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2203.14263.pdf | 1
A General Survey on Attention Mechanisms in
Deep Learning
Gianni Brauwers and Flavius Frasincar
Abstract —Attention is an important mechanism that can be employed for a variety of deep learning models across many different
domains and tasks. This survey provides an overview of the most important attention mechanisms ... |
2210.00312.pdf | Published as a conference paper at ICLR 2023
MULTIMODAL ANALOGICAL REASONING OVER
KNOWLEDGE GRAPHS
Ningyu Zhang1∗Lei Li1∗Xiang Chen1∗Xiaozhuan Liang1Shumin Deng2Huajun Chen1†
1Zhejiang University, AZFT Joint Lab for Knowledge Engine
2National University of Singapore
{zhangningyu,leili21,xiang chen,liangxiaozhuan,231sm,... |
2310.12397.pdf | GPT-4 Doesn’t Know It’s Wrong: An Analysis of
Iterative Prompting for Reasoning Problems
Kaya Stechly∗Matthew Marquez∗Subbarao Kambhampati∗
Abstract
There has been considerable divergence of opinion on the reasoning abilities
of Large Language Models (LLMs). While the initial optimism that reasoning
might emerge automa... |
2309.14322.pdf | Small-scale proxies for large-scale Transformer training instabilities
Mitchell Wortsman Peter J. Liu Lechao Xiao Katie Everett
Alex Alemi Ben Adlam John D. Co-Reyes Izzeddin Gur Abhishek Kumar
Roman Novak Jeffrey Pennington Jascha Sohl-dickstein Kelvin Xu
Jaehoon Lee*Justin Gilmer*Simon Kornblith*
Google DeepMind
Abst... |
2308.05660.pdf | Thermodynamic Linear Algebra
Maxwell Aifer, Kaelan Donatella, Max Hunter Gordon,
Thomas Ahle, Daniel Simpson, Gavin Crooks, Patrick J. Coles
Normal Computing Corporation, New York, New York, USA
Linear algebraic primitives are at the core of many modern algorithms in engineering, science, and
machine learning. Hence, a... |
2309.10150.pdf | Q-Transformer: Scalable Offline Reinforcement
Learning via Autoregressive Q-Functions
Yevgen Chebotar∗, Quan Vuong∗, Alex Irpan, Karol Hausman, Fei Xia, Yao Lu, Aviral Kumar,
Tianhe Yu, Alexander Herzog, Karl Pertsch, Keerthana Gopalakrishnan, Julian Ibarz, Ofir Nachum,
Sumedh Sontakke, Grecia Salazar, Huong T Tran, Jo... |
2109.01652.pdf | Published as a conference paper at ICLR 2022
FINETUNED LANGUAGE MODELS AREZERO-SHOT
LEARNERS
Jason Wei∗, Maarten Bosma∗, Vincent Y. Zhao∗, Kelvin Guu∗, Adams Wei Yu,
Brian Lester, Nan Du, Andrew M. Dai, and Quoc V . Le
Google Research
ABSTRACT
This paper explores a simple method for improving the zero-shot learning abi... |
1610.06258.pdf | Using Fast Weights to Attend to the Recent Past
Jimmy Ba
University of Toronto
jimmy@psi.toronto.eduGeoffrey Hinton
University of Toronto and Google Brain
geoffhinton@google.com
Volodymyr Mnih
Google DeepMind
vmnih@google.comJoel Z. Leibo
Google DeepMind
jzl@google.comCatalin Ionescu
Google DeepMind
cdi@google.com
Abst... |
sciadv.adn0042.pdf | Hikichi et al., Sci. Adv. 10, eadn0042 (2024) 1 March 2024
Science Adv AnceS | ReSeAR cH AR ticle
1 of 20VIROLOGY
Epistatic pathways can drive HIV- 1 escape from
integrase strand transfer inhibitors
Yuta Hikichi1, Jonathan R. Grover2, Alicia Schäfer2, Walther Mothes2, Eric O. Freed1*
People living with human immu... |
10.1016.j.cell.2023.12.034.pdf | Leading Edge
Commentary
Enabling structure-based drug discovery
utilizing predicted models
Edward B. Miller,1,*Howook Hwang,1Mee Shelley,2Andrew Placzek,2Joa˜o P.G.L.M. Rodrigues,1Robert K. Suto,3
Lingle Wang,1Karen Akinsanya,1and Robert Abel1
1Schro ¨dinger New York, 1540 Broadway, 24th Floor, New York, NY 10036, USA
... |
1805.02867.pdf | arXiv:1805.02867v2 [cs.PF] 28 Jul 2018Online normalizer calculation for softmax
Maxim Milakov
NVIDIA
mmilakov@nvidia.comNatalia Gimelshein
NVIDIA
ngimelshein@nvidia.com
Abstract
The Softmax function is ubiquitous in machine learning, mul tiple previous works
suggested faster alternatives for it. In this paper we prop... |
10.1101.2024.01.02.573943.pdf | De Novo Atomic Protein Structure Modeling for Cryo-EM
Density Maps Using 3D Transformer and Hidden Markov
Model
Nabin Giri1,2and Jianlin Cheng1,2*
1Electrical Engineering and Computer Science, University of Missouri, Columbia, 65211,
Missouri, USA.
2NextGen Precision Health Institute, University of Missouri, Columbia, ... |
score-matching-denoising.pdf | 1
A Connection Between Score Matching
and Denoising Autoencoders
Pascal Vincent
vincentp@iro.umontreal.ca
Dept. IRO, Université de Montréal,
CP 6128, Succ. Centre-Ville, Montréal (QC) H3C 3J7, Canada.
Technical Report 1358
Département d’Informatique et de Recherche Opérationnelle
December 2010
THIS IS A PREPRINT VERSIO... |
2202.08371.pdf | arXiv:2202.08371v1 [cs.LG] 15 Feb 2022THE QUARKS OF ATTENTION
PIERRE BALDI AND ROMAN VERSHYNIN
Abstract. Attention plays a fundamental role in both natural and artifi cial intelligence
systems. In deep learning, attention-based neural archite ctures, such as transformer archi-
tectures, are widely used to tackle probl... |
2404.12358.pdf | Preprint
From rtoQ∗: Your Language Model is Secretly a Q-Function
Rafael Rafailov*
Stanford University
rafailov@stanford.eduJoey Hejna*
Stanford University
jhejna@stanford.eduRyan Park
Stanford University
rypark@stanford.edu
Chelsea Finn
Stanford University
cbfinn@stanford.edu
Abstract
Reinforcement Learning From Human... |
2112.07868.pdf | Few-shot Instruction Prompts for Pretrained Language Models to Detect
Social Biases
Shrimai Prabhumoye1, Rafal Kocielnik2, Mohammad Shoeybi1,
Anima Anandkumar1,2, Bryan Catanzaro1
1NVIDIA,2California Institute of Technology
{sprabhumoye@nvidia.com, rafalko@caltech.edu}
Abstract
Warning: this paper contains content that... |
2101.03288.pdf | How to Train Your Energy-Based Models
Yang Song yangsong@cs.stanford.edu
Stanford University
Diederik P. Kingma dpkingma@google.com
Google Research
Abstract
Energy-Based Models (EBMs), also known as non-normalized probabilistic models, specify
probability density or mass functions up to an unknown normalizing constant.... |
2303.07487v2.pdf | Using VAEs to Learn Latent Variables: Observations on
Applications in cryo-EM
Edelberg, Daniel G.
Yale UniversityLederman, Roy R.
Yale University
May 12, 2023
Abstract
Variational autoencoders (VAEs) are a popular generative model used to approximate distributions.
The encoder part of the VAE is used in amortized learn... |
2205.12365.pdf | Low-rank Optimal Transport:
Approximation, Statistics and Debiasing
Meyer Scetbon
CREST, ENSAE
meyer.scetbon@ensae.frMarco Cuturi
Apple and CREST, ENSAE
cuturi@apple.com
Abstract
The matching principles behind optimal transport (OT) play an increasingly impor-
tant role in machine learning, a trend which can be observe... |
2207.06569.pdf | Benign, Tempered, or Catastrophic:
A Taxonomy of Over/f_itting
Neil Mallinar∗
UC San Diego
nmallina@ucsd.eduJames B. Simon∗
UC Berkeley
james.simon@berkeley.eduAmirhesam Abedsoltan
UC San Diego
aabedsoltan@ucsd.edu
Parthe Pandit
UC San Diego
parthepandit@ucsd.eduMikhail Belkin
UC San Diego
mbelkin@ucsd.eduPreetum Nakki... |
1909.08593v2.pdf | Fine-Tuning Language Models from Human Preferences
Daniel M. Ziegler∗Nisan Stiennon∗Jeffrey Wu Tom B. Brown
Alec Radford Dario Amodei Paul Christiano Geoffrey Irving
OpenAI
{dmz,nisan,jeffwu,tom,alec,damodei,paul,irving}@openai.com
Abstract
Reward learning enables the application of rein-
forcement learning (RL) to tas... |
1406.2661.pdf | Generative Adversarial Nets
Ian J. Goodfellow, Jean Pouget-Abadie∗, Mehdi Mirza, Bing Xu, David Warde-Farley,
Sherjil Ozair†, Aaron Courville, Yoshua Bengio‡
D´epartement d’informatique et de recherche op ´erationnelle
Universit ´e de Montr ´eal
Montr ´eal, QC H3C 3J7
Abstract
We propose a new framework for estimating ... |
2402.10171.pdf | Data Engineering for Scaling Language Models to 128K Context
Yao FuκRameswar PandaηXinyao NiuµXiang YueπHannaneh HajishirziσYoon KimλHao Pengδ
κUniversity of EdinburghηMIT-IBM Watson AI LabµUniversity of MelbourneπOhio State University
σUniversity of WashingtonλMITδUIUC
yao.fu@ed.ac.uk yoonkim@mit.edu haopeng@illinois.... |
2402.03175v1.pdf | 1
THEMATRIX : A B AYESIAN LEARNING MODEL FOR LLM S
Siddhartha Dalal
Department of Statistics
Columbia University
The City of New York
sd2803@columbia.eduVishal Misra
Department of Computer Science
Columbia University
The City of New York
vishal.misra@columbia.edu
ABSTRACT
In this paper, we introduce a Bayesian learning... |
2402.04845.pdf | AlphaFold Meets Flow Matching for Generating Protein Ensembles
Bowen Jing1Bonnie Berger1 2Tommi Jaakkola1
Abstract
The biological functions of proteins often de-
pend on dynamic structural ensembles. In this
work, we develop a flow-based generative mod-
eling approach for learning and sampling the
conformational landsc... |
1506.00552.pdf | Coordinate Descent Converges Faster with the
Gauss-Southwell Rule Than Random Selection
Julie Nutini1, Mark Schmidt1, Issam H. Laradji1, Michael Friedlander2, Hoyt Koepke3
1University of British Columbia,2University of California, Davis,3Dato
Abstract
There has been significant recent work on the theory and application ... |
10.1016.j.acha.2021.12.009.pdf | Appl. Comput. Harmon. Anal. 59 (2022) 85–116
Contents lists available at ScienceDirect
Applied and Computational Harmonic Analysis
www.elsevier.com/locate/acha
Loss landscapes and optimization in over-parameterized
non-linear systems and neural networks
Chaoyue Liua, Libin Zhub,c, Mikhail Belkinc,∗
aDepar... |
2309.02390.pdf | 5 September 2023
Explaining grokking through circuit efficiency
Vikrant Varma*, 1, Rohin Shah*, 1, Zachary Kenton1, János Kramár1and Ramana Kumar1
*Equal contributions,1Google DeepMind
One of the most surprising puzzles in neural network generalisation is grokking : a network with perfect
training accuracy but poor gen... |
10.1016.j.cell.2023.12.035.pdf | Article
Brain-wide neural activity underlying memory-
guided movement
Graphical abstract
Highlights
dAnatomy-guided activity recordings in multi-regional neural
circuits during behavior
dMovement encoding is strongest in the medulla, followed bythe midbrain and cortex
dChoice coding arises in a specific multi-regional c... |
2309.14525.pdf | Preprint
ALIGNING LARGE MULTIMODAL MODELS
WITH FACTUALLY AUGMENTED RLHF
Zhiqing Sun∗♠, Sheng Shen∗♣, Shengcao Cao∗♢
Haotian Liu♡, Chunyuan Li♮, Yikang Shen△, Chuang Gan†∇△, Liang-Yan Gui†♢
Yu-Xiong Wang†♢, Yiming Yang†♠, Kurt Keutzer†♣, Trevor Darrell†♣
♣UC Berkeley,♠CMU,♢UIUC,♡UW–Madison,∇UMass Amherst
♮Microsoft Rese... |
2306.12672.pdf | From Word Models to World Models:
Translating from Natural Language to the
Probabilistic Language of Thought
Lionel Wong1⋆, Gabriel Grand1⋆, Alexander K. Lew1, Noah D. Goodman2, Vikash K.
Mansinghka1, Jacob Andreas1, Joshua B. Tenenbaum1
⋆Equal contribution.
1MIT,2Stanford
Abstract
How does language inform our downstre... |
2210.17323.pdf | Published as a conference paper at ICLR 2023
GPTQ: A CCURATE POST-TRAINING QUANTIZATION
FOR GENERATIVE PRE-TRAINED TRANSFORMERS
Elias Frantar∗
IST AustriaSaleh Ashkboos
ETH ZurichTorsten Hoefler
ETH ZurichDan Alistarh
IST Austria & NeuralMagic
ABSTRACT
Generative Pre-trained Transformer models, known as GPT or OPT, set ... |
10.1016.j.cell.2024.01.026.pdf | Article
Cryo-EM structures of the plant plastid-encoded
RNA polymerase
Graphical abstract
Highlights
dPlant chloroplast RNA polymerase comprises a catalytic
core and four peripheral modules
dThe scaffold module stabilizes the catalytic core and bridgesother modules
dThe protection module has SOD activity, and the RNAmo... |
10.1038.s41467-021-26529-9.pdf | ARTICLE
The generative capacity of probabilistic protein
sequence models
Francisco McGee1,2,3, Sandro Hauri4,5, Quentin Novinger2,5, Slobodan Vucetic4,5, Ronald M. Levy1,3,6,7,
Vincenzo Carnevale2,3✉& Allan Haldane1,7✉
Potts models and variational autoencoders (VAEs) have recently gained popularity as gen-
erative prot... |
2205.11916.pdf | Large Language Models are Zero-Shot Reasoners
Takeshi Kojima
The University of Tokyo
t.kojima@weblab.t.u-tokyo.ac.jpShixiang Shane Gu
Google Research, Brain Team
Machel Reid
Google Research∗Yutaka Matsuo
The University of TokyoYusuke Iwasawa
The University of Tokyo
Abstract
Pretrained large language models (LLMs) are w... |
2308.06259v3.pdf | Published as a conference paper at ICLR 2024
SELF-ALIGNMENT WITH INSTRUCTION BACKTRANS -
LATION
Xian Li, Ping Yu, Chunting Zhou, Timo Schick, Omer Levy, Luke Zettlemoyer
Jason Weston &Mike Lewis
Meta
{xianl,jase,mikelewis}@meta.com
ABSTRACT
We present a scalable method to build a high quality instruction following lang... |
2209.12892.pdf | LEARNING TO LEARN WITH GENERATIVE MODELS OF
NEURAL NETWORK CHECKPOINTS
William Peebles∗Ilija Radosavovic∗Tim Brooks Alexei A. Efros Jitendra Malik
University of California, Berkeley
ABSTRACT
We explore a data-driven approach for learning to optimize neural networks. We
construct a dataset of neural network checkpoints ... |
2023.findings-acl.426.pdf | Findings of the Association for Computational Linguistics: ACL 2023 , pages 6810–6828
July 9-14, 2023 ©2023 Association for Computational Linguistics
“Low-Resource” Text Classification: A Parameter-Free Classification
Method with Compressors
Zhiying Jiang1,2, Matthew Y.R. Yang1, Mikhail Tsirlin1,
Raphael Tang1, Yiqin D... |
1911.00172.pdf | Published as a conference paper at ICLR 2020
GENERALIZATION THROUGH MEMORIZATION :
NEAREST NEIGHBOR LANGUAGE MODELS
Urvashi Khandelwal†∗, Omer Levy‡, Dan Jurafsky†, Luke Zettlemoyer‡& Mike Lewis‡
†Stanford University
‡Facebook AI Research
{urvashik,jurafsky }@stanford.edu
{omerlevy,lsz,mikelewis }@fb.com
ABSTRACT
We in... |
2024.03.18.585544v1.full.pdf | 1
Towards Interpretable Cryo-EM: Disentangling
Latent Spaces of Molecular Conformations
David A. Klindt1,2,∗, Aapo Hyv ¨arinen3, Axel Levy1,4, Nina Miolane2and
Fr´ed´eric Poitevin1
1LCLS, SLAC National Accelerator Laboratory, Stanford University, CA, USA
2Department of Electrical and Computer Engineering, UCSB, CA, USA... |
2309.03649.pdf | Exploring kinase DFG loop conformational
stability with AlphaFold2-RAVE
Bodhi P. Vani,†Akashnathan Aranganathan,‡and Pratyush Tiwary∗,¶,§
†Institute for Physical Science and Technology, University of Maryland, College Park,
Maryland 20742, USA
‡Biophysics Program and Institute for Physical Science and Technology, Unive... |
NIPS-2007-active-preference-learning-with-discrete-choice-data-Paper.pdf | Active Preference Learning with Discrete Choice Data
Eric Brochu, Nando de Freitas and Abhijeet Ghosh
Department of Computer Science
University of British Columbia
Vancouver, BC, Canada
{ebrochu, nando, ghosh}@cs.ubc.ca
Abstract
We propose an active learning algorithm that learns a continuous valuation model
from discr... |
2206.14858.pdf | Solving Quantitative Reasoning Problems with
Language Models
Aitor Lewkowycz∗, Anders Andreassen†, David Dohan†, Ethan Dyer†, Henryk Michalewski†,
Vinay Ramasesh†, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo,
Yuhuai Wu, Behnam Neyshabur∗, Guy Gur-Ari∗, and Vedant Misra∗
Google Research
Abstract
Language mo... |
1909.12264.pdf | Quantum Graph Neural Networks
Guillaume Verdon
X, The Moonshot Factory
Mountain View, CA
gverdon@x.teamTrevor McCourt
Google Research
Venice, CA
trevormccrt@google.com
Enxhell Luzhnica, Vikash Singh,
Stefan Leichenauer, Jack Hidary
X, The Moonshot Factory
Mountain View, CA
{enxhell,singvikash,
sleichenauer,hidary}@x.te... |
2403.08763.pdf | Simple and Scalable Strategies to Continually Pre-train
Large Language Models
Adam Ibrahim∗†⊚ibrahima@mila.quebec
Benjamin Thérien∗†⊚benjamin.therien@mila.quebec
Kshitij Gupta∗†⊚kshitij.gupta@mila.quebec
Mats L. Richter†⊚mats.richter@mila.quebec
Quentin Anthony♢†⊚qubitquentin@gmail.com
Timothée Lesort†⊚t.lesort@gmail.c... |
2310.02226.pdf | Think before you speak:
Training Language Models With Pause Tokens
Sachin Goyal∗
Machine Learning Department
Carnegie Mellon University
sachingo@andrew.cmu.eduZiwei Ji
Google Research, NY
ziweiji@google.comAnkit Singh Rawat
Google Research, NY
ankitsrawat@google.com
Aditya Krishna Menon
Google Research, NY
adityakmenon... |
2212.00178.pdf | Open Relation and Event Type Discovery with Type Abstraction
Sha Li, Heng Ji, Jiawei Han
University of Illinois Urbana-Champaign
{shal2, hengji, hanj}@illinois.edu
Abstract
Conventional “closed-world" information ex-
traction (IE) approaches rely on human ontolo-
gies to define the scope for extraction. As
a result, suc... |
10.1016.j.cell.2023.12.037.pdf | Article
Xist ribonucleoproteins promote female sex-biased
autoimmunity
Graphical abstract
Highlights
dTransgenic mouse models inducibly express Xist in male
animals
dXist expression in males induces autoantibodies andautoimmune pathology
dXist in males reprograms T and B cell populations to female-like patterns
dAutoan... |
2012.02296v2.pdf | Generative Capacity of Probabilistic Protein
Sequence Models
Francisco McGee1,2,4, Quentin Novinger2,5, Ronald M Levy1,3,4,6, Vincenzo Carnevale2,3,*,
and Allan Haldane1,6,*
1Center for Biophysics and Computational Biology, Temple University, Philadelphia, 19122, USA
2Institute for Computational Molecular Science, Temp... |
2401.00368.pdf | Improving Text Embeddings with
Large Language Models
Liang Wang∗, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei
Microsoft Corporation
https://aka.ms/GeneralAI
Abstract
In this paper, we introduce a novel and simple method for obtaining high-quality
text embeddings using only synthetic data and less t... |
More-Is-Different-Anderson.pdf | The reductionist hypothesis may still
lbe a topic for controversy among phi-
losophers, but among the great majority
of active scientists I think it is accepted
without question The workings of our
minds and bodles, and of all the ani-
mate or lnanimate matter of which we
have any detailed knowledges are as
sum... |
2002.11557v1.pdf | Query-Efficient Correlation Clustering
David García–Soriano
d.garcia.soriano@isi.it
ISI Foundation
Turin, ItalyKonstantin Kutzkov
kutzkov@gmail.com
Amalfi Analytics
Barcelona, Spain
Francesco Bonchi
francesco.bonchi@isi.it
ISI Foundation, Turin, Italy
Eurecat, Barcelona, SpainCharalampos Tsourakakis
ctsourak@bu.edu
Bos... |
10.1093.gbe.evad084.pdf | Unsupervised Deep Learning Can Identify Protein
Functional Groups from Unaligned Sequences
Kyle T. David
1,* and Kenneth M. Halanych
2
1Department of Biological Sciences, Auburn University, Auburn, Alabama, USA
2Center for Marine Sciences, University of North Carolina Wilmington, Wilmington, North Carolina, USA
*C... |
10.1038.s41467-024-46631-y.pdf | Article https://doi.org/10.1038/s41467-024-46631-y
Alignment of brain embeddings and arti ficial
contextual embeddings in natural languagepoints to common geometric patterns
Ariel Goldstein1,2, Avigail Grinstein-Dabush2,8, Mariano Schain2,8,
Haocheng Wang3, Zhuoqiao Hong3, Bobbi Aubrey3,4, Mariano Schain2,
Samuel A. Nas... |
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