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What is a meaningful representation of protein sequences?

What is a meaningful representation of protein sequences?

28 November 2020
N. Detlefsen
Søren Hauberg
Wouter Boomsma
ArXivPDFHTML

Papers citing "What is a meaningful representation of protein sequences?"

25 / 25 papers shown
Title
Interpreting Equivariant Representations
Interpreting Equivariant Representations
Andreas Abildtrup Hansen
Anna Calissano
Aasa Feragen
93
1
0
23 Jan 2024
ProtTrans: Towards Cracking the Language of Life's Code Through
  Self-Supervised Deep Learning and High Performance Computing
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
62
947
0
13 Jul 2020
BERTology Meets Biology: Interpreting Attention in Protein Language
  Models
BERTology Meets Biology: Interpreting Attention in Protein Language Models
Jesse Vig
Ali Madani
Lav Varshney
Caiming Xiong
R. Socher
Nazneen Rajani
71
293
0
26 Jun 2020
ProGen: Language Modeling for Protein Generation
ProGen: Language Modeling for Protein Generation
Ali Madani
Bryan McCann
Nikhil Naik
N. Keskar
N. Anand
Raphael R. Eguchi
Po-Ssu Huang
R. Socher
66
283
0
08 Mar 2020
Variational Autoencoders with Riemannian Brownian Motion Priors
Variational Autoencoders with Riemannian Brownian Motion Priors
Dimitris Kalatzis
David Eklund
Georgios Arvanitidis
Søren Hauberg
BDL
DRL
117
49
0
12 Feb 2020
Pre-Training of Deep Bidirectional Protein Sequence Representations with
  Structural Information
Pre-Training of Deep Bidirectional Protein Sequence Representations with Structural Information
Seonwoo Min
Seunghyun Park
Siwon Kim
Hyun-Soo Choi
Byunghan Lee
Sungroh Yoon
SSL
46
62
0
25 Nov 2019
RoBERTa: A Robustly Optimized BERT Pretraining Approach
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Yinhan Liu
Myle Ott
Naman Goyal
Jingfei Du
Mandar Joshi
Danqi Chen
Omer Levy
M. Lewis
Luke Zettlemoyer
Veselin Stoyanov
AIMat
653
24,464
0
26 Jul 2019
Evaluating Protein Transfer Learning with TAPE
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
91
796
0
19 Jun 2019
Reliable training and estimation of variance networks
Reliable training and estimation of variance networks
N. Detlefsen
Martin Jørgensen
Søren Hauberg
UQCV
89
89
0
04 Jun 2019
Learning protein sequence embeddings using information from structure
Learning protein sequence embeddings using information from structure
Tristan Bepler
Bonnie Berger
73
290
0
22 Feb 2019
Continuous Hierarchical Representations with Poincaré Variational
  Auto-Encoders
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders
Emile Mathieu
Charline Le Lan
Chris J. Maddison
Ryota Tomioka
Yee Whye Teh
BDL
DRL
74
178
0
17 Jan 2019
BERT: Pre-training of Deep Bidirectional Transformers for Language
  Understanding
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Kristina Toutanova
VLM
SSL
SSeg
1.8K
94,891
0
11 Oct 2018
Explorations in Homeomorphic Variational Auto-Encoding
Explorations in Homeomorphic Variational Auto-Encoding
Luca Falorsi
P. D. Haan
Tim R. Davidson
Nicola De Cao
Maurice Weiler
Patrick Forré
Taco S. Cohen
BDL
DRL
96
116
0
12 Jul 2018
3D Steerable CNNs: Learning Rotationally Equivariant Features in
  Volumetric Data
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Maurice Weiler
Mario Geiger
Max Welling
Wouter Boomsma
Taco S. Cohen
3DPC
108
505
0
06 Jul 2018
Only Bayes should learn a manifold (on the estimation of differential
  geometric structure from data)
Only Bayes should learn a manifold (on the estimation of differential geometric structure from data)
Søren Hauberg
55
33
0
13 Jun 2018
Hyperspherical Variational Auto-Encoders
Hyperspherical Variational Auto-Encoders
Tim R. Davidson
Luca Falorsi
Nicola De Cao
Thomas Kipf
Jakub M. Tomczak
DRL
BDL
104
385
0
03 Apr 2018
Intertwiners between Induced Representations (with Applications to the
  Theory of Equivariant Neural Networks)
Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks)
Taco S. Cohen
Mario Geiger
Maurice Weiler
60
47
0
28 Mar 2018
Latent Space Oddity: on the Curvature of Deep Generative Models
Latent Space Oddity: on the Curvature of Deep Generative Models
Georgios Arvanitidis
Lars Kai Hansen
Søren Hauberg
DRL
107
270
0
31 Oct 2017
Attention Is All You Need
Attention Is All You Need
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan Gomez
Lukasz Kaiser
Illia Polosukhin
3DV
701
131,652
0
12 Jun 2017
DeepSF: deep convolutional neural network for mapping protein sequences
  to folds
DeepSF: deep convolutional neural network for mapping protein sequences to folds
Jie Hou
B. Adhikari
Jianlin Cheng
63
200
0
04 Jun 2017
Dilated Residual Networks
Dilated Residual Networks
Feng Yu
V. Koltun
Thomas Funkhouser
MedIm
121
1,617
0
28 May 2017
Deep Residual Learning for Image Recognition
Deep Residual Learning for Image Recognition
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
MedIm
2.2K
194,020
0
10 Dec 2015
Metrics for Probabilistic Geometries
Metrics for Probabilistic Geometries
A. Tosi
Søren Hauberg
A. Vellido
Neil D. Lawrence
79
62
0
27 Nov 2014
Efficient Estimation of Word Representations in Vector Space
Efficient Estimation of Word Representations in Vector Space
Tomas Mikolov
Kai Chen
G. Corrado
J. Dean
3DV
677
31,512
0
16 Jan 2013
Representation Learning: A Review and New Perspectives
Representation Learning: A Review and New Perspectives
Yoshua Bengio
Aaron Courville
Pascal Vincent
OOD
SSL
261
12,439
0
24 Jun 2012
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