Random Embeddings and Linear Regression can Predict Protein Function

Random Embeddings and Linear Regression can Predict Protein Function

25 April 2021

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Papers citing "Random Embeddings and Linear Regression can Predict Protein Function"

11 / 11 papers shown

Title
Is Transfer Learning Necessary for Protein Landscape Prediction? Amir Shanehsazzadeh David Belanger David Dohan SSL 123 62 0 31 Oct 2020
ProtTrans: Towards Cracking the Language of Life's Code Through Self-Supervised Deep Learning and High Performance Computing Ahmed Elnaggar M. Heinzinger Christian Dallago Ghalia Rehawi Yu Wang ... Tamas B. Fehér Christoph Angerer Martin Steinegger D. Bhowmik B. Rost DRL 80 956 0 13 Jul 2020
How Powerful Are Randomly Initialized Pointcloud Set Functions? Aditya Sanghi P. Jayaraman 3DPC 46 3 0 11 Mar 2020
On the impressive performance of randomly weighted encoders in summarization tasks Jonathan Pilault Jaehong Park C. Pal BDL 72 5 0 21 Feb 2020
J-Net: Randomly weighted U-Net for audio source separation Bo Chen Yen-Min Hsu Hung-yi Lee 50 2 0 29 Nov 2019
Do NLP Models Know Numbers? Probing Numeracy in Embeddings Eric Wallace Yizhong Wang Sujian Li Sameer Singh Matt Gardner 75 265 0 17 Sep 2019
Evaluating Protein Transfer Learning with TAPE Roshan Rao Nicholas Bhattacharya Neil Thomas Yan Duan Xi Chen John F. Canny Pieter Abbeel Yun S. Song SSL 105 809 0 19 Jun 2019
Learning protein sequence embeddings using information from structure Tristan Bepler Bonnie Berger 85 290 0 22 Feb 2019
No Training Required: Exploring Random Encoders for Sentence Classification John Wieting Douwe Kiela 82 99 0 29 Jan 2019
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 1.1K 7,201 0 20 Apr 2018
A Powerful Generative Model Using Random Weights for the Deep Image Representation Kun He Yan Wang John E. Hopcroft 116 78 0 15 Jun 2016

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