The mixture proportions of pretraining data domains (e.g., Wikipedia, bo...
This work discusses the use of contrastive learning and deep learning fo...
We present a method to formulate algorithm discovery as program search, ...
Mixup is a popular data augmentation technique for training deep neural
...
We present a combined scaling method called BASIC that achieves 85.7
zer...
For a global breeding organization, identifying the next generation of
s...
For a globally recognized planting breeding organization, manually-recor...
Back-translation is an effective strategy to improve the performance of
...
Pre-trained representations are becoming crucial for many NLP and percep...
Neural networks are often over-parameterized and hence benefit from
aggr...
Large scale crop yield estimation is, in part, made possible due to the
...
Despite recent success, most contrastive self-supervised learning method...
EfficientNets are a family of state-of-the-art image classification mode...
The future landscape of modern farming and plant breeding is rapidly cha...
Modern trends in digital agriculture have seen a shift towards artificia...
The success of modern farming and plant breeding relies on accurate and
...
Precise in-season corn grain yield estimates enable farmers to make real...
Many training algorithms of a deep neural network can be interpreted as
...
To acquire a new skill, humans learn better and faster if a tutor, based...
Aggregating multiple learners through an ensemble of models aims to make...
Multilingual training of neural machine translation (NMT) systems has le...
Recent advances in Neural Machine Translation (NMT) show that adding
syn...
In this work, we examine methods for data augmentation for text-based ta...
We propose Efficient Neural Architecture Search (ENAS), a fast and
inexp...
We propose Efficient Neural Architecture Search (ENAS), a fast and
inexp...
The past few years have witnessed a growth in size and computational
req...
This paper presents a framework to tackle combinatorial optimization pro...
An attentional mechanism has lately been used to improve neural machine
...