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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 LeeJustin GilmerSimon 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...
2311.17932.pdf	Generating Molecular Conformer Fields Yuyang Wang1Ahmed A. Elhag1Navdeep Jaitly1Joshua M. Susskind1Miguel Angel Bautista1 Abstract In this paper we tackle the problem of generat- ing conformers of a molecule in 3D space given its molecular graph. We parameterize these con- formers as continuous functions that map ele- ...
2305.15076.pdf	Meta-Learning Online Adaptation of Language Models Nathan Hu* Eric Mitchell* Christopher D. Manning Chelsea Finn Stanford University Abstract Large language models encode impressively broad world knowledge in their parameters. However, the knowledge in static language models falls out of date, limiting the model’s effe...
2102.03902.pdf	Nystr ¨omformer: A Nystr ¨om-based Algorithm for Approximating Self-Attention Yunyang Xiong1Zhanpeng Zeng1Rudrasis Chakraborty2Mingxing Tan3 Glenn Fung4Yin Li1Vikas Singh1 1University of Wisconsin-Madison2UC Berkeley3Google Brain4American Family Insurance yxiong43@wisc.edu, zzeng38@wisc.edu, rudra@berkeley.edu, tanming...
2310.07820.pdf	Large Language Models Are Zero-Shot Time Series Forecasters Nate Gruver∗ NYUMarc Finzi∗ CMUShikai Qiu∗ NYUAndrew Gordon Wilson NYU Abstract By encoding time series as a string of numerical digits, we can frame time series forecasting as next-token prediction in text. Developing this approach, we find that large languag...
2211.10438.pdf	SmoothQuant: Accurate and Efﬁcient Post-Training Quantization for Large Language Models Guangxuan Xiao* 1Ji Lin* 1Mickael Seznec2Hao Wu2Julien Demouth2Song Han1 Abstract Large language models (LLMs) show excel- lent performance but are compute- and memory- intensive. Quantization can reduce memory and accelerate infere...
2009.14794.pdf	Published as a conference paper at ICLR 2021 RETHINKING ATTENTION WITH PERFORMERS Krzysztof Choromanski∗1, Valerii Likhosherstov∗2, David Dohan∗1, Xingyou Song∗1 Andreea Gane∗1, Tamas Sarlos∗1, Peter Hawkins∗1, Jared Davis∗3, Afroz Mohiuddin1 Lukasz Kaiser1, David Belanger1, Lucy Colwell1,2, Adrian Weller2,4 1Google2Un...
2305.19466.pdf	The Impact of Positional Encoding on Length Generalization in Transformers Amirhossein Kazemnejad1,2, Inkit Padhi3 Karthikeyan Natesan Ramamurthy3,Payel Das3,Siva Reddy1,2,4 1Mila - Québec AI Institute;2McGill University; 3IBM Research;4Facebook CIFAR AI Chair {amirhossein.kazemnejad,siva.reddy}@mila.quebec inkpad@ibm....
10.1093.molbev.msx095.pdf	Inference of Epistatic Effects Leading to Entrenchment and Drug Resistance in HIV-1 Protease William F. Flynn,1,2Allan Haldane,2,3Bruce E. Torbett,4and Ronald M. Levy*,2,3 1Department of Physics and Astronomy, Ru tgers University, New Brunswick, NJ 2Center for Biophysics and Computational Bio logy, Temple University, P...
2202.01169.pdf	UNIFIED SCALING LAWS FOR ROUTED LANGUAGE MODELS Aidan Clark∗, Diego de las Casas∗, Aurelia Guy∗, Arthur Mensch∗ Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake Hechtman‡, Trevor Cai, Sebastian Borgeaud, George van den Driessche, Eliza Rutherford, Tom Hennigan, Matthew Johnson‡, Katie Millican, Albin Cassirer, Ch...
2304.10970.pdf	Can GPT-4 Perform Neural Architecture Search? Mingkai Zheng1,3Xiu Su1Shan You2Fei Wang2 Chen Qian2Chang Xu1Samuel Albanie3 1The University of Sydney2SenseTime Research3CAML Lab, University of Cambridge mingkaizheng@outlook.com ,xisu5992@uni.sydney.edu.au, {youshan,wangfei,qianchen}@sensetime.com ,c.xu@sydney.edu.au sam...
2205.11487.pdf	Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Chitwan Saharia∗, William Chan∗, Saurabh Saxena†, Lala Li†, Jay Whang†, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho†, David J Fleet†, Mohammad Norouzi∗ {sa...
2310.08118.pdf	Can Large Language Models Really Improve by Self-critiquing Their Own Plans? Karthik Valmeekam∗ School of Computing & AI Arizona State University Tempe. kvalmeek@asu.eduMatthew Marquez∗ School of Computing & AI Arizona State University, Tempe. mmarqu22@asu.edu Subbarao Kambhampati School of Computing & AI Arizona State...
Bradley-RankAnalysisIncomplete-1952.pdf	Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons Author(s): Ralph Allan Bradley and Milton E. Terry Source: Biometrika , Dec., 1952 , Vol. 39, No. 3/4 (Dec., 1952), pp. 324-345 Published by: Oxford University Press on behalf of Biometrika Trust Stable URL: http://www.jstor.com/stab...
2305.14224.pdf	mmT5: Modular Multilingual Pre-Training Solves Source Language Hallucinations Jonas Pfeiffer Francesco Piccinno Massimo Nicosia Xinyi Wang Machel Reid Sebastian Ruder Google DeepMind Abstract Multilingual sequence-to-sequence models per- form poorly with increased language coverage and fail to consistently generate tex...
Hastings1970.pdf	Monte Carlo Sampling Methods Using Markov Chains and Their Applications W. K. Hastings Biometrika , Vol. 57, No. 1. (Apr., 1970), pp. 97-109. Stable URL: http://links.jstor.org/sici?sici=0006-3444%28197004%2957%3A1%3C97%3AMCSMUM%3E2.0.CO%3B2-C Biometrika is currently published by Biometrika Trust. Your use of the JSTOR...
2109.10862v2.pdf	Recursively Summarizing Books with Human Feedback Jeff Wu∗Long Ouyang∗Daniel M. Ziegler∗Nisan Stiennon∗Ryan Lowe∗ Jan Leike∗Paul Christiano∗ OpenAI Abstract A major challenge for scaling machine learning is training models to perform tasks that are very difﬁcult or time-consuming for humans to evaluate. We present prog...
2303.02535.pdf	Streaming Active Learning with Deep Neural Networks Akanksha Saran1Safoora Yousefi2Akshay Krishnamurthy1John Langford1Jordan T. Ash1 Abstract Active learning is perhaps most naturally posed as an online learning problem. However, prior active learning approaches with deep neural networks assume offline access to the en...
rules_of_ml.pdf	Rules of Machine Learning: Best Practices for ML Engineering Martin Zinkevich This document is intended to help those with a basic knowledge of machine learning get the benefit of best practices in machine learning from around Google. It p...
stochastic-backprop-and-approximate-inference.pdf	Stochastic Backpropagation and Approximate Inference in Deep Generative Models Danilo J. Rezende, Shakir Mohamed, Daan Wierstra {danilor, shakir, daanw }@google.com Google DeepMind, London Abstract We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directe...
10.1016.j.cell.2023.12.026.pdf	Article Immune evasion, infectivity, and fusogenicity of SARS-CoV-2 BA.2.86 and FLip variants Graphical abstract Highlights dBA.2.86 is less immune evasive compared to FLip and other XBB variants dBA.2.86 is antigenically more similar to BA.2 and BA.4/5 thanXBB variants dMAb S309 is unable to neutralize BA.2.86 possibl...
10.1016.j.cell.2023.12.032.pdf	Article DNA-guided transcription factor cooperativity shapes face and limb mesenchyme Graphical abstract Highlights dMutually dependent binding of TWIST1 and homeodomain TFs in embryonic mesenchyme dTF co-binding drives enhancer accessibility and sharedtranscriptional regulation dWeak TF-TF contacts guided by DNA media...
10.1101.2023.04.30.538439.pdf	scGPT: Towards Building a Foundation Model for Single-Cell 1 Multi-omics Using Generative AI 2 Haotian Cui1,2,3 ∗, Chloe Wang1,2,3∗, Hassaan Maan1,3,4, Bo Wang1,2,3,4,5 †3 1Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada 4 2Department of Computer Science, University of Toronto, Toronto, ON, Ca...
56-preference-proxies-evaluating-.pdf	Preference Proxies: Evaluating Large Language Models in capturing Human Preferences in Human-AI Tasks Mudit Verma* 1Siddhant Bhambri* 1Subbarao Kambhampati1 Abstract In this work, we investigate the potential of Large Language Models (LLMs) to serve as effective human proxies by capturing human preferences in the conte...
10.1038.s41586-019-1923-7.pdf	706 \| Nature \| Vol 577 \| 30 January 2020 ArticleImproved protein structure prediction using potentials from deep learning Andrew W. Senior1,4*, Richard Evans1,4, John Jumper1,4, James Kirkpatrick1,4, Laurent Sifre1,4, Tim Green1, Chongli Qin1, Augustin Žídek1, Alexander W. R. Nelson1, Alex Bridgland1, Hugo Penedone...
2211.17192.pdf	Fast Inference from Transformers via Speculative Decoding Yaniv Leviathan* 1Matan Kalman* 1Yossi Matias1 Abstract Inference from large autoregressive models like Transformers is slow - decoding Ktokens takes Kserial runs of the model. In this work we in- troduce speculative decoding - an algorithm to sample from autore...
MLSB2021-Deep-generative-models-create.pdf	Deep generative models create new and diverse protein structures Zeming Lin NYU & FAIR zl2799@nyu.edu,zlin@fb.comTom Sercu FAIR tsercu@fb.comYann LeCun NYU & FAIR yann@nyu.edu,yann@fb.com Alexander Rives FAIR arives@fb.com Abstract We explore the use of modern variational autoencoders for generating protein structures....
10.1101.2024.03.21.585615.pdf	Engineeringhighlyactiveanddiversenuclease enzymesbycombiningmachinelearningand ultra-high-throughputscreening NeilThomas,1,DavidBelanger,2,ChenlingXu3,HansonLee3,KathleenHirano3,KosukeIwai3, VanjaPolic3,KendraDNyberg3,KevinHoff3,LucasFrenz3,CharlieAEmrich1,JunWKim1, MariyaChavarha4,AbiRamanan1,JeremyJAgresti3,LucyJCo...
2112.04426.pdf	Improving language models by retrieving from trillions of tokens Sebastian Borgeaudy, Arthur Menschy, Jordan Hoﬀmanny, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego de Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saﬀron...
10.1038.s41586-023-06291-2.pdf	Nature \| www.nature.com \| 1 ArticleLarge language models encode clinical knowledge Karan Singhal1,4 ✉, Shekoofeh Azizi1,4 ✉, Tao Tu1,4, S. Sara Mahdavi1, Jason Wei1, Hyung Won Chung1, Nathan Scales1, Ajay Tanwani1, Heather Cole-Lewis1, Stephen Pfohl1, Perry Payne1, Martin Seneviratne1, Paul Gamble1, Chris Kelly1, Ab...
NeurIPS-2020-learning-to-summarize-with-human-feedback-Paper.pdf	Learning to summarize from human feedback Nisan Stiennon∗Long Ouyang∗Jeff Wu∗Daniel M. Ziegler∗Ryan Lowe∗ Chelsea Voss∗Alec Radford Dario Amodei Paul Christiano∗ OpenAI Abstract As language models become more powerful, training and evaluation are increas- ingly bottlenecked by the data and metrics used for a particular...
10.1038.s41467-024-46715-9.pdf	Article https://doi.org/10.1038/s41467-024-46715-9 High-throughput prediction of protein conformational distributions withsubsampled AlphaFold2 Gabriel Monteiro da Silva1,J e n n i f e rY .C u i1,D a v i dC .D a l g a r n o2, George P. Lisi1,3& Brenda M. Rubenstein1,3 This paper presents an innovative appro ach for pre...
2401.13660.pdf	MambaByte: Token-free Selective State Space Model Junxiong Wang Tushaar Gangavarapu Jing Nathan Yan Alexander M Rush Cornell University {jw2544,tg352,jy858,arush}@cornell.edu Abstract Token-free language models learn directly from raw bytes and remove the bias of subword tokenization. Operating on bytes, however, resul...
1905.13678.pdf	Learning Sparse Networks Using Targeted Dropout Aidan N. Gomez1,2,3Ivan Zhang2 Siddhartha Rao Kamalakara2Divyam Madaan2 Kevin Swersky1Yarin Gal3Geoffrey E. Hinton1 1Google Brain2for.ai3Department of Computer Science University of Oxford Abstract Neural networks are easier to optimise when they have many more weights th...
10.1101.2021.02.12.430858.pdf	MSA Transformer Roshan Rao1 2Jason Liu3Robert Verkuil3Joshua Meier3 John F. Canny1Pieter Abbeel1Tom Sercu3Alexander Rives3 4 Abstract Unsupervised protein language models trained across millions of diverse sequences learn struc- ture and function of proteins. Protein language models studied to date have been trained to...
1911.12360.pdf	Published as a conference paper at ICLR 2021 HOW MUCH OVER-PARAMETERIZATION ISSUFFI- CIENT TO LEARN DEEPRELU N ETWORKS ? Zixiang Chen:˚, Yuan Cao:˚, Difan Zou:˚, Quanquan Gu: :Department of Computer Science, University of California, Los Angles {chenzx19,yuancao,knowzou,qgu}@cs.ucla.edu ABSTRACT A recent line of resear...
2309.00754.pdf	EFFICIENT RLHF: R EDUCING THE MEMORY USAGE OF PPO Michael Santacroce, Yadong Lu, Han Yu, Yuanzhi Li, Yelong Shen Microsoft {misantac,yadonglu,hanyu,yuanzhili,yelong.shen}@microsoft.com ABSTRACT Reinforcement Learning with Human Feedback (RLHF) has revolutionized lan- guage modeling by aligning models with human prefere...
121-Testing-Manifold.pdf	JOURNAL OF THE AMERICAN MATHEMATICAL SOCIETY Volume 29, Number 4, October 2016, Pages 983–1049 http://dx.doi.org/10.1090/jams/852Article electronically published on February 9, 2016 TESTING THE MANIFOLD HYPOTHESIS CHARLES FEFFERMAN, SANJOY MITTER, AND HARIHARAN NARAYANAN Contents 1. Introduction 984 1.1. Deﬁnitions 988...
Moving-structural-biology-forward-together-cell.pdf	Leading Edge Editorial Moving structural biology forward together The ﬁeld of structural biology has undergone revolutions in the past decades. Technological advances have pushed the bound-aries of what is possible. With that, structural biologists today can solve more physiologically relevant structures than they coul...
2303.11366.pdf	Reflexion: Language Agents with Verbal Reinforcement Learning Noah Shinn Northeastern University noahshinn024@gmail.comFederico Cassano Northeastern University cassano.f@northeastern.edu Edward Berman Northeastern University berman.ed@northeastern.eduAshwin Gopinath Massachusetts Institute of Technology agopi@mit.edu K...
2203.15556.pdf	Training Compute-Optimal Large Language Models Jordan Hoﬀmann★, Sebastian Borgeaud★, Arthur Mensch★, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simo...
2304.15004.pdf	Are Emergent Abilities of Large Language Models a Mirage? Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo Computer Science, Stanford University Abstract Recent work claims that large language models display emergent abilities , abil- ities not present in smaller-scale models that are present in larger-scale models. W...
2309.01933.pdf	PROVABLY SAFE SYSTEMS : THE ONLY PATH TO CONTROLLABLE AGI Max Tegmark Department of Physics Insitute for AI & Fundamental Interactions Massachusetts Institute of Technology Cambridge, MA 02139 Steve Omohundro Beneficial AI Research Palo Alto, CA 94301 September 6, 2023 ABSTRACT We describe a path to humanity safely thr...
few-shot-clustering.pdf	Large Language Models Enable Few-Shot Clustering Vijay Viswanathan1, Kiril Gashteovski2, Carolin Lawrence2, Tongshuang Wu1, Graham Neubig1, 3 1Carnegie Mellon University,2NEC Laboratories Europe,3Inspired Cognition Abstract Unlike traditional unsupervised clustering, semi-supervised clustering allows users to pro- vide...
10.1038.s41586-019-1724-z.pdf	350 \| Nature \| Vol 575 \| 14 November 2019 ArticleGrandmaster level in StarCraft II using multi-agent reinforcement learning Oriol Vinyals1,3*, Igor Babuschkin1,3, Wojciech M. Czarnecki1,3, Michaël Mathieu1,3, Andrew Dudzik1,3, Junyoung Chung1,3, David H. Choi1,3, Richard Powell1,3, Timo Ewalds1,3, Petko Georgiev1...
2401.04056.pdf	A Minimaximalist Approach to Reinforcement Learning from Human Feedback Gokul Swamy1 *Christoph Dann2Rahul Kidambi2Zhiwei Steven Wu1Alekh Agarwal2 Abstract We present Self-Play Preference Optimization (SPO), an algorithm for reinforcement learning from human feedback. Our approach is minimal- istin that it does not req...
2301.11325.pdf	MusicLM: Generating Music From Text Andrea Agostinelli* 1Timo I. Denk* 1 Zal´an Borsos1Jesse Engel1Mauro Verzetti1Antoine Caillon2Qingqing Huang1Aren Jansen1 Adam Roberts1Marco Tagliasacchi1Matt Shariﬁ1Neil Zeghidour1Christian Frank1 Abstract We introduce MusicLM, a model for generating high-ﬁdelity music from text des...

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