Top Papers
Improving the Cluster Structure Extracted from OPTICS Plots
Density-based clustering is closely associated with the two algorithms DBSCAN and OPTICS.
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.
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
In this work we introduce a new optimisation method called SAGA in the spirit of SAG, SDCA, MISO and SVRG, a set of recently proposed incremental gradient algorithms with fast linear convergence rates.
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on
Question Answering
on PubChemQA
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Multi-query attention (MQA), which only uses a single key-value head, drastically speeds up decoder inference.
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
CorentinJ/Real-Time-Voice-Cloning
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NeurIPS 2018
Clone a voice in 5 seconds to generate arbitrary speech in real-time
Efficient Neural Audio Synthesis
CorentinJ/Real-Time-Voice-Cloning
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ICML 2018
The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile CPU in real time.
Generalized End-to-End Loss for Speaker Verification
CorentinJ/Real-Time-Voice-Cloning
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In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function.
Ranked #1 on
Speaker Verification
on CALLHOME
