ML and NLP Research Highlights of 2021

In this post, I will cover the papers and research areas that I found most inspiring. I tried to cover the papers that I was aware of but likely missed many relevant ones. Feel free to highlight them as well as ones that you found inspiring in the comments. Pre-trained models were applied in many different domains and started to be considered critical for ML research [1]. In computer vision, supervised pre-trained models such as Vision Transformer [2] have been scaled up [3] and self-supervised pre-trained models have started to match their performance [4]. The latter have been scaled beyond the controlled environment of ImageNet to random collections of images [5]. In speech, new models have been built based on wav2vec 2.0 [6] such as W2v-BERT [7] as well as more powerful multilingual models such as XLS-R [8]. At the same time, we saw new unified pre-trained models for previously under-researched modality pairs such as for videos and language [9] as well as speech and language [10]. In vision and language, controlled studies shed new light on important components of such multi-modal models [11][12].