Trending Research
Unsupervised Cross-lingual Representation Learning for Speech Recognition
This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages.
PaddleNLP
PaddlePaddle/PaddleNLP
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ACL ARR January 2022
Easy-to-use and powerful NLP library with awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications.
Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training
The Transformer architecture has improved the performance of deep learning models in domains such as Computer Vision and Natural Language Processing.
A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation
Despite its simplicity, benchmark results show our system's note estimation to be substantially better than a comparable baseline, and its frame-level accuracy to be only marginally below those of specialized state-of-the-art AMT systems.
OPT: Open Pre-trained Transformer Language Models
Large language models, which are often trained for hundreds of thousands of compute days, have shown remarkable capabilities for zero- and few-shot learning.
Ranked #1 on
Stereotypical Bias Analysis
on CrowS-Pairs
Goal-Guided Neural Cellular Automata: Learning to Control Self-Organising Systems
shyamsn97/controllable-ncas
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Inspired by cellular growth and self-organization, Neural Cellular Automata (NCAs) have been capable of "growing" artificial cells into images, 3D structures, and even functional machines.
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
r-three/t-few
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ICL incurs substantial computational, memory, and storage costs because it involves processing all of the training examples every time a prediction is made.
Ranked #1 on
Few-Shot Text Classification
on RAFT
Thin-Plate Spline Motion Model for Image Animation
yoyo-nb/thin-plate-spline-motion-model
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Firstly, we propose thin-plate spline motion estimation to produce a more flexible optical flow, which warps the feature maps of the source image to the feature domain of the driving image.
Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels
The confusion matrix, a ubiquitous visualization for helping people evaluate machine learning models, is a tabular layout that compares predicted class labels against actual class labels over all data instances.
A Unified Framework for Implicit Sinkhorn Differentiation
marvin-eisenberger/implicit-sinkhorn
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The Sinkhorn operator has recently experienced a surge of popularity in computer vision and related fields.
