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RQLLzMCefQu.pdf | 2,022 | 0 | provable rl with exogenous distractors via multistep inverse dynamics yonathan efroni1, dipendra misra1, akshay krishnamurthy1, alekh agarwal2 1microsoft research, new york, ny 2google †, john langford1 abstract many real-world applications of reinforcement learning (rl) require the agent to deal with high-dimensional ... | 6 | [
108,
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505.7435378,
193.1034166
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fPhKeld3Okz.pdf | 2,022 | 1 | gradient step denoiser for convergent plugand-play samuel hurault ∗, arthur leclaire & nicolas papadakis univ. bordeaux, bordeaux inp, cnrs, imb, umr 5251,f-33400 talence, france abstract plug-and-play (pnp) methods constitute a class of iterative algorithms for imaging problems where regularization is performed by an ... | 3 | [
108.299,
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NX1He-aFO_F.pdf | 2,021 | 1 | learning value functions in deep policy gradients using residual variance yannis flet-berliac∗ inria, scool team univ. lille, cristal, cnrs yannis.flet-berliac@inria.fr odalric-ambrym maillard inria, scool team reda ouhamma∗ inria, scool team univ. lille, cristal, cnrs reda.ouhamma@inria.fr philippe preux inria, scool ... | 1 | [
108.299,
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apv504XsysP.pdf | 2,022 | 2 | ab-initio potential energy surfaces pairing gnns with neural wave functions by nicholas gao & stephan g ¨unnemann department of informatics & munich data science institute technical university of munich, germany @in.tum.de gaoni,guennemann { abstract solving the schr¨odinger equation is key to many quantum mechanical p... | 2 | [
108.249,
172.1100784,
240.1607551,
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ZKy2X3dgPA.pdf | 2,022 | 1 | it takes two to tango: mixup for deep metric learning shashanka venkataramanan1∗ bill psomas3∗ konstantinos karantzalos3 ewa kijak1 yannis avrithis2 laurent amsaleg1 1inria, univ rennes, cnrs, irisa 2athena rc 3national technical university of athens abstract metric learning involves learning a discriminative represent... | 2 | [
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285.1006768,
284.7491658,
297.0558768
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ivwZO-HnzG_.pdf | 2,023 | 2 | recon: reducing conflicting gradients from the root for multi-task learning guangyuan shi, qimai li, wenlong zhang, jiaxin chen, xiao-ming wu(cid:12) department of computing, the hong kong polytechnic university, hong kong s.a.r., china {guang-yuan.shi, qee-mai.li, wenlong.zhang}@connect.polyu.hk, jiax.chen@connect.pol... | 2 | [
108.249,
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244.5652576,
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yqPnIRhHtZv.pdf | 2,021 | 2 | learning hyperbolic representations of topological features panagiotis kyriakis university of southern california los angeles, usa pkyriaki@usc.edu iordanis fostiropoulos university of southern california los angeles, usa fostirop@usc.edu paul bogdan university of southern california los angeles, usa pbogdan@usc.edu ab... | 6 | [
108.249,
448.6240784,
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E3Ys6a1NTGT.pdf | 2,021 | 0 | under review as a conference paper at iclr 2021 the importance of pessimism in fixed-dataset policy optimization anonymous authors paper under double-blind review abstract we study worst-case guarantees on the expected return of fixed-dataset policy optimization algorithms. our core contribution is a unified conceptual... | 9 | [
108,
549.4730784,
504.0033874,
559.4356784
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_4GFbtOuWq-.pdf | 2,022 | 1 | capacity of group-invariant linear readouts from equivariant representations: how many objects can be linearly classified under all possible views? matthew farrell∗‡, blake bordelon∗‡, shubhendu trivedi†, & cengiz pehlevan‡ ‡ harvard university {msfarrell,blake bordelon,cpehlevan}@seas.harvard.edu † massachusetts insti... | 2 | [
108.299,
156.2966768,
440.3236016,
168.2518768
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zEn1BhaNYsC.pdf | 2,023 | 1 | minimax optimal kernel operator learning via multilevel training jikai jin school of mathematical sciences peking university beijing, china jkjin@pku.edu.cn yiping lu institute for computational & mathematical engineering stanford university stanford, ca, us yplu@stanford.edu jose blanchet management science and engine... | 0 | [
126.82956,
288.1406768,
205.9888518,
300.0958768
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a4COps0uokg.pdf | 2,023 | 1 | user-interactive offline reinforcement learning phillip swazinna siemens & tu munich munich, germany swazinna@in.tum.de steffen udluft siemens technology munich, germany steffen.udluft@siemens.com thomas runkler siemens & tu munich munich, germany thomas.runkler@siemens.com abstract offline reinforcement learning algor... | 9 | [
108.299,
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bZJbzaj_IlP.pdf | 2,022 | 2 | a non-parametric regression viewpoint : generalization of overparametrized deep relu network under noisy observations namjoon suh, hyunouk ko, xiaoming huo h.milton stewart school of industrial and systems engineering georgia institute of technology atlanta, ga, usa {namjsuh,hko39,huo}@gatech.edu abstract we study the ... | 1 | [
108.249,
146.2630784,
351.6933716,
156.2256784
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qyTBxTztIpQ.pdf | 2,022 | 1 | crowdplay: crowdsourcing human demonstrations for offline learning matthias gerstgrasser, rakshit trivedi & david c. parkes school of engineering and applied sciences harvard university {matthias,rstrivedi,parkes}@seas.harvard.edu abstract crowdsourcing has been instrumental for driving ai advances that rely on largesc... | 8 | [
108.299,
239.5206768,
190.2013701,
251.4758768
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qrwe7XHTmYb.pdf | 2,021 | 1 | gshard: scaling giant models with conditional computation and automatic sharding dmitry lepikhin lepikhin@google.com hyoukjoong lee hyouklee@google.com yuanzhong xu yuanzx@google.com dehao chen dehao@google.com orhan firat orhanf@google.com yanping huang huangyp@google.com maxim krikun krikun@google.com noam shazeer no... | 1 | [
108.299,
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TBWA6PLJZQm.pdf | 2,022 | 2 | learning with noisy labels revisited: a study using real-world human annotations jiaheng wei∗ †, zhaowei zhu∗†, hao cheng†, tongliang liu‡, gang niu§, and yang liu† †university of california, santa cruz, †{jiahengwei,zwzhu,haocheng,yangliu}@ucsc.edu, ‡ tongliang.liu@sydney.edu.au, ‡tml lab, university of sydney, § gang... | 4 | [
108.249,
698.0240784,
369.1514979,
707.9866784
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q7n2RngwOM.pdf | 2,022 | 1 | β-intact-vae: identifying and estimating causal effects under limited overlap pengzhou (abel) wu & kenji fukumizu department of statistical science, the graduate university for advanced studies & the institute of statistical mathematics tachikawa, tokyo {wu.pengzhou,fukumizu}@ism.ac.jp abstract as an important problem ... | 7 | [
108.299,
570.8906768,
200.0834953,
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dpXL6lz4mOQ.pdf | 2,022 | 1 | learning guarantees for graph convolutional networks on the stochastic block model wei lu department of mathematics brandeis university waltham, ma 02453, usa luwei@brandeis.edu abstract an abundance of neural network models and algorithms for diverse tasks on graphs have been developed in the past five years. however,... | 6 | [
108.299,
365.6586768,
249.392498,
377.6138768
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CJd-BtnwtXq.pdf | 2,023 | 2 | a non-asymptotic analysis of oversmoothing in graph neural networks xinyi wu1, zhengdao chen2∗, william wang1, ali jadbabaie1 1laboratory for information and decision systems (lids), mit 2courant institute of mathematical sciences, new york university {xinyiwu,wwang314,jadbabai}@mit.edu, zc1216@nyu.edu abstract oversmo... | 2 | [
108.249,
254.7020784,
299.1576267,
264.6646784
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DlpCotqdTy.pdf | 2,023 | 0 | provably auditing ordinary least squares in low dimensions ankur moitra & dhruv rohatgi massachusetts institute of technology {moitra, drohatgi}@mit.edu abstract auditing the stability of a machine learning model to small changes in the training procedure is critical for engendering trust in practical applications. for... | 14 | [
214.516532664,
484.3460784,
236.14095372,
494.3086784
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rzvOQrnclO0.pdf | 2,022 | 1 | gradient information matters in policy optimization by back-propagating through model chongchong li 1∗, yue wang 2†, wei chen 3†, yuting liu 1, zhi-ming ma 4 & tie-yan liu 2 1 beijing jiaotong university {18118002,ytliu}@bjtu.edu.cn 2 microsoft research asia {yuwang5,tyliu}@microsoft.com 3 institute of computing techno... | 1 | [
108.299,
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O-G91-4cMdv.pdf | 2,023 | 2 | words are all you need? language as an approximation for human similarity judgments raja marjieh1,*, pol van rijn2,*, ilia sucholutsky3,*, theodore r. sumers3, harin lee2,4, thomas l. griffiths1,3,**, nori jacoby2,** ∗*/**equal contribution. 1department of psychology, princeton university 2max planck institute for empi... | 5 | [
108.249,
661.6480784,
230.7728668,
671.6106784
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kkpL4zUXtiw.pdf | 2,023 | 0 | bi-level physics-informed neural networks for pde constrained optimization using broyden’s hypergradients zhongkai hao1,2,3, chengyang ying1, hang su1,4, jun zhu1,3,4∗, jian song2, ze cheng5 1dept. of comp. sci. & tech., institute for ai, bnrist center, thbi lab, tsinghua-bosch joint center for ml, tsinghua university ... | 16 | [
241.84,
393.4282556,
370.1593,
434.5516
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9SDQB3b68K.pdf | 2,022 | 0 | dara: dynamics-aware reward augmentation in offline reinforcement learning jinxin liu123∗ hongyin zhang1∗ donglin wang13† 1 westlake university. 3 institute of advanced technology, westlake institute for advanced study. {liujinxin, zhanghongyin, wangdonglin}@westlake.edu.cn 2 zhejiang university. abstract offline reinf... | 20 | [
113.56396372,
166.07832791,
244.8082071659,
175.350918234
] |
vXj_ucZQ4hA.pdf | 2,021 | 0 | robust pruning at initialization soufiane hayou, jean-francois ton, arnaud doucet & yee whye teh department of statistics university of oxford united kingdom {soufiane.hayou, ton, doucet, teh}@stats.ox.ac.uk abstract overparameterized neural networks (nn) display state-of-the-art performance. however, there is a growin... | 5 | [
107.751,
107.4020784,
503.9968519344,
128.3236784
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NRHajbzg8y0P.pdf | 2,023 | 1 | multimodal analogical reasoning knowledge graphs over ningyu zhang1∗ lei li1∗ xiang chen1∗ xiaozhuan liang1 1zhejiang university, azft joint lab for knowledge engine 2national university of singapore {zhangningyu,leili21,xiang chen,liangxiaozhuan,231sm,huajunsir}@zju.edu.cn shumin deng2 huajun chen1† abstract analogica... | 1 | [
108.299,
468.2826768,
200.0860082,
480.2378768
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zOHQGKO3WGY.pdf | 2,023 | 2 | semi-supervised learning with a principled likelihood from a generative model of data curation stoil ganev and laurence aitchison department of computer science university of bristol, bristol, uk laurence.aitchison@bristol.ac.uk abstract we currently do not have an understanding of semi-supervised learning (ssl) object... | 2 | [
108.249,
212.0160784,
314.6465919,
221.9786784
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wtcud6HroZr.pdf | 2,023 | 2 | learning to decompose visual features with latent textual prompts feng wang1, manling li2, xudong lin3, hairong lv1, alexander g. schwing2 & heng ji2 1tsinghua university 2university of illinois at urbana-champaign 3columbia university abstract recent advances in pre-training vision-language models like clip (radford e... | 8 | [
108.249,
633.9030784,
209.0760807,
643.8656784
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p_jIy5QFB7.pdf | 2,023 | 1 | taking a step back with kcal: multi-class kernel-based calibration for deep neural networks shubhendu trivedi zhen lin1 1 department of computer science, university of illinois at urbana-champaign 2 carle illinois college of medicine, university of illinois at urbana-champaign {zhenlin4,jimeng}@illinois.edu shubhendu@c... | 2 | [
108.299,
398.4296768,
321.9741114,
410.3848768
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JtBRnrlOEFN.pdf | 2,022 | 1 | charformer: fast character transformers via gradient-based subword tokenization yi tay∗, vinh q. tran∗, sebastian ruder†, jai gupta, hyung won chung, dara bahri zhen qin, simon baumgartner, cong yu, donald metzler google research and deepmind† yitay@google.com, vqtran@google.com abstract state-of-the-art models in natu... | 7 | [
108.299,
137.7796768,
211.1957635,
149.7348768
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GTGb3M_KcUl.pdf | 2,021 | 1 | dynatune: dynamic tensor program optimization in deep neural network compilation minjia zhang∗, menghao li*, chi wang & mingqin li microsoft corporation {minjiaz,t-meli,wang.chi,mingqli}@microsoft.com abstract recently, the dl compiler, together with learning to compile has proven to be a powerful technique for optimiz... | 3 | [
108.299,
697.5936768,
172.8109394,
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3YjQfCLdrzz.pdf | 2,023 | 2 | fosr: first-order spectral rewiring for addressing oversquashing in gnns kedar karhadkar ucla kedar@math.ucla.edu pradeep kr. banerjee mpi mis pradeep@mis.mpg.de guido montúfar ucla & mpi mis montufar@math.ucla.edu abstract graph neural networks (gnns) are able to leverage the structure of graph data by passing message... | 3 | [
108.249,
662.3520784,
296.7283756,
672.3146784
] |
NECTfffOvn1.pdf | 2,021 | 2 | fidelity-based deep adiabatic scheduling eli ovits & lior wolf tel aviv university abstract adiabatic quantum computation is a form of computation that acts by slowly interpolating a quantum system between an easy to prepare initial state and a final state that represents a solution to a given computational problem. th... | 7 | [
108.249,
352.2470784,
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89GT-S49mGd.pdf | 2,023 | 0 | function-space regularized rényi divergences jeremiah birrell1, yannis pantazis2, paul dupuis3, luc rey-bellet1, markos a. katsoulakis1 1university of massachusetts, amherst, 2foundation for research & technology - hellas, 3brown university, {jbirrell, luc, markos}@umass.edu, pantazis@iacm.forth.gr, paul_dupuis@brown.e... | 4 | [
107.671,
238.5868166,
504.3615730076,
273.9108556
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-qh0M9XWxnv.pdf | 2,021 | 0 | analyzing the expressive power of graph neural networks in a spectral perspective muhammet balcilar∗, guillaume renton, pierre h´eroux, benoit ga ¨uz`ere, s´ebastien adam, paul honeine normandy university, litis lab, university of rouen normandy, insa rouen normandie rouen, 76000, france abstract in the recent literatu... | 3 | [
412.915,
582.2210828,
415.73380996,
589.1948828
] |
UYneFzXSJWh.pdf | 2,022 | 0 | fine-tuning can distort pretrained features and underperform out-of-distribution ananya kumar, aditi raghunathan, robbie jones, tengyu ma, percy liang stanford university, computer science department abstract when transferring a pretrained model to a downstream task, two popular methods are full fine-tuning (updating a... | 17 | [
108,
160.5860828,
504.0003388,
200.7998
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GrpU6dxFmMN.pdf | 2,023 | 2 | improving the imputation of missing data with markov blanket discovery yang liu, anthony c. constantinou machine intelligence and decision systems (minds) research group queen mary university of london {yangliu, a.constantinou}@qmul.ac.uk abstract the process of imputation of missing data typically relies on generative... | 7 | [
108.249,
102.6010784,
247.0935817,
112.5636784
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v8JIQdiN9Sh.pdf | 2,023 | 2 | on the effectiveness of out-of-distribution data in self-supervised long-tail learning jianhong bai1∗, zuozhu liu1∗, hualiang wang2, jin hao3, yang feng4, huanpeng chu1, haoji hu1† 1zhejiang university, 2the hong kong university of science and technology, 3harvard university, 4angelalign technology abstract though self... | 6 | [
108.249,
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0N8jUH4JMv6.pdf | 2,021 | 0 | implicit convex regularizers of cnn architectures: convex optimization of two- and three-layer networks in polynomial time tolga ergen & mert pilanci department of electrical engineering stanford university stanford, ca 94305, usa {ergen,pilanci}@stanford.edu abstract we study training of convolutional neural networks ... | 18 | [
142.905,
230.4710828,
215.6351785,
247.5358828
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3KUfbI9_DQE.pdf | 2,023 | 0 | distributionally robust post-hoc classifiers under prior shifts jiaheng wei∗ † uc santa cruz harikrishna narasimhan google research ehsan amid google research wen-sheng chu google research yang liu uc santa cruz abhishek kumar † google research abstract the generalization ability of machine learning models degrades sig... | 2 | [
119.339,
549.2720784,
289.8588616,
559.2346784
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3SV-ZePhnZM.pdf | 2,021 | 0 | incremental few-shot learning via vector quantization in deep embedded space kuilin chen department of mechanical and industrial engineering university of toronto toronto, ontario, canada kuilin.chen@mail.utoronto.ca chi-guhn lee department of mechanical and industrial engineering university of toronto toronto, ontario... | 5 | [
336.285,
678.1680828,
346.43561028,
690.0138556
] |
3YjQfCLdrzz.pdf | 2,023 | 2 | fosr: first-order spectral rewiring for addressing oversquashing in gnns kedar karhadkar ucla kedar@math.ucla.edu pradeep kr. banerjee mpi mis pradeep@mis.mpg.de guido montúfar ucla & mpi mis montufar@math.ucla.edu abstract graph neural networks (gnns) are able to leverage the structure of graph data by passing message... | 1 | [
108.249,
262.4900784,
229.6712761,
272.4526784
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k1FHgri5y3-.pdf | 2,023 | 1 | sparse random networks for communication-efficient federated learning berivan isik¶∗, francesco pase§∗, deniz gunduz‡, tsachy weissman¶, michele zorzi§ ¶stanford university, §university of padova, ‡imperial college london berivan.isik@stanford.edu, pasefrance@dei.unipd.it abstract one main challenge in federated learni... | 1 | [
108.299,
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m8uJvVgwRci.pdf | 2,022 | 0 | creating training sets via weak indirect supervision jieyu zhang1,2, bohan wang1,3, xiangchen song4, yujing wang1, yaming yang1, jing bai1, alexander ratner2,5 1microsoft research asia 3university of science and technology of china {jieyuz2, ajratner}@cs.washington.edu {yujwang, yayaming, jbai}@microsoft.com wbhfy@mail... | 0 | [
108,
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-8sBpe7rDiV.pdf | 2,022 | 1 | network insensitivity to parameter noise via adversarial regularization julian büchel ibm research - zurich synsense, zürich, switzerland eth zürich, switzerland jbu@zurich.ibm.com fynn faber eth zürich, switzerland faberf@ethz.ch dylan r. muir synsense, zürich, switzerland dylan.muir@synsense.ai abstract neuromorphic ... | 1 | [
108.299,
316.1946768,
211.1957635,
328.1498768
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9SDQB3b68K.pdf | 2,022 | 1 | dara: dynamics-aware reward augmentation in offline reinforcement learning jinxin liu123∗ hongyin zhang1∗ donglin wang13† 1 westlake university. 3 institute of advanced technology, westlake institute for advanced study. {liujinxin, zhanghongyin, wangdonglin}@westlake.edu.cn 2 zhejiang university. abstract offline reinf... | 6 | [
108.299,
442.9726768,
200.0834953,
454.9278768
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8qDwejCuCN.pdf | 2,021 | 1 | unsupervised representation learning for time series with temporal neighborhood coding sana tonekaboni∗ university of toronto & vector institute the hospital for sick children stonekaboni@cs.toronto.edu anna goldengerg university of toronto & vector institute the hospital for sick children anna.goldenberg@utoronto.ca d... | 0 | [
126.82956,
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WmIwYTd0YTF.pdf | 2,023 | 0 | stable target field for reduced variance score estimation in diffusion models yilun xu⇤, shangyuan tong⇤, tommi jaakkola computer science and artificial intelligence lab, massachusetts institute of technology ylxu@mit.edu; sytong, tommi { @csail.mit.edu } abstract diffusion models generate samples by reversing a fixed ... | 11 | [
108,
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505.38794225,
649.3161094
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v6s3HVjPerv.pdf | 2,022 | 0 | do users benefit from interpretable vision? a user study, baseline, and dataset leon sixt∗1, martin schuessler∗23, oana-iuliana popescu1, philipp weiß3, tim landgraf1 freie universit¨at berlin1 weizenbaum institut berlin2 tu berlin3 leon.sixt@fu-berlin.de, martin.schuessler@tu-berlin.de ∗ equal contribution abstract a ... | 7 | [
108,
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OUz_9TiTv9j.pdf | 2,022 | 2 | a zest of lime: towards architecture-independent model distances hengrui jia, hongyu chen, jonas guan university of toronto and vector institute {nickhengrui.jia, hy.chen}@mail.utoronto.ca, jonas@cs.toronto.edu ali shahin shamsabadi vector institute and the alan turing institute a.shahinshamsabadi@turing.ac.uk nicolas ... | 7 | [
108.249,
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287.4222612,
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-6vS_4Kfz0.pdf | 2,021 | 1 | optimizing memory placement using evolutionary graph reinforcement learning shauharda khadka ∗ intel labs estelle aflalo ∗ intel israel mattias marder ∗ intel israel avrech ben-david ∗ technion santiago miret intel labs shie mannor technion tamir hazan technion hanlin tang intel labs somdeb majumdar † intel labs abstra... | 0 | [
126.82956,
354.4216768,
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qrwe7XHTmYb.pdf | 2,021 | 2 | gshard: scaling giant models with conditional computation and automatic sharding dmitry lepikhin lepikhin@google.com hyoukjoong lee hyouklee@google.com yuanzhong xu yuanzx@google.com dehao chen dehao@google.com orhan firat orhanf@google.com yanping huang huangyp@google.com maxim krikun krikun@google.com noam shazeer no... | 5 | [
108.249,
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P8YIphWNEGO.pdf | 2,023 | 2 | mlpinit: embarrassingly simple gnn training acceleration with mlp initialization xiaotian han1∗ tong zhao2 yozen liu2 xia hu3 neil shah2 1texas a&m university han@tamu.edu 3rice university @snap.com tzhao,yliu2,nshah } 2snap inc. xia.hu@rice.edu abstract training graph neural networks (gnns) on large graphs is complex ... | 7 | [
132.15924,
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389.6193944,
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w0QXrZ3N-s.pdf | 2,023 | 2 | the modality focusing hypothesis: towards understanding crossmodal knowledge distillation zihui xue∗ ,1, zhengqi gao∗,2, sucheng ren∗,3, hang zhao† ,4 1 the university of texas at austin 3 south china university of technology 2 massachusetts institute of technology 4 tsinghua university, shanghai qi zhi institute abstr... | 5 | [
132.15924,
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1PL1NIMMrw.pdf | 2,023 | 2 | self-consistency improves chain of thought reasoning in language models xuezhi wang†‡, jason wei†, dale schuurmans†, quoc le†, ed h. chi†, sharan narang†, aakanksha chowdhery†, denny zhou†§ †google research, brain team ‡xuezhiw@google.com, §dennyzhou@google.com abstract chain-of-thought prompting combined with pre-trai... | 4 | [
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kJqXEPXMsE0.pdf | 2,023 | 2 | 3d equivariant diffusion for target-aware molecule generation and affinity prediction jiaqi guan1∗, wesley wei qian1∗, xingang peng2, yufeng su1, jian peng1, jianzhu ma3 1 department of computer science, university of illinois urbana-champaign 2 school of intelligence science and technology, peking university 3 institu... | 2 | [
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298.7426784
] |
IxmWsm4xrua.pdf | 2,023 | 2 | toeplitz neural network for sequence modeling 2zhen qin 2xiaodong han 3weixuan sun 2bowen he 1dong li 4yuchao dai 5lingpeng kong 1yiran zhong∗ 1shanghai ai laboratory 4northwestern polytechnical university 2sensetime research 5the university of hong kong 3australian national university 3dongxu li abstract sequence mode... | 7 | [
108.249,
174.9940784,
207.9624746,
184.9566784
] |
iaO86DUuKi.pdf | 2,021 | 2 | conservative safety critics for exploration homanga bharadhwaj1∗, aviral kumar2, nicholas rhinehart2, sergey levine2, florian shkurti1, animesh garg1 1university of toronto, vector institute 2university of california berkeley homanga@cs.toronto.edu abstract safe exploration presents a major challenge in reinforcement l... | 6 | [
108.249,
263.9380784,
221.3478983,
273.9006784
] |
2QzNuaRHn4Z.pdf | 2,023 | 1 | bitrate-constrained dro: beyond worst case robustness to unknown group shifts amrith setlur1 don dennis1 benjamin eysenbach1 aditi raghunathan1 chelsea finn2 virginia smith1 1 carnegie mellon university 2 stanford university sergey levine3 3 uc berkeley abstract training machine learning models robust to distribution s... | 4 | [
108.149,
193.7465888,
249.610707,
205.7017888
] |
hx1IXFHAw7R.pdf | 2,021 | 1 | provable rich observation reinforcement learning with combinatorial latent states dipendra misra∗ microsoft research qinghua liu princeton university chi jin princeton university john langford microsoft research abstract we propose a novel setting for reinforcement learning that combines two common real-world difficult... | 4 | [
108.299,
554.4386768,
484.0596029,
566.3938768
] |
SEcSahl0Ql.pdf | 2,023 | 0 | iterative circuit repair against formal specifications matthias cosler cispa helmholtz center for information security matthias.cosler@cispa.de frederik schmitt cispa helmholtz center for information security frederik.schmitt@cispa.de christopher hahn stanford university hahn@cs.stanford.edu bernd finkbeiner cispa helm... | 18 | [
108,
330.4830784,
504.0045882,
352.0920978
] |
n05upKp02kQ.pdf | 2,023 | 0 | partially observable rl with b-stability: unified structural condition and sharp sample-efficient algorithms fan chen peking university chern@pku.edu.cn yu bai˚ salesforce research yu.bai@salesforce.com song mei˚ uc berkeley songmei@berkeley.edu abstract partial observability—where agents can only observe partial infor... | 7 | [
229.727,
508.08155,
244.72669056,
526.2958556
] |
tgcAoUVHRIB.pdf | 2,022 | 2 | neural methods for logical reasoning over knowledge graphs alfonso amayuelas∗†, shuai zhang†, susie xi rao† & ce zhang† ∗epfl †eth zurich alfonso.amayuelas@alumni.epfl.ch {shuazhang, raox, ce.zhang}@inf.ethz.ch abstract reasoning is a fundamental problem for computers and deeply studied in artificial intelligence. in t... | 5 | [
108.249,
427.6920784,
316.9899982,
437.6546784
] |
MMAeCXIa89.pdf | 2,022 | 0 | πbo: augmenting acquisition functions with user beliefs for bayesian optimization carl hvarfner1, danny stoll2, artur souza3, marius lindauer4, frank hutter2,5 & luigi nardi1,6 1lund university, 2university of freiburg, 3federal university of minas gerais, 4leibniz university hannover, 5bosch center for artificial inte... | 3 | [
107.532,
198.2970784,
505.7467626992,
285.1908556
] |
Ai8Hw3AXqks.pdf | 2,023 | 2 | simplified state space layers for sequence modeling jimmy t.h. smith*, 1, 2, andrew warrington*, 2, 3, scott w. linderman2, 3 *equal contribution. 1institute for computational and mathematical engineering, stanford university. 2wu tsai neurosciences institute, stanford university. 3department of statistics, stanford un... | 7 | [
108.249,
147.1490784,
284.3248521,
157.1116784
] |
y5W8tpojhtJ.pdf | 2,023 | 0 | neural collapse inspired feature-classifier alignment for few-shot class incremental learning yibo yang1∗, haobo yuan2∗, xiangtai li3, zhouchen lin3,4,5†, philip torr6, dacheng tao1 1jd explore academy 2school of computer science, wuhan university 3national key lab of general ai, school of intelligence science and tech... | 10 | [
117.963,
138.6100784,
392.64015,
148.8018182
] |
1_OGWcP1s9w.pdf | 2,023 | 2 | learning fair graph representations via automated data augmentations hongyi ling, zhimeng jiang, youzhi luo, shuiwang ji∗, na zou∗ texas a&m university college station, tx 77843, usa {hongyiling,zhimengj,yzluo,sji,nzou1}@tamu.edu abstract we consider fair graph representation learning via data augmentations. while this... | 5 | [
108.249,
474.9280784,
295.0864816,
484.8906784
] |
47B_ctC4pJ.pdf | 2,023 | 2 | learning input-agnostic manipulation directions in stylegan with text guidance yoonjeon kim1, hyunsu kim2, junho kim2, yunjey choi2,eunho yang1,3∗ korea advanced institute of science and technology (kaist)1, naver ai lab2, aitrics3 yoonkim313@kaist.ac.kr hyunsu1125.kim@navercorp.com jhkim.ai@navercorp.com yunjey.choi@n... | 3 | [
108.249,
291.9430784,
427.3523434,
302.5930978
] |
bXNl-myZkJl.pdf | 2,023 | 2 | more convnets in the 2020s: scaling up kernels beyond 51 × 51 using sparsity shiwei liu1,2, tianlong chen1∗, xiaohan chen1∗, xuxi chen1, qiao xiao2, boqian wu2, tommi k¨arkk¨ainen4, mykola pechenizkiy2, decebal constantin mocanu2,3,5, zhangyang wang1 1university of texas at austin, 2eindhoven university of technology, ... | 6 | [
108.249,
573.5860784,
245.4033902,
583.5486784
] |
O50443AsCP.pdf | 2,022 | 0 | tapex: table pre-training via learning a neural sql executor qian liu†∗, bei chen§, jiaqi guo♢∗, morteza ziyadi♡, zeqi lin§, weizhu chen♡, jian-guang lou§ †beihang university, ♢xi’an jiaotong university, §microsoft research asia, ♡microsoft azure ai qian.liu@buaa.edu.cn, jasperguo2013@stu.xjtu.edu.cn {bei.chen, morteza... | 12 | [
108,
403.3570784,
504.0037874,
468.1136784
] |
hcQHRHKfN_.pdf | 2,022 | 0 | continuously discovering novel strategies via reward-switching policy optimization , bingliang zhang2, yi wu23^ zihan zhou∗† 1z 1 cs department, university of toronto, 2 iiis, tsinghua university, 3 shanghai qi zhi institute ^ \ z , wei fu∗ 2\ footoredo@gmail.com, fuwth17@gmail.com, jxwuyi@gmail.com abstract we present... | 17 | [
107.532,
196.8300784,
504.352875408,
250.6276784
] |
jh-rTtvkGeM.pdf | 2,021 | 1 | gradient descent on neural networks typically occurs at the edge of stability jeremy cohen simran kaur yuanzhi li j. zico kolter1 and ameet talwalkar2 carnegie mellon university and: 1bosch ai correspondence to: jeremycohen@cmu.edu 2 determined ai abstract we empirically demonstrate that full-batch gradient descent on ... | 6 | [
108.299,
521.4536768,
250.0674048,
533.4088768
] |
b0JxQC7JLWh.pdf | 2,023 | 2 | certified defences against adversarial patch attacks on semantic segmentation maksym yatsura1,2∗, kaspar sakmann1, n. grace hua1, matthias hein2,3, jan hendrik metzen1 1bosch center for artificial intelligence, robert bosch gmbh, 2university of tübingen, 3tübingen ai center abstract adversarial patch attacks are an eme... | 4 | [
108.249,
92.7680784,
197.7477433,
102.7306784
] |
N0uJGWDw21d.pdf | 2,022 | 1 | bag of instances aggregation boosts self-supervised distillation haohang xu1,2∗ jiemin fang3,4∗ xiaopeng zhang2 lingxi xie2 xinggang wang4 wenrui dai1 hongkai xiong1 qi tian2 1shanghai jiao tong university 2huawei inc. 3institute of artificial intelligence, huazhong university of science & technology 4school of eic, hu... | 5 | [
108.299,
572.8436768,
200.0834953,
584.7988768
] |
RJkAHKp7kNZ.pdf | 2,022 | 0 | vision-based manipulators need to also see from their hands kyle hsu∗, moo jin kim∗, rafael rafailov, jiajun wu, chelsea finn stanford university {kylehsu,moojink,rafailov,jiajunwu,cbfinn}@cs.stanford.edu abstract we study how the choice of visual perspective affects learning and generalization in the context of physic... | 2 | [
123.445,
654.1880784,
486.3087132,
664.3698556
] |
YiBa9HKTyXE.pdf | 2,022 | 0 | permutation-based sgd: is random optimal? shashank rajput∗ kangwook lee dimitris papailiopoulos university of wisconsin-madison abstract a recent line of ground-breaking results for permutation-based sgd has corroborated a widely observed phenomenon: random permutations offer faster convergence than with-replacement sa... | 2 | [
108,
488.8080784,
504.0044533982,
520.6886784
] |
JXhROKNZzOc.pdf | 2,022 | 2 | squant: on-the-fly data-free quantization via diagonal hessian approximation cong guo1,2, yuxian qiu1,2, jingwen leng1,2, ∗, xiaotian gao3, chen zhang4, yunxin liu5, fan yang3, yuhao zhu6 & minyi guo1,2, ∗ 1 shanghai jiao tong university, 2 shanghai qi zhi institute 3 microsoft research, 4 damo academy, alibaba group 5... | 2 | [
108.249,
274.9370784,
178.45549,
284.8996784
] |
-RwZOVybbj.pdf | 2,023 | 1 | risk-aware reinforcement learning with coherent risk measures and non-linear function approximation arun verma† thanh lam† †department of computer science, national university of singapore, republic of singapore ‡department of electrical engineering and computer science, mit, usa {chithanh, arun, lowkh}@comp.nus.edu.sg... | 8 | [
108.299,
193.7236768,
195.3774711,
205.6788768
] |
lqU2cs3Zca.pdf | 2,021 | 2 | signatory: differentiable computations of the signature and logsignature transforms, on both cpu and gpu terry lyons patrick kidger mathematical institute, university of oxford the alan turing institute, british library {kidger, tlyons}@maths.ox.ac.uk abstract signatory is a library for calculating and performing funct... | 3 | [
108.249,
525.6310784,
287.9790893,
535.5936784
] |
t98k9ePQQpn.pdf | 2,022 | 2 | optimal transport for long-tailed recognition with learnable cost matrix hanyu peng, mingming sun, ping, li cognitive computing lab baidu research no.10 xibeiwang east road, beijing 100193, china 10900 ne 8th st. bellevue, washington 98004, usa {penghanyu,sunmingming01,liping11}@baidu.com abstract it is attracting atte... | 7 | [
108.249,
516.9290784,
291.5599318,
526.8916784
] |
57PipS27Km.pdf | 2,022 | 2 | continuous-time meta-learning with forward mode differentiation tristan deleu∗ david kanaa leo feng giancarlo kerg pierre-luc bacon 2 yoshua bengio 1,2 guillaume lajoie 2 mila – université de montréal abstract drawing inspiration from gradient-based meta-learning methods with infinitely small gradient steps, we introdu... | 2 | [
108.249,
698.0240784,
444.0841081,
707.9866784
] |
cP5IcoAkfKa.pdf | 2,021 | 1 | large batch simulation for deep reinforcement learning brennan shacklett1∗ erik wijmans2 aleksei petrenko3,4 manolis savva5 dhruv batra2 vladlen koltun3 kayvon fatahalian1 1stanford university 2georgia institute of technology 3intel labs 4university of southern california 5simon fraser university abstract we accelerate... | 8 | [
108.299,
254.8736768,
190.2013701,
266.8288768
] |
6kCiVaoQdx9.pdf | 2,022 | 0 | few-shot voronoi diagrams: a geometric approach learning cluster-induced as chunwei ma1, ziyun huang2, mingchen gao1, jinhui xu1 1department of computer science and engineering, university at buffalo 2computer science and software engineering, penn state erie 1{chunweim,mgao8,jinhui}@buffalo.edu 2{zxh201}@psu.edu abstr... | 17 | [
362.2062569462,
297.8813626029,
376.6968559862,
303.6776022189
] |
vrW3tvDfOJQ.pdf | 2,022 | 2 | sample efficient deep reinforcement learning via uncertainty estimation vincent mai, kaustubh mani and liam paull ∗ robotics and embodied ai lab mila - quebec institute of artificial intelligence universit´e de montr´eal, quebec, canada {vincent.mai,kaustubh.mani,liam.paull}@umontreal.ca abstract in model-free deep rei... | 2 | [
108.249,
261.8160784,
429.0638626,
271.7786784
] |
O3bqkf_Puys.pdf | 2,021 | 0 | pstnet: point spatio-temporal convolution on point cloud sequences hehe fan1, xin yu2, yuhang ding3, yi yang2 & mohan kankanhalli1 1school of computing, national university of singapore 2reler, university of technology sydney 3baidu research abstract point cloud sequences are irregular and unordered in the spatial dime... | 20 | [
505.3713216,
450.019346692,
515.3339216,
484.905134047
] |
7C9aRX2nBf2.pdf | 2,023 | 1 | sequential latent variable models for few-shot high-dimensional time-series forecasting xiajun jiang∗, ryan missel∗, zhiyuan li & linwei wang golisano college of computing and information sciences rochester institute of technology rochester, ny 14623, usa {xj7056,rxm7244,zl7904,linwei.wang}@rit.edu abstract modern appl... | 7 | [
108.299,
282.3586768,
394.8472906,
294.3138768
] |
gVOXZproe-e.pdf | 2,023 | 0 | how to prepare your task head for finetuning yi ren university of british columbia renyi.joshua@gmail.com shangmin guo university of edinburgh s.guo@ed.ac.uk | 0 | [
295.374,
644.2084978,
392.0012574,
675.4714166
] |
KmtVD97J43e.pdf | 2,022 | 2 | synchromesh: reliable code generation from pre-trained language models gabriel poesia∗† stanford university poesia@stanford.edu oleksandr polozov∗‡ x, the moonshot factory polozov@google.com vu le, ashish tiwari, gustavo soares, christopher meek, sumit gulwani microsoft research, redmond {levu,astiwar,gustavo.soares,me... | 7 | [
108.249,
146.2630784,
184.9687361,
156.2256784
] |
OqcZu8JIIzS.pdf | 2,022 | 0 | pareto policy pool for model-based offline reinforcement learning yijun yang1,4, jing jiang1, tianyi zhou2,3, jie ma1, yuhui shi4 1australian artificial intelligence institute, university of technology sydney 2university of washington, seattle, 3university of maryland, college park 4department of computer science and e... | 9 | [
108,
217.5490784,
505.744956483,
227.5616784
] |
vZTp1oPV3PC.pdf | 2,023 | 2 | one transformer can understand both 2d & 3d molecular data tianlang chen2,5∗, tie-yan liu3, shengjie luo1, shuxin zheng3, 1national key laboratory of general artificial intelligence, school of intelligence science and technology, peking university 2school of eecs, peking university 3microsoft research 4center for data ... | 8 | [
108.249,
609.4560784,
209.0760807,
619.4186784
] |
3yJ-hcJBqe.pdf | 2,023 | 0 | adaptive robust evidential optimization for open set detection from imbalanced data hitesh sapkota & qi yu rochester institute of technology {hxs1943, qi.yu}@rit.edu abstract open set detection (osd) aims at identifying data samples of an unknown class (i.e., open set) from those of known classes (i.e., closed set) bas... | 10 | [
108,
241.8430784,
505.746094192,
273.9518182
] |
1YLJDvSx6J4.pdf | 2,021 | 0 | learning from protein structure with geometric vector perceptrons bowen jing∗, stephan eismann∗, patricia suriana, raphael j.l. townshend, ron o. dror stanford university {bjing, seismann, psuriana, raphael, rondror}@cs.stanford.edu abstract learning on 3d structures of large biomolecules is emerging as a distinct area... | 4 | [
135.397,
462.8540784,
399.5354138,
472.8166784
] |
8tYRqb05pVn.pdf | 2,023 | 1 | linearly mapping from image to text space jack merullo, louis castricato, carsten eickhoff, ellie pavlick department of computer science brown university providence, ri, usa {jack merullo,louis castricato,carsten,ellie pavlick}@brown.edu abstract the extent to which text-only language models (lms) learn to represent fe... | 8 | [
108.299,
258.7336768,
195.3774711,
270.6888768
] |
okwxL_c4x84.pdf | 2,023 | 0 | clifford neural layers for pde modeling johannes brandstetter microsoft research ai4science johannesb@microsoft.com rianne van den berg microsoft research ai4science rvandenberg@microsoft.com max welling microsoft research ai4science maxwelling@microsoft.com jayesh k. gupta microsoft autonomous systems and robotics res... | 32 | [
312.993,
144.1100828,
380.88958656,
155.2918556
] |
De4FYqjFueZ.pdf | 2,023 | 0 | transformers learn shortcuts to automata bingbin liu1 ∗ jordan t. ash2 1carnegie mellon university surbhi goel3† akshay krishnamurthy2 cyril zhang2 2microsoft research nyc 3university of pennsylvania abstract algorithmic reasoning requires capabilities which are most naturally understood through recurrent models of com... | 14 | [
117.963,
346.3420784,
427.83715,
356.5338182
] |
FLA55mBee6Q.pdf | 2,022 | 1 | coptidice: offline constrained reinforcement learning via stationary distribution correction estimation jongmin lee1∗, cosmin paduraru2, daniel j. mankowitz2, nicolas heess2, doina precup2, kee-eung kim1, arthur guez2 1kaist, 2deepmind abstract we consider the offline constrained reinforcement learning (rl) problem, in... | 0 | [
126.82956,
352.3286768,
205.9888518,
364.2838768
] |
YWNAX0caEjI.pdf | 2,022 | 1 | neural structured prediction for inductive node classification meng qu∗1,2, huiyu cai∗1,2, jian tang1,3,4 1mila - qu´ebec ai institute 2universit´e de montr´eal 3hec montr´eal 4canadian institute for advanced research (cifar) abstract this paper studies node classification in the inductive setting, i.e., aiming to lear... | 6 | [
108.299,
697.5936768,
194.1824456,
709.5488768
] |
LNpMtk15AS4.pdf | 2,023 | 0 | boosting differentiable causal discovery via adaptive sample reweighting an zhang1,2, fangfu liu3, wenchang ma2, zhibo cai4, xiang wang∗ 5, tat-seng chua1,2 1sea-next joint lab, 2national university of singapore, 3tsinghua university 4renmin university of china, 5university of science and technology of china anzhang@u.... | 3 | [
108,
582.4570784,
504.003511,
658.3918556
] |
TGFO0DbD_pk.pdf | 2,021 | 0 | genetic soft updates for policy evolution in deep reinforcement learning enrico marchesini∗, davide corsi, alessandro farinelli university of verona, department of computer science abstract the combination of evolutionary algorithms (eas) and deep reinforcement learning (drl) has been recently proposed to merge the ben... | 9 | [
117.963,
547.8900784,
417.07815,
558.0818182
] |
xENf4QUL4LW.pdf | 2,022 | 1 | sample selection with uncertainty of losses for learning with noisy labels xiaobo xia1 tongliang liu1† bo han2 mingming gong3 1tml lab, the university of sydney 2hong kong baptist university 3the university of melbourne 5riken aip 6the university of tokyo jun yu4 gang niu5 masashi sugiyama5,6 4university of science and... | 9 | [
108.299,
697.5936768,
229.2579856,
709.5488768
] |
y0VvIg25yk.pdf | 2,022 | 0 | on the learning and learnability of quasimetrics tongzhou wang mit csail phillip isola mit csail abstract our world is full of asymmetries. gravity and wind can make reaching a place easier than coming back. social artifacts such as genealogy charts and citation graphs are inherently directed. in reinforcement learning... | 31 | [
107.691,
381.5910784,
504.00139488,
435.3896784
] |
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