Top 10 Arxiv Papers Today
2.365 Mikeys
#1. PaperRobot: Incremental Draft Generation of Scientific Ideas
Qingyun Wang, Lifu Huang, Zhiying Jiang, Kevin Knight, Heng Ji, Mohit Bansal, Yi Luan
We present a PaperRobot who performs as an automatic research assistant by
(1) conducting deep understanding of a large collection of human-written papers
in a target domain and constructing comprehensive background knowledge graphs
(KGs); (2) creating new ideas by predicting links from the background KGs, by
combining graph attention and contextual text attention; (3) incrementally
writing some key elements of a new paper based on memory-attention networks:
from the input title along with predicted related entities to generate a paper
abstract, from the abstract to generate conclusion and future work, and finally
from future work to generate a title for a follow-on paper. Turing Tests, where
a biomedical domain expert is asked to compare a system output and a
human-authored string, show PaperRobot generated abstracts, conclusion and
future work sections, and new titles are chosen over human-written ones up to
30%, 24% and 12% of the time, respectively.
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hardmaru:
Automating academic supervisors? https://t.co/JhFoRnrwIv https://t.co/eKydraGmf5
Miles_Brundage:
Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3
🤖✍️
UberCitizen:
Qué ambicioso esto, y qué fascinante.
Acaba de ser publicado en https://t.co/i0sQ9kDRqL
👇👇👇
"PaperRobot: Incremental Draft Generation of Scientific Ideas" ─
https://t.co/HDYQC9lXPZ
BrundageBot:
PaperRobot: Incremental Draft Generation of Scientific Ideas. Qingyun Wang, Lifu Huang, Zhiying Jiang, Kevin Knight, Heng Ji, Mohit Bansal, and Yi Luan https://t.co/BP4KpcL35I
mandubian:
"60% of 6.4 million papers in biomedic & chemistry are about incremental work"
Paperbot builds graph knowledge from existing papers, then generate new incremental ideas and their abstract/conclusion and finally title for future follow-on papers... Whoaa :D
https://t.co/JjRabY9CwN
keunwoochoi:
@bfirsh https://t.co/nybQxVEKUk
jochenleidner:
Wang et al.'s PaperRobot: "Turing tests show that PaperRobot-generated output strings are sometimes chosen over human[-]written ones" https://t.co/NDXXRZMfxn #AI #machinelearning #research https://t.co/Pfylhdpc3H
arxivml:
"PaperRobot: Incremental Draft Generation of Scientific Ideas",
Qingyun Wang, Lifu Huang, Zhiying Jiang, Kevin Knig…
https://t.co/4YS1Ib5PXF
Gelarehai:
Interesting!
"PaperRobot: Incremental Draft Generation of Scientific Ideas"
"PaperRobot automatically reads existing papers to build background
knowledge graphs, in which nodes are entities/concepts and edges are the relations between these entities"
https://t.co/Ps2iT7bUlF https://t.co/lXER2P3zHG
reddit_ml:
[R] PaperRobot: Incremental Draft Generation of Scientific Ideas https://t.co/M6ZXlelLrt
bxrobertz:
A machine learning paper about building a sophisticated ML model to write machine learning (and other scientific) papers ... https://t.co/gV1q5y6HwR
hereticreader:
PaperRobot: Incremental Draft Generation of Scientific Ideas - https://t.co/9D1COPFWPY https://t.co/JktBlbKgD8
LisandroKaunitz:
"PaperRobot generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time, respectively"
https://t.co/qhMpl8ZvWl
🤔
tedherman:
next year it will be writing grant proposals https://t.co/leAL5kuluk
malokhem:
드디어 나왔다 딥러닝 기반 논문봇 https://t.co/log7Et1mFA
jeremieclos:
"PaperRobot: Incremental Draft Generation of Scientific Ideas". Barely finished my PhD and already on my way to be automated. Life is difficult.
https://t.co/Ne7VX1tOPZ
himakotsu:
PaperRobot: Incremental Draft Generation of Scientific Ideas. (arXiv:1905.07870v1 [https://t.co/Elc9rIUsHa]) https://t.co/J0MiuuFFYQ
nsatourian:
This is really, really interesting. It's exciting to see NLP applied more to accelerating scientific discovery.
PaperRobot: Incremental Draft Generation of Scientific Ideas - https://t.co/a8dvJdvhQK
SciFi:
PaperRobot: Incremental Draft Generation of Scientific Ideas. https://t.co/locwFgFOzq
jsjeong3:
PaperRobot: Incremental Draft Generation of Scientific Ideas https://t.co/NT2vBUNLne
arxiv_cs_LG:
PaperRobot: Incremental Draft Generation of Scientific Ideas. Qingyun Wang, Lifu Huang, Zhiying Jiang, Kevin Knight, Heng Ji, Mohit Bansal, and Yi Luan https://t.co/0sZKs3edwk
Alex4386_dev:
https://t.co/lldXWCIIeo
https://t.co/rB3UMQfiX1
PaperRobot: Incremental Draft Generation of Scientific Ideas
🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔🤔
arxiv_cscl:
PaperRobot: Incremental Draft Generation of Scientific Ideas https://t.co/T3NbQUPwfA
arxiv_cscl:
PaperRobot: Incremental Draft Generation of Scientific Ideas https://t.co/T3NbQUPwfA
arxiv_cscl:
PaperRobot: Incremental Draft Generation of Scientific Ideas https://t.co/T3NbQUxUR0
_ivana__anavi_:
PaperRobot: Incremental Draft Generation of Scientific Ideas 🤯
https://t.co/90LfXZT5GB
DataSciNews:
RT @hardmaru: Automating academic supervisors? https://t.co/JhFoRnrwIv https://t.co/eKydraGmf5
ceobillionaire:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
ceobillionaire:
RT @hardmaru: Automating academic supervisors? https://t.co/JhFoRnrwIv https://t.co/eKydraGmf5
TweetinChar:
RT @hardmaru: Automating academic supervisors? https://t.co/JhFoRnrwIv https://t.co/eKydraGmf5
TheAnnaGat:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
kennybastani:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
KyleCranmer:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
cvondrick:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
mapc:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
mohitban47:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
sigitpurnomo:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
KevinFaircloth1:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
drfeldt:
RT @hardmaru: Automating academic supervisors? https://t.co/JhFoRnrwIv https://t.co/eKydraGmf5
drfeldt:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
desertnaut:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
sina_lana:
RT @hardmaru: Automating academic supervisors? https://t.co/JhFoRnrwIv https://t.co/eKydraGmf5
windx0303:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
bradleypallen:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
KloudStrife:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
ErmiaBivatan:
RT @hardmaru: Automating academic supervisors? https://t.co/JhFoRnrwIv https://t.co/eKydraGmf5
gardenfelder:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
puneethmishra:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
urban_stevie:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
marwen_o:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
shivamg:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
matthijsMmaas:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
lorenlugosch:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
commoncitizen01:
RT @malokhem: 드디어 나왔다 딥러닝 기반 논문봇 https://t.co/log7Et1mFA
TheLeanAcademic:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
drvicentes:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
ezajko:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
csanhuezalobos:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
MasterScrat:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
rohand24:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
_rahulgopinath:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
Rovio_Red:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
MuhammadThalhah:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
westis96:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
AssistedEvolve:
RT @hardmaru: Automating academic supervisors? https://t.co/JhFoRnrwIv https://t.co/eKydraGmf5
UrScienceFriend:
RT @hardmaru: Automating academic supervisors? https://t.co/JhFoRnrwIv https://t.co/eKydraGmf5
anoobian:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
rhira2016:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
swapnil_bishnu:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
mnrmja007:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
GuXuemei:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
howardmeng:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
straybrid:
RT @hardmaru: Automating academic supervisors? https://t.co/JhFoRnrwIv https://t.co/eKydraGmf5
Mufei_Li:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
0xhexhex:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
saadmrb:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
Ogtaufmixpoike6:
RT @malokhem: 드디어 나왔다 딥러닝 기반 논문봇 https://t.co/log7Et1mFA
t_sanfe:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
Charles9n:
RT @Miles_Brundage: Very interesting: "PaperRobot: Incremental Draft Generation of Scientific Ideas," Wang et al.: https://t.co/v0zQC8oRB3…
ialhashims:
RT @hardmaru: Automating academic supervisors? https://t.co/JhFoRnrwIv https://t.co/eKydraGmf5
Github
Stargazers: 16
Subscribers: 11
Subscribers: 11
Forks: 0
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Authors: 7
Total Words: 7967
Unqiue Words: 2765
2.217 Mikeys
#2. Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, Kouhei Nishida
High sensitivity of neural architecture search (NAS) methods against their
input such as step-size (i.e., learning rate) and search space prevents
practitioners from applying them out-of-the-box to their own problems, albeit
its purpose is to automate a part of tuning process. Aiming at a fast, robust,
and widely-applicable NAS, we develop a generic optimization framework for NAS.
We turn a coupled optimization of connection weights and neural architecture
into a differentiable optimization by means of stochastic relaxation. It
accepts arbitrary search space (widely-applicable) and enables to employ a
gradient-based simultaneous optimization of weights and architecture (fast). We
propose a stochastic natural gradient method with an adaptive step-size
mechanism built upon our theoretical investigation (robust). Despite its
simplicity and no problem-dependent parameter tuning, our method exhibited near
state-of-the-art performances with low computational budgets both on image
classification and inpainting tasks.
more |
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BrundageBot:
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search. Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, and Kouhei Nishida https://t.co/3BXgz33PMt
argv_sat184:
Our ICML2019 paper has been available on arXiv! https://t.co/Qx0sfrIb0k
We proposed ASNG-NAS, and applied image classification on CIFAR-10 and image restoration on CelebA. ASNG-NAS acquired a structure with the test error 2.76% in 0.11 GPU days! https://t.co/nErqINeU0m
argv_sat184:
Our ICML2019 paper has been available on arXiv! https://t.co/kPTj843wOK
We proposed ASNG-NAS, and applied image classification on CIFAR-10 and image restoration on CelebA. ASNG-NAS acquired a structure with the test error 2.83% on CIFAR-10 in 0.11 GPU days! https://t.co/n7s6MlOKO6
arxiv_cs_LG:
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search. Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari, Kento Uchida, Shota Saito, and Kouhei Nishida https://t.co/PxnNw0SyfL
StatsPapers:
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search. https://t.co/utgLCNCVs5
imenurok:
RT @argv_sat184: Our ICML2019 paper has been available on arXiv! https://t.co/Qx0sfrIb0k
We proposed ASNG-NAS, and applied image classific…
imenurok:
RT @argv_sat184: Our ICML2019 paper has been available on arXiv! https://t.co/kPTj843wOK
We proposed ASNG-NAS, and applied image classific…
KokiMadono:
RT @argv_sat184: Our ICML2019 paper has been available on arXiv! https://t.co/kPTj843wOK
We proposed ASNG-NAS, and applied image classific…
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Authors: 6
Total Words: 0
Unqiue Words: 0
2.17 Mikeys
#3. The $q$-multiple gamma functions of Barnes-Milnor type
Hanamichi Kawamura
The multiple gamma functions of BM (Barnes-Milnor) type and the $q$-multiple
gamma functions have been studied independently. In this paper, we introduce a
new generalization of the multiple gamma functions called the $q$-BM multiple
gamma function including those functions and prove some properties the BM
multiple gamma functions satisfy for them.
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691_7758337633:
！？！？なにこの論文やば
https://t.co/rYurMkQ4Jk
mathNTb:
Hanamichi Kawamura : The $q$-multiple gamma functions of Barnes-Milnor type https://t.co/0yB78jx7jW https://t.co/6E2i5BF6zQ
ephemeral_shade:
RT @691_7758337633: ！？！？なにこの論文やば
https://t.co/rYurMkQ4Jk
adhara_mathphys:
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adhara_mathphys:
RT @mathNTb: Hanamichi Kawamura : The $q$-multiple gamma functions of Barnes-Milnor type https://t.co/0yB78jx7jW https://t.co/6E2i5BF6zQ
_kohta:
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691_7758337633:
RT @mathNTb: Hanamichi Kawamura : The $q$-multiple gamma functions of Barnes-Milnor type https://t.co/0yB78jx7jW https://t.co/6E2i5BF6zQ
dark_yoshi_math:
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junpi316:
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estis__:
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R_O_R_I_J_O:
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kuma_0437:
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dentakumath:
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KonumaTakaki:
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math_elliptic:
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OSAKA_DTC:
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4294967291prime:
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yosswi414_0:
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Freufirst:
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Space_kid_Jr:
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ONEWAN:
RT @mathNTb: Hanamichi Kawamura : The $q$-multiple gamma functions of Barnes-Milnor type https://t.co/0yB78jx7jW https://t.co/6E2i5BF6zQ
henzihenzi:
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pxfnc:
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ponta_both:
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tyamada1093:
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tyamada1093:
RT @mathNTb: Hanamichi Kawamura : The $q$-multiple gamma functions of Barnes-Milnor type https://t.co/0yB78jx7jW https://t.co/6E2i5BF6zQ
tkg5th:
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milm_ac:
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Kind_Kings:
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tks392:
RT @mathNTb: Hanamichi Kawamura : The $q$-multiple gamma functions of Barnes-Milnor type https://t.co/0yB78jx7jW https://t.co/6E2i5BF6zQ
tks392:
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schil_ler:
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https://t.co/rYurMkQ4Jk
TarhoYamada:
RT @691_7758337633: ！？！？なにこの論文やば
https://t.co/rYurMkQ4Jk
KOUSEI2002RIKEI:
RT @691_7758337633: ！？！？なにこの論文やば
https://t.co/rYurMkQ4Jk
oimo_pad:
RT @691_7758337633: ！？！？なにこの論文やば
https://t.co/rYurMkQ4Jk
EGG114514801:
RT @691_7758337633: ！？！？なにこの論文やば
https://t.co/rYurMkQ4Jk
Github
None.
Youtube
None.
Other stats
Sample Sizes : None.
Authors: 1
Total Words: 1729
Unqiue Words: 556
2.169 Mikeys
#4. Parallel Neural Text-to-Speech
Kainan Peng, Wei Ping, Zhao Song, Kexin Zhao
In this work, we propose a non-autoregressive seq2seq model that converts
text to spectrogram. It is fully convolutional and obtains about 17.5 times
speed-up over Deep Voice 3 at synthesis while maintaining comparable speech
quality using a WaveNet vocoder. Interestingly, it has even fewer attention
errors than the autoregressive model on the challenging test sentences.
Furthermore, we build the first fully parallel neural text-to-speech system by
applying the inverse autoregressive flow~(IAF) as the parallel neural vocoder.
Our system can synthesize speech from text through a single feed-forward pass.
We also explore a novel approach to train the IAF from scratch as a generative
model for raw waveform, which avoids the need for distillation from a
separately trained WaveNet.
more |
pdf
| html
Figures
Tweets
BrundageBot:
Parallel Neural Text-to-Speech. Kainan Peng, Wei Ping, Zhao Song, and Kexin Zhao https://t.co/donUIpMPWT
__dhgrs__:
Baidu ResearchのDeep Voiceなどを開発したチームの新しいTTSの論文。WaveNetをParallel WaveNetにした要領で、attention機構をParallel化したところ、高速化に成功しただけでなくattentionのエラーも減ったことがポイント。
https://t.co/o9vFo1VFtd
reddit_ml:
[R] Parallel Neural Text-to-Speech https://t.co/fuAP6fu3UC
arxiv_cs_LG:
Parallel Neural Text-to-Speech. Kainan Peng, Wei Ping, Zhao Song, and Kexin Zhao https://t.co/ffs3sOmu7A
arxiv_cscl:
Parallel Neural Text-to-Speech https://t.co/nv9tZroqxo
Github
None.
Youtube
None.
Other stats
Sample Sizes : None.
Authors: 4
Total Words: 7400
Unqiue Words: 2585
2.168 Mikeys
#5. Lightweight Network Architecture for Real-Time Action Recognition
Alexander Kozlov, Vadim Andronov, Yana Gritsenko
In this work we present a new efficient approach to Human Action Recognition
called Video Transformer Network (VTN). It leverages the latest advances in
Computer Vision and Natural Language Processing and applies them to video
understanding. The proposed method allows us to create lightweight CNN models
that achieve high accuracy and real-time speed using just an RGB mono camera
and general purpose CPU. Furthermore, we explain how to improve accuracy by
distilling from multiple models with different modalities into a single model.
We conduct a comparison with state-of-the-art methods and show that our
approach performs on par with most of them on famous Action Recognition
datasets. We benchmark the inference time of the models using the modern
inference framework and argue that our approach compares favorably with other
methods in terms of speed/accuracy trade-off, running at 56 FPS on CPU. The
models and the training code are available.
more |
pdf
| html
Figures
None.
Tweets
BrundageBot:
Lightweight Network Architecture for Real-Time Action Recognition. Alexander Kozlov, Vadim Andronov, and Yana Gritsenko https://t.co/Ap1z82Vc7E
ZFPhalanx:
Lightweight Network Architecture for Real-Time Action Recognition
https://t.co/TMA0vk6QGP
「decoder that integrates intra-frame temporal information ...」🤔
SciFi:
Lightweight Network Architecture for Real-Time Action Recognition. https://t.co/6OWchP7e0s
arxiv_cs_LG:
Lightweight Network Architecture for Real-Time Action Recognition. Alexander Kozlov, Vadim Andronov, and Yana Gritsenko https://t.co/KHMcFBDSzN
arxiv_cscv:
Lightweight Network Architecture for Real-Time Action Recognition https://t.co/trqC00qw9l
Github
None.
Youtube
None.
Other stats
Sample Sizes : None.
Authors: 3
Total Words: 0
Unqiue Words: 0
2.163 Mikeys
#6. Towards Neural Decompilation
Omer Katz, Yuval Olshaker, Yoav Goldberg, Eran Yahav
We address the problem of automatic decompilation, converting a program in
low-level representation back to a higher-level human-readable programming
language. The problem of decompilation is extremely important for security
researchers. Finding vulnerabilities and understanding how malware operates is
much easier when done over source code.
The importance of decompilation has motivated the construction of
hand-crafted rule-based decompilers. Such decompilers have been designed by
experts to detect specific control-flow structures and idioms in low-level code
and lift them to source level. The cost of supporting additional languages or
new language features in these models is very high.
We present a novel approach to decompilation based on neural machine
translation. The main idea is to automatically learn a decompiler from a given
compiler. Given a compiler from a source language S to a target language T ,
our approach automatically trains a decompiler that can translate (decompile) T
back to S . We used our framework to...
more |
pdf
| html
Figures
Tweets
Miles_Brundage:
"Towards Neural Decompilation," Katz et al.: https://t.co/MLIHbuntRy
BrundageBot:
Towards Neural Decompilation. Omer Katz, Yuval Olshaker, Yoav Goldberg, and Eran Yahav https://t.co/6CrnJQCutq
NandoDF:
RT @Miles_Brundage: "Towards Neural Decompilation," Katz et al.: https://t.co/MLIHbuntRy
judegomila:
RT @Miles_Brundage: "Towards Neural Decompilation," Katz et al.: https://t.co/MLIHbuntRy
rquintino:
RT @Miles_Brundage: "Towards Neural Decompilation," Katz et al.: https://t.co/MLIHbuntRy
mrdrozdov:
RT @Miles_Brundage: "Towards Neural Decompilation," Katz et al.: https://t.co/MLIHbuntRy
suvsh:
RT @Miles_Brundage: "Towards Neural Decompilation," Katz et al.: https://t.co/MLIHbuntRy
dmi_paras:
RT @Miles_Brundage: "Towards Neural Decompilation," Katz et al.: https://t.co/MLIHbuntRy
Github
None.
Youtube
None.
Other stats
Sample Sizes : None.
Authors: 4
Total Words: 13606
Unqiue Words: 3185
2.159 Mikeys
#7. Quantum Fluctuations at the Planck Scale
Fulvio Melia
The recently measured cutoff, k_min=[4.34(+/-)0.50]/r_cmb (with r_cmb the
comoving distance to the last scattering surface), in the fluctuation spectrum
of the cosmic microwave background, appears to disfavor slow-roll inflation and
the associated transition of modes across the horizon. We show in this Letter
that k_min instead corresponds to the first mode emerging out of the Planck
domain into the semi-classical universe. The required scalar-field potential is
exponential, though not inflationary, and satisfies the zero active mass
condition, rho_phi+3p_phi=0. Quite revealingly, the observed amplitude of the
temperature anisotropies requires the quantum fluctuations in phi to have
classicalized at ~3.5x10^15 GeV, consistent with the energy scale in grand
unified theories. Such scalar-field potentials are often associated with
Kaluza-Klein cosmologies, string theory and even supergravity.
more |
pdf
| html
Figures
None.
Tweets
life_wont_wait:
この観測も全然知らないんだけどスローロールが死ぬなんて話があるのか？/Quantum Fluctuations at the Planck Scale https://t.co/33gPkDV7mY
dajmeyer:
.@FulvioMelia, Quantum Fluctuations at Planck Scale https://t.co/gvKRqlNpOl "missing is firm understanding of how classicalization converts homogeneous, isotropic quantum fluctuations into classical anisotropies..prob w/all models invoking a quantum origin for the perturbations"
StarshipBuilder:
Quantum Fluctuations at the Planck Scale
https://t.co/pu21i9SndR
HEPPhenoPapers:
Quantum Fluctuations at the Planck Scale. https://t.co/xgJLKaRTha
s_dual:
RT @life_wont_wait: この観測も全然知らないんだけどスローロールが死ぬなんて話があるのか？/Quantum Fluctuations at the Planck Scale https://t.co/33gPkDV7mY
BryanKeIIy:
RT @qraal: [1905.08626] Quantum Fluctuations at the Planck Scale https://t.co/t4S9tOGQkG
Github
None.
Youtube
None.
Other stats
Sample Sizes : None.
Authors: 1
Total Words: 0
Unqiue Words: 0
2.157 Mikeys
#8. CNNs found to jump around more skillfully than RNNs: Compositional generalization in seq2seq convolutional networks
Roberto Dessì, Marco Baroni
Lake and Baroni (2018) introduced the SCAN dataset probing the ability of
seq2seq models to capture compositional generalizations, such as inferring the
meaning of "jump around" 0-shot from the component words. Recurrent networks
(RNNs) were found to completely fail the most challenging generalization cases.
We test here a convolutional network (CNN) on these tasks, reporting hugely
improved performance with respect to RNNs. Despite the big improvement, the CNN
has however not induced systematic rules, suggesting that the difference
between compositional and non-compositional behaviour is not clear-cut.
more |
pdf
| html
Figures
None.
Tweets
BrundageBot:
CNNs found to jump around more skillfully than RNNs: Compositional generalization in seq2seq convolutional networks. Roberto Dessì and Marco Baroni https://t.co/H4BjnPmPe3
SciFi:
CNNs found to jump around more skillfully than RNNs: Compositional generalization in seq2seq convolutional networks. https://t.co/WHsNLIbEA5
arxiv_cs_LG:
CNNs found to jump around more skillfully than RNNs: Compositional generalization in seq2seq convolutional networks. Roberto Dessì and Marco Baroni https://t.co/OiH6X7YLWU
arxiv_cscl:
CNNs found to jump around more skillfully than RNNs: Compositional generalization in seq2seq convolutional networks https://t.co/NfsG95WqkP
arxiv_cscl:
CNNs found to jump around more skillfully than RNNs: Compositional generalization in seq2seq convolutional networks https://t.co/NfsG95WqkP
Github
None.
Youtube
None.
Other stats
Sample Sizes : None.
Authors: 2
Total Words: 0
Unqiue Words: 0
2.154 Mikeys
#9. Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction
Kosuke Nishida, Kyosuke Nishida, Masaaki Nagata, Atsushi Otsuka, Itsumi Saito, Hisako Asano, Junji Tomita
Question answering (QA) using textual sources such as reading comprehension
(RC) has attracted much attention recently. This study focuses on the task of
explainable multi-hop QA, which requires the system to return the answer with
evidence sentences by reasoning and gathering disjoint pieces of the reference
texts. For evidence extraction of explainable multi-hop QA, the existed method
extracted evidence sentences by evaluating the importance of each sentence
independently. In this study, we propose the Query Focused Extractor (QFE)
model and introduce the multi-task learning of the QA model for answer
selection and the QFE model for evidence extraction. QFE sequentially extracts
the evidence sentences by an RNN with an attention mechanism to the question
sentence, which is inspired by extractive summarization models. It enables QFE
to consider the dependency among the evidence sentences and cover the important
information in the question sentence. Experimental results show that QFE with
the simple RC baseline model achieves a...
more |
pdf
| html
Figures
None.
Tweets
BrundageBot:
Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction. Kosuke Nishida, Kyosuke Nishida, Masaaki Nagata, Atsushi Otsuka, Itsumi Saito, Hisako Asano, and Junji Tomita https://t.co/PsIowC4yuA
kyoun:
Our #ACL2019_Italy paper about explainable multi-hop QA is out on arXiv! https://t.co/RdGq0wokpW We propose a query-based extractive summarization model, QFE, and the multi-task learning of QA and evidence extraction. Our model achieves SOTA in evidence extraction on HotpotQA! https://t.co/cG1KNJM9jN
arxiv_cscl:
Answering while Summarizing: Multi-task Learning for Multi-hop QA with Evidence Extraction https://t.co/W5GxOPjD6q
ymym3412:
RT @kyoun: Our #ACL2019_Italy paper about explainable multi-hop QA is out on arXiv! https://t.co/RdGq0wokpW We propose a query-based extra…
devoidikk:
RT @kyoun: Our #ACL2019_Italy paper about explainable multi-hop QA is out on arXiv! https://t.co/RdGq0wokpW We propose a query-based extra…
sobamchan:
RT @kyoun: Our #ACL2019_Italy paper about explainable multi-hop QA is out on arXiv! https://t.co/RdGq0wokpW We propose a query-based extra…
rose_miura:
RT @kyoun: Our #ACL2019_Italy paper about explainable multi-hop QA is out on arXiv! https://t.co/RdGq0wokpW We propose a query-based extra…
Github
None.
Youtube
None.
Other stats
Sample Sizes : None.
Authors: 7
Total Words: 0
Unqiue Words: 0
2.139 Mikeys
#10. Verification Artifacts in Cooperative Verification: Survey and Unifying Component Framework
Dirk Beyer, Heike Wehrheim
The goal of cooperative verification is to combine verification approaches in
such a way that they work together to verify a system model. In particular,
cooperative verifiers provide exchangeable information (verification artifacts)
to other verifiers or consume such information from other verifiers with the
goal of increasing the overall effectiveness and efficiency of the verification
process. This paper first gives an overview over approaches for leveraging
strengths of different techniques, algorithms, and tools in order to increase
the power and abilities of the state of the art in software verification.
Second, we specifically outline cooperative verification approaches and discuss
their employed verification artifacts. We formalize all artifacts in a uniform
way, thereby fixing their semantics and providing verifiers with a precise
meaning of the exchanged information.
more |
pdf
| html
Figures
Tweets
Github
An Exchange Format for Verification Witnesses
Stargazers: 8
Subscribers: 7
Subscribers: 7
Forks: 3
Open Issues: 2
Open Issues: 2
Youtube
None.
Other stats
Sample Sizes : None.
Authors: 2
Total Words: 9303
Unqiue Words: 2787
About
Assert is a website where the best academic papers on arXiv (computer science, math, physics), bioRxiv (biology), BITSS (reproducibility), EarthArXiv (earth science), engrXiv (engineering), LawArXiv (law), PsyArXiv (psychology), SocArXiv (social science), and SportRxiv (sport research) bubble to the top each day.
Papers are scored (in real-time) based on how verifiable they are (as determined by their Github repos) and how interesting they are (based on Twitter).
To see top papers, follow us on twitter @assertpub_ (arXiv), @assert_pub (bioRxiv), and @assertpub_dev (everything else).
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