Top 10 Arxiv Papers Today


2.288 Mikeys
#1. Neural Tangents: Fast and Easy Infinite Neural Networks in Python
Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz
Neural Tangents is a library designed to enable research into infinite-width neural networks. It provides a high-level API for specifying complex and hierarchical neural network architectures. These networks can then be trained and evaluated either at finite-width as usual or in their infinite-width limit. Infinite-width networks can be trained analytically using exact Bayesian inference or using gradient descent via the Neural Tangent Kernel. Additionally, Neural Tangents provides tools to study gradient descent training dynamics of wide but finite networks in either function space or weight space. The entire library runs out-of-the-box on CPU, GPU, or TPU. All computations can be automatically distributed over multiple accelerators with near-linear scaling in the number of devices. Neural Tangents is available at www.github.com/google/neural-tangents. We also provide an accompanying interactive Colab notebook.
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hardmaru: Neural Tangents is a Python library designed to enable research into “infinite-width” neural networks. They provide an API for specifying complex neural network architectures that can then be trained and evaluated in their infinite-width limit. 🙉🤯 https://t.co/Wr2SqlMOwA https://t.co/vAXC02pAs8
Montreal_AI: Neural Tangents: Fast and Easy Infinite Neural Networks in Python Novak et al.: https://t.co/bt7WzIoihH #DeepLearning #NeuralNetworks #Python https://t.co/JuzLIh5kxy
jaschasd: Paper: https://t.co/617vP1bttE Github: https://t.co/fZxNUwBRer Colab Notebook: https://t.co/UwXvlLRpwZ
sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt Github: https://t.co/iutzkEhEOM Colab Notebook: https://t.co/JcxUkWwJ0h
arxivml: "Neural Tangents: Fast and Easy Infinite Neural Networks in Python", Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon L… https://t.co/VgglRsifeV
arxiv_cs_LG: Neural Tangents: Fast and Easy Infinite Neural Networks in Python. Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, and Samuel S. Schoenholz https://t.co/owrRWgj38l
ceobillionaire: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
ericjang11: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
brandondamos: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
SingularMattrix: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
ballforest: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
ballforest: RT @StatsPapers: Neural Tangents: Fast and Easy Infinite Neural Networks in Python. https://t.co/yi2WBveWY2
eigenhector: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
LiamFedus: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
ayirpelle: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
TheGregYang: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
EricSchles: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
superbradyon: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
all2one: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
deepgradient: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
muktabh: RT @StatsPapers: Neural Tangents: Fast and Easy Infinite Neural Networks in Python. https://t.co/yi2WBveWY2
geoffroeder: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
jhhhuggins: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
__tmats__: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
KouroshMeshgi: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
puneethmishra: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
amlankar95: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
tmasada: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
jrugelesuribe: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
harujoh: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
desipoika: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
Daniel_J_Im: RT @jaschasd: Paper: https://t.co/617vP1bttE Github: https://t.co/fZxNUwBRer Colab Notebook: https://t.co/UwXvlLRpwZ
BoakyeTweets: RT @Montreal_AI: Neural Tangents: Fast and Easy Infinite Neural Networks in Python Novak et al.: https://t.co/bt7WzIoihH #DeepLearning #N…
_powei: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
hadisalmanX: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
HydryHydra: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
kadarakos: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
iugoaoj: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
MarkTan57229491: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
tequehead: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
ShirotaShin: RT @StatsPapers: Neural Tangents: Fast and Easy Infinite Neural Networks in Python. https://t.co/yi2WBveWY2
jainnitk: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
karnadi_1: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
phi_nate: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
moKhabb: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
amarotaylorw: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
gshartnett: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
brunoboutteau: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
namhoonlee09: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
tristanasharp: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
shimoke4869: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
alfo_512: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
ndrmnl: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
Bill_Hally: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
TakaAomidoro: RT @StatsPapers: Neural Tangents: Fast and Easy Infinite Neural Networks in Python. https://t.co/yi2WBveWY2
dyitry: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
Wind_Xiaoli: RT @Montreal_AI: Neural Tangents: Fast and Easy Infinite Neural Networks in Python Novak et al.: https://t.co/bt7WzIoihH #DeepLearning #N…
shivamsaboo17: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
FallintTree: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
dave_co_dev: RT @sschoenholz: After a ton of work by a bunch of people, we're releasing an entirely new Neural Tangents. Paper: https://t.co/2KqBv44KJt…
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2.158 Mikeys
#2. Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization
Miika Aittala, Prafull Sharma, Lukas Murmann, Adam B. Yedidia, Gregory W. Wornell, William T. Freeman, Fredo Durand
We recover a video of the motion taking place in a hidden scene by observing changes in indirect illumination in a nearby uncalibrated visible region. We solve this problem by factoring the observed video into a matrix product between the unknown hidden scene video and an unknown light transport matrix. This task is extremely ill-posed, as any non-negative factorization will satisfy the data. Inspired by recent work on the Deep Image Prior, we parameterize the factor matrices using randomly initialized convolutional neural networks trained in a one-off manner, and show that this results in decompositions that reflect the true motion in the hidden scene.
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HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
arxivml: "Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization", Miika Aittala, Prafull Sharma,… https://t.co/qbqNUX4IdL
tovissy: Approximate motion can be reconstructed from the shadows of the objects or humans cast https://t.co/UO6XjeCJAD or https://t.co/vkGB2e61K1 #ComputationalMirrors #ComputationalSensing
arxiv_cs_LG: Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization. Miika Aittala, Prafull Sharma, Lukas Murmann, Adam B. Yedidia, Gregory W. Wornell, William T. Freeman, and Fredo Durand https://t.co/U2qoy8xxv0
prafull7: Checkout our work "Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization" to be presented at NeurIPS 2019. Video: https://t.co/UYufwILtiF Paper: https://t.co/2VD9gnVGzT Project webpage: https://t.co/QOZpWzR3QV
Memoirs: Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization. https://t.co/Z1l8NtWQH3
arxiv_cs_cv_pr: Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization. Miika Aittala, Prafull Sharma, Lukas Murmann, Adam B. Yedidia, Gregory W. Wornell, William T. Freeman, and Fredo Durand https://t.co/X8sZcR2Lel
arxiv_cscv: Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization https://t.co/5PzT8qyzRB
arxiv_cscv: Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization https://t.co/5PzT8qyzRB
drinami: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
HomeiMiyashita: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
shigekzishihara: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
kuronekodaisuki: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
TrevorFSmith: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
strangeqargo: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
olli101: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
EDDxample: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
nakano_muramoto: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
RE_Minory: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
akashjainx: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
Sad_kmbo: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
retropalm: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
oritey: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
MaruHiroReiwa: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
__Enceladus_: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
misakaxindex: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
GuruswamySachin: RT @HCI_Research: Computational Mirrors: Revealing Hidden Video https://t.co/eRziGp2c3b https://t.co/81iB2qusyT
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2.15 Mikeys
#3. Superabundance of Exoplanet Sub-Neptunes Explained by Fugacity Crisis
Edwin S. Kite, Bruce Fegley Jr., Laura Schaefer, Eric B. Ford
Transiting planets with radii 2-3 $R_\bigoplus$ are much more numerous than larger planets. We propose that this drop-off is so abrupt because at $R$ $\sim$ 3 $R_\bigoplus$, base-of-atmosphere pressure is high enough for the atmosphere to readily dissolve into magma, and this sequestration acts as a strong brake on further growth. The viability of this idea is demonstrated using a simple model. Our results support extensive magma-atmosphere equilibration on sub-Neptunes, with numerous implications for sub-Neptune formation and atmospheric chemistry.
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edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavainspace propose a new explanation. https://t.co/YdA4w9WgHr
edwinkite: @lavainspace Here's the bottom-line graphic. See https://t.co/cZMVKPbcxF for tests and implications for observations! https://t.co/b1MIhztSBl
qraal: [1912.02701] Superabundance of Exoplanet Sub-Neptunes Explained by Fugacity Crisis https://t.co/W6P5nmnYbK
StarshipBuilder: Superabundance of Exoplanet Sub-Neptunes Explained by Fugacity Crisis https://t.co/pJ3sFDXpRC
ProfAbelMendez: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
megschwamb: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
CColose: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
didaclopez: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
nyrath: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
astromarkmarley: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
IanAdAstra: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
panther_modern: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
GeologyDave: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
brianwolven: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
brettmor: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
ElevenPointTwo: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
Yorrike: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
PlanetaryGao: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
nogueira_astro: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
awhoward: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
GarciafXimo: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
lavainspace: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
SETIatNL: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
tadkomacek: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
gummiks: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
revprez: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
ChannonVisscher: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
AbVsm: RT @edwinkite: Why does the exoplanet radius distribution fall off steeply at 3 R_Earth? In today's https://t.co/cZMVKPbcxF, me and @lavain…
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2.135 Mikeys
#4. Playing Pool with $|ψ\rangle$: from Bouncing Billiards to Quantum Search
Adam R. Brown
In "Playing Pool with $\pi$'', Galperin invented an extraordinary method to learn the digits of $\pi$ by counting the collisions of billiard balls. Here I demonstrate an exact isomorphism between Galperin's bouncing billiards and Grover's algorithm for quantum search. This provides an illuminating way to visualize what Grover's algorithm is actually doing.
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3blue1brown: Remember that video about how block collisions can compute the digits of pi? A friend, Adam Brown, just showed that the math underlying this is actually identical to the math behind a very famous quantum search algorithm (Grover's): https://t.co/Gqyhx2KqaO Genuinely crazy! https://t.co/mZKx7gnLQv
michael_nielsen: There's a great series of videos by @3blue1brown, showing how pi arises from a question about two balls colliding! But even more remarkable, Adam Brown just posted a paper showing the collisions in that video are isomorphic to the quantum search algorithm!!https://t.co/JdrbXnOEvk
Hal_Tasaki: 【3/3 物理の人向け】重要な量子計算のアルゴリズムである Grover's algorithm とGalperin の π の計算法との関係(ある意味での同値性!)を示した Brown さんの一昨日の論文。笑っちゃうほど意外な話で愉しい。Grover's algorithm の簡潔で明快きわまりない解説まであるよ。 https://t.co/DgEfnDRbMd
taketo1024: 元ツイートではさらにこの見方で Grover の量子検索アルゴリズムが理解できるって論文が紹介されてる📄 Playing Pool with |ψ⟩: from Bouncing Billiards to Quantum Search https://t.co/jjotGK3f5m https://t.co/5LbPBxcVxX
taketo1024: Playing Pool with |ψ⟩: from Bouncing Billiards to Quantum Search https://t.co/rNSTjiE5ZE
nuclear94: Calculating pi https://t.co/zroTGHsp2c
tkmtSo: 以前物体の衝突回数がちょうど円周率になるって話をしたことがあったのだけど、どうやらその数理は量子探索アルゴリズムのグローバーのアルゴリズムと同じであることが指摘されたらしい、すごい https://t.co/ZblCDXwiKY https://t.co/vYL4WZeC4i
PopulusRe: L'article bizarro du jour ! https://t.co/dlrFlDUudF
memming_KO: @banjihasaram https://t.co/sycQ0jSU7t https://t.co/Bh8inrQgDn
raul314314: Calculando pi a través del número de colisiones de dos bloques con diferente masa.[1912.02207] Playing Pool with $|ψ\rangle$: from Bouncing Billiards to Quantum Search https://t.co/MmiTl1vgTg
wearecuriee: Playing Pool with |ψ⟩: from Bouncing Billiards to Quantum Search https://t.co/XsRlQ3bXEH
VishwasNavada: https://t.co/4ShiAD4Bcm
LimgTW: RT @taketo1024: Playing Pool with |ψ⟩: from Bouncing Billiards to Quantum Search https://t.co/rNSTjiE5ZE
n_kats_: RT @taketo1024: Playing Pool with |ψ⟩: from Bouncing Billiards to Quantum Search https://t.co/rNSTjiE5ZE
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2.125 Mikeys
#5. Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline
Vishvak Murahari, Dhruv Batra, Devi Parikh, Abhishek Das
Prior work in visual dialog has focused on training deep neural models on the VisDial dataset in isolation, which has led to great progress, but is limiting and wasteful. In this work, following recent trends in representation learning for language, we introduce an approach to leverage pretraining on related large-scale vision-language datasets before transferring to visual dialog. Specifically, we adapt the recently proposed ViLBERT (Lu et al., 2019) model for multi-turn visually-grounded conversation sequences. Our model is pretrained on the Conceptual Captions and Visual Question Answering datasets, and finetuned on VisDial with a VisDial-specific input representation and the masked language modeling and next sentence prediction objectives (as in BERT). Our best single model achieves state-of-the-art on Visual Dialog, outperforming prior published work (including model ensembles) by more than 1% absolute on NDCG and MRR. Next, we carefully analyse our model and find that additional finetuning using 'dense' annotations i.e....
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abhshkdz: New work from @VishvakM! BERT for visual dialog. Achieves single-model SoTA. Paper: https://t.co/McXkt9myme Code: https://t.co/xIDs30wSVz https://t.co/jM2XVKsFrE
arxivml: "Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline", Vishvak Murahari, Dhruv Batra, Dev… https://t.co/viWHj6hw4I
arxiv_cs_LG: Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline. Vishvak Murahari, Dhruv Batra, Devi Parikh, and Abhishek Das https://t.co/tEqr78Y3tq
SciFi: Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline. https://t.co/xl3RZRblqs
arxiv_cs_cv_pr: Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline. Vishvak Murahari, Dhruv Batra, Devi Parikh, and Abhishek Das https://t.co/I7FH4Ifbzc
arxiv_cscv: Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline https://t.co/SWNbpZrsL7
arxiv_cscv: Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline https://t.co/SWNbpZrsL7
arxiv_cscl: Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline https://t.co/rnhCuy7Rh7
arxiv_cscl: Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline https://t.co/rnhCuy7Rh7
arxiv_cscl: Large-scale Pretraining for Visual Dialog: A Simple State-of-the-Art Baseline https://t.co/rnhCuy7Rh7
VishvakM: New preprint! https://t.co/MqKsCrkJln A simple BERT-style vision and language model (ViLBERT) extended to Visual Dialog leading to SOTA performance! Great starting point for future transformer-based models. Code:https://t.co/8NUNWn4wJl
deviparikh: RT @abhshkdz: New work from @VishvakM! BERT for visual dialog. Achieves single-model SoTA. Paper: https://t.co/McXkt9myme Code: https://t…
KouroshMeshgi: RT @abhshkdz: New work from @VishvakM! BERT for visual dialog. Achieves single-model SoTA. Paper: https://t.co/McXkt9myme Code: https://t…
ghhosh: RT @abhshkdz: New work from @VishvakM! BERT for visual dialog. Achieves single-model SoTA. Paper: https://t.co/McXkt9myme Code: https://t…
westis96: RT @VishvakM: New preprint! https://t.co/MqKsCrkJln A simple BERT-style vision and language model (ViLBERT) extended to Visual Dialog leadi…
rajammanabrolu: RT @VishvakM: New preprint! https://t.co/MqKsCrkJln A simple BERT-style vision and language model (ViLBERT) extended to Visual Dialog leadi…
rajpratim: RT @VishvakM: New preprint! https://t.co/MqKsCrkJln A simple BERT-style vision and language model (ViLBERT) extended to Visual Dialog leadi…
prithvijitch: RT @VishvakM: New preprint! https://t.co/MqKsCrkJln A simple BERT-style vision and language model (ViLBERT) extended to Visual Dialog leadi…
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2.12 Mikeys
#6. AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan
Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment. In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers. We propose AugMix, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions. AugMix significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half.
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CarlRioux: [R] AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty: Paper: https://t.co/XDp5PukD6w Code: https://t.co/23Yh3ncwm8 We propose AugMix, a data processing technique that mixes augmented images and enforces consistent embeddings… https://t.co/WXDm7Peg7k
arxivml: "AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty", Dan Hendrycks, Norman Mu, Ekin D. … https://t.co/TlUdca9lhA
arxiv_cs_LG: AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan https://t.co/Yq1BzDhKBY
StatsPapers: AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. https://t.co/Au2myvfICS
arxiv_cs_cv_pr: AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan https://t.co/f68O5xKqqt
arxiv_cscv: AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty https://t.co/g4sdqZdOl9
arxiv_cscv: AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty https://t.co/g4sdqZdOl9
hitoriblog: RT @TheNormanMu: @balajiln @DanHendrycks @ekindogus @barret_zoph @jmgilmer Code: https://t.co/ZR0fS5MC6w Paper: https://t.co/I7NOfHRP1v
yu4u: RT @TheNormanMu: @balajiln @DanHendrycks @ekindogus @barret_zoph @jmgilmer Code: https://t.co/ZR0fS5MC6w Paper: https://t.co/I7NOfHRP1v
nickschurch: RT @StatsPapers: AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. https://t.co/Au2myvfICS
shinmura0: RT @TheNormanMu: @balajiln @DanHendrycks @ekindogus @barret_zoph @jmgilmer Code: https://t.co/ZR0fS5MC6w Paper: https://t.co/I7NOfHRP1v
matsuko_std: RT @TheNormanMu: @balajiln @DanHendrycks @ekindogus @barret_zoph @jmgilmer Code: https://t.co/ZR0fS5MC6w Paper: https://t.co/I7NOfHRP1v
Github

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Repository: augmix
User: google-research
Language: Python
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2.113 Mikeys
#7. 12-in-1: Multi-Task Vision and Language Representation Learning
Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Devi Parikh, Stefan Lee
Much of vision-and-language research focuses on a small but diverse set of independent tasks and supporting datasets often studied in isolation; however, the visually-grounded language understanding skills required for success at these tasks overlap significantly. In this work, we investigate these relationships between vision-and-language tasks by developing a large-scale, multi-task training regime. Our approach culminates in a single model on 12 datasets from four broad categories of task including visual question answering, caption-based image retrieval, grounding referring expressions, and multi-modal verification. Compared to independently trained single-task models, this represents a reduction from approximately 3 billion parameters to 270 million while simultaneously improving performance by 2.05 points on average across tasks. We use our multi-task framework to perform in-depth analysis of the effect of joint training diverse tasks. Further, we show that finetuning task-specific models from our single multi-task model can...
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roadrunning01: 12-in-1: Multi-Task Vision and Language Representation Learning pdf: https://t.co/tx6ESpQoO6 abs: https://t.co/VQPEOOyiEz https://t.co/vHVnSkEmbQ
arxivml: "12-in-1: Multi-Task Vision and Language Representation Learning", Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Dev… https://t.co/IeX968LouR
arxiv_cs_cv_pr: 12-in-1: Multi-Task Vision and Language Representation Learning. Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Devi Parikh, and Stefan Lee https://t.co/a14Xafioik
arxiv_cscv: 12-in-1: Multi-Task Vision and Language Representation Learning https://t.co/MPS2wmZpQa
arxiv_cscv: 12-in-1: Multi-Task Vision and Language Representation Learning https://t.co/MPS2wmZpQa
arxiv_cscl: 12-in-1: Multi-Task Vision and Language Representation Learning https://t.co/XiDV5hP8eM
arxiv_cscl: 12-in-1: Multi-Task Vision and Language Representation Learning https://t.co/XiDV5hxwQc
arxiv_cscl: 12-in-1: Multi-Task Vision and Language Representation Learning https://t.co/XiDV5hP8eM
arxiv_cscl: 12-in-1: Multi-Task Vision and Language Representation Learning https://t.co/XiDV5hP8eM
vedanujg: One model for many V&L tasks! It answers questions, points to objects and retrieves images given descriptions, guesses which object is the subject of a dialog, etc. 👆🏽performance than dataset-specific models, 1/12th the size. SOTA on 7 (of 12) datasets. https://t.co/pduDvjIO4W https://t.co/Pe053v6rad
EricSchles: RT @roadrunning01: 12-in-1: Multi-Task Vision and Language Representation Learning pdf: https://t.co/tx6ESpQoO6 abs: https://t.co/VQPEOOyiE…
deepgradient: RT @roadrunning01: 12-in-1: Multi-Task Vision and Language Representation Learning pdf: https://t.co/tx6ESpQoO6 abs: https://t.co/VQPEOOyiE…
santiagoitzcoat: RT @roadrunning01: 12-in-1: Multi-Task Vision and Language Representation Learning pdf: https://t.co/tx6ESpQoO6 abs: https://t.co/VQPEOOyiE…
jmsl: RT @roadrunning01: 12-in-1: Multi-Task Vision and Language Representation Learning pdf: https://t.co/tx6ESpQoO6 abs: https://t.co/VQPEOOyiE…
ryo_masumura: RT @arxiv_cscl: 12-in-1: Multi-Task Vision and Language Representation Learning https://t.co/XiDV5hP8eM
subhobrata1: RT @roadrunning01: 12-in-1: Multi-Task Vision and Language Representation Learning pdf: https://t.co/tx6ESpQoO6 abs: https://t.co/VQPEOOyiE…
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2.109 Mikeys
#8. Sensitivity Analysis via the Proportion of Unmeasured Confounding
Matteo Bonvini, Edward H Kennedy
In observational studies, identification of ATEs is generally achieved by assuming "no unmeasured confounding," possibly after conditioning on enough covariates. Because this assumption is both strong and untestable, a sensitivity analysis should be performed. Common approaches include modeling the bias directly or varying the propensity scores to probe the effects of a potential unmeasured confounder. In this paper, we take a novel approach whereby the sensitivity parameter is the proportion of unmeasured confounding. We consider different assumptions on the probability of a unit being unconfounded. In each case, we derive sharp bounds on the average treatment effect as a function of the sensitivity parameter and propose nonparametric estimators that allow flexible covariate adjustment. We also introduce a one-number summary of a study's robustness to the number of confounded units. Finally, we explore finite-sample properties via simulation, and apply the methods to an observational database used to assess the effects of right...
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ArtofWarm: Sensitivity Analysis via the Proportion of Unmeasured Confounding In observational studies, identification of ATEs is generally achieved by assuming “no unmeasured confounding,” possibly after conditioning on enough covariates. https://t.co/8f3QIeU9M5 https://t.co/Ad2ws9VNfF
edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely crucial for causality: holy grail is finding one that's interpretable *&* flexible @bonv3 & I propose contamination model approach, w/effect bds across % confounded https://t.co/DaMwtSiH9K
StatsPapers: Sensitivity Analysis via the Proportion of Unmeasured Confounding. https://t.co/GSYBtZB0a1
seanjtaylor: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
Stat_Ron: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
jd_wilko: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
RogerHilfiker: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
fhollenbach: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
KhoaVuUmn: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
maria__cuellar: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
_alfang: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
JulianEGerez: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
yizhoujin: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
subhobrata1: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
graduatedescent: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
PFenton_Villar: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
HAAANBIN: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
bbrsntd: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
theres_a_stat: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
TatThangVo1: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
bonv3: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
LuMaoWisc: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
statsCong: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
mathalytics: RT @edwardhkennedy: New paper alert! "Sensitivity analysis via % of unmeasured confounding" https://t.co/xmQoNAjhUJ SA is absolutely cruc…
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2.097 Mikeys
#9. Normalizing Flows for Probabilistic Modeling and Inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, Balaji Lakshminarayanan
Normalizing flows provide a general mechanism for defining expressive probability distributions, only requiring the specification of a (usually simple) base distribution and a series of bijective transformations. There has been much recent work on normalizing flows, ranging from improving their expressive power to expanding their application. We believe the field has now matured and is in need of a unified perspective. In this review, we attempt to provide such a perspective by describing flows through the lens of probabilistic modeling and inference. We place special emphasis on the fundamental principles of flow design, and discuss foundational topics such as expressive power and computational trade-offs. We also broaden the conceptual framing of flows by relating them to more general probability transformations. Lastly, we summarize the use of flows for tasks such as generative modeling, approximate inference, and supervised learning.
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DeepSpiker: Looking for something to read in your flight to #NeurIPS2019? Read about Normalizing Flows from our extensive review paper (also with new insights on how to think about and derive new flows) https://t.co/cPjQjZn3uf with @gpapamak @eric_nalisnick @DeepSpiker @balajiln @shakir_za https://t.co/EWh8Aui7n0
gpapamak: Check out our extensive review paper on normalizing flows! This paper is the product of years of thinking about flows: it contains everything we know about them, and many new insights. With @eric_nalisnick, @DeepSpiker, @shakir_za, @balajiln. https://t.co/BBymd1uSwx Thread 👇 https://t.co/er8QebcPS2
arxivml: "Normalizing Flows for Probabilistic Modeling and Inference", George Papamakarios, Eric Nalisnick, Danilo Jimenez R… https://t.co/gbvVIxPwuo
arxiv_cs_LG: Normalizing Flows for Probabilistic Modeling and Inference. George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan https://t.co/TQmlbVp0Je
hereticreader: Normalizing Flows for Probabilistic Modeling and Inference - https://t.co/9D1COPFWPY https://t.co/NqFK3hewOc
StatsPapers: Normalizing Flows for Probabilistic Modeling and Inference. https://t.co/Hc0w5Bx6yR
ari_seff: And for an extensive review, check out the just-released "Normalizing Flows for Probabilistic Modeling and Inference" (https://t.co/iAOCgNh7Ch) from @gpapamak @eric_nalisnick @DeepSpiker @balajiln @shakir_za
ballforest: RT @StatsPapers: Normalizing Flows for Probabilistic Modeling and Inference. https://t.co/Hc0w5Bx6yR
jd_mashiro: RT @StatsPapers: Normalizing Flows for Probabilistic Modeling and Inference. https://t.co/Hc0w5Bx6yR
mxwlj: RT @StatsPapers: Normalizing Flows for Probabilistic Modeling and Inference. https://t.co/Hc0w5Bx6yR
tak_yamm: RT @StatsPapers: Normalizing Flows for Probabilistic Modeling and Inference. https://t.co/Hc0w5Bx6yR
morioka: RT @StatsPapers: Normalizing Flows for Probabilistic Modeling and Inference. https://t.co/Hc0w5Bx6yR
ShirotaShin: RT @StatsPapers: Normalizing Flows for Probabilistic Modeling and Inference. https://t.co/Hc0w5Bx6yR
GiseopK: RT @StatsPapers: Normalizing Flows for Probabilistic Modeling and Inference. https://t.co/Hc0w5Bx6yR
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2.097 Mikeys
#10. Ultrafast Photorealistic Style Transfer via Neural Architecture Search
Jie An, Haoyi Xiong, Jun Huan, Jiebo Luo
The key challenge in photorealistic style transfer is that an algorithm should faithfully transfer the style of a reference photo to a content photo while the generated image should look like one captured by a camera. Although several photorealistic style transfer algorithms have been proposed, they need to rely on post- and/or pre-processing to make the generated images look photorealistic. If we disable the additional processing, these algorithms would fail to produce plausible photorealistic stylization in terms of detail preservation and photorealism. In this work, we propose an effective solution to these issues. Our method consists of a construction step (C-step) to build a photorealistic stylization network and a pruning step (P-step) for acceleration. In the C-step, we propose a dense auto-encoder named PhotoNet based on a carefully designed pre-analysis. PhotoNet integrates a feature aggregation module (BFA) and instance normalized skip links (INSL). To generate faithful stylization, we introduce multiple style transfer...
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roadrunning01: Ultrafast Photorealistic Style Transfer via Neural Architecture Search pdf: https://t.co/Tfblmc0lVm abs: https://t.co/fLQWC6wzvu https://t.co/ys7iDQuRZG
arxivml: "Ultrafast Photorealistic Style Transfer via Neural Architecture Search", Jie An, Haoyi Xiong, Jun Huan, Jiebo Luo https://t.co/HEcXcMF7w5
arxiv_cs_cv_pr: Ultrafast Photorealistic Style Transfer via Neural Architecture Search. Jie An, Haoyi Xiong, Jun Huan, and Jiebo Luo https://t.co/Ur2JBKHf61
arxiv_cscv: Ultrafast Photorealistic Style Transfer via Neural Architecture Search https://t.co/qhBT5tYAbt
arxiv_cscv: Ultrafast Photorealistic Style Transfer via Neural Architecture Search https://t.co/qhBT5ugb31
arxiv_cscv: Ultrafast Photorealistic Style Transfer via Neural Architecture Search https://t.co/qhBT5tYAbt
arxiv_csgr: Ultrafast Photorealistic Style Transfer via Neural Architecture Search https://t.co/L48XQ22Sjq
arxiv_csgr: Ultrafast Photorealistic Style Transfer via Neural Architecture Search https://t.co/L48XQ22Sjq
arxiv_csgr: Ultrafast Photorealistic Style Transfer via Neural Architecture Search https://t.co/L48XQ22Sjq
syoyo: RT @roadrunning01: Ultrafast Photorealistic Style Transfer via Neural Architecture Search pdf: https://t.co/Tfblmc0lVm abs: https://t.co/fL…
yshhrknmr: RT @roadrunning01: Ultrafast Photorealistic Style Transfer via Neural Architecture Search pdf: https://t.co/Tfblmc0lVm abs: https://t.co/fL…
nakano_muramoto: RT @arxiv_cscv: Ultrafast Photorealistic Style Transfer via Neural Architecture Search https://t.co/qhBT5tYAbt
subhobrata1: RT @roadrunning01: Ultrafast Photorealistic Style Transfer via Neural Architecture Search pdf: https://t.co/Tfblmc0lVm abs: https://t.co/fL…
AndroidBlogger: RT @roadrunning01: Ultrafast Photorealistic Style Transfer via Neural Architecture Search pdf: https://t.co/Tfblmc0lVm abs: https://t.co/fL…
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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.

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