model to agree with the predictions the other model would have made.
TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning
TensorFlow Eager is a multi-stage, Python-embedded domain-specific language for hardware-accelerated machine learning, suitable for both interactive research and production.
Faster Neural Network Training with Data Echoing
In the twilight of Moore's law, GPUs and other specialized hardware accelerators have dramatically sped up neural network training.
FRUIT: Faithfully Reflecting Updated Information in Text
Textual knowledge bases such as Wikipedia require considerable effort to keep up to date and consistent.
Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$
Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves.
Arithmetic Sampling: Parallel Diverse Decoding for Large Language Models
Decoding methods for large language models often trade-off between diversity of outputs and parallelism of computation.
PaLM 2 Technical Report
Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM.
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