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Deep Learning: A Critical Appraisal

Deep Learning: A Critical Appraisal

2 January 2018
G. Marcus
    HAI
    VLM
ArXivPDFHTML

Papers citing "Deep Learning: A Critical Appraisal"

24 / 124 papers shown
Title
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
F. V. Harmelen
A. T. Teije
8
67
0
29 May 2019
An Explicitly Relational Neural Network Architecture
An Explicitly Relational Neural Network Architecture
Murray Shanahan
Kyriacos Nikiforou
Antonia Creswell
Christos Kaplanis
David Barrett
M. Garnelo
NAI
3DV
GAN
25
68
0
24 May 2019
Local Relation Networks for Image Recognition
Local Relation Networks for Image Recognition
Han Hu
Zheng-Wei Zhang
Zhenda Xie
Stephen Lin
FAtt
32
498
0
25 Apr 2019
Compositional generalization in a deep seq2seq model by separating
  syntax and semantics
Compositional generalization in a deep seq2seq model by separating syntax and semantics
Jacob Russin
Jason Jo
R. C. O'Reilly
Yoshua Bengio
30
102
0
22 Apr 2019
Good-Enough Compositional Data Augmentation
Good-Enough Compositional Data Augmentation
Jacob Andreas
30
230
0
21 Apr 2019
Making AI meaningful again
Making AI meaningful again
Jobst Landgrebe
Barry F. Smith
19
35
0
09 Jan 2019
An overview of deep learning in medical imaging focusing on MRI
An overview of deep learning in medical imaging focusing on MRI
A. Lundervold
A. Lundervold
OOD
22
1,608
0
25 Nov 2018
Scalable agent alignment via reward modeling: a research direction
Scalable agent alignment via reward modeling: a research direction
Jan Leike
David M. Krueger
Tom Everitt
Miljan Martic
Vishal Maini
Shane Legg
34
397
0
19 Nov 2018
A Model for General Intelligence
A Model for General Intelligence
M. Das
AI4CE
14
0
0
06 Nov 2018
Assessing Generalization in Deep Reinforcement Learning
Assessing Generalization in Deep Reinforcement Learning
Charles Packer
Katelyn Gao
Jernej Kos
Philipp Krahenbuhl
V. Koltun
D. Song
OffRL
18
233
0
29 Oct 2018
The 30-Year Cycle In The AI Debate
The 30-Year Cycle In The AI Debate
J. Chauvet
27
8
0
08 Oct 2018
Towards Neural Theorem Proving at Scale
Towards Neural Theorem Proving at Scale
Pasquale Minervini
Matko Bosnjak
Tim Rocktaschel
Sebastian Riedel
LRM
NAI
24
38
0
21 Jul 2018
Recent Advances in Deep Learning: An Overview
Recent Advances in Deep Learning: An Overview
Matiur Rahman Minar
Jibon Naher
VLM
24
116
0
21 Jul 2018
Measuring abstract reasoning in neural networks
Measuring abstract reasoning in neural networks
David Barrett
Felix Hill
Adam Santoro
Ari S. Morcos
Timothy Lillicrap
OOD
25
356
0
11 Jul 2018
JUMPER: Learning When to Make Classification Decisions in Reading
JUMPER: Learning When to Make Classification Decisions in Reading
Xianggen Liu
Lili Mou
Haotian Cui
Zhengdong Lu
Sen Song
32
20
0
06 Jul 2018
Assessing the impact of machine intelligence on human behaviour: an
  interdisciplinary endeavour
Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour
Emilia Gómez
Carlos Castillo
V. Charisi
V. Dahl
G. Deco
...
Núria Sebastián
Xavier Serra
Joan Serrà
Songül Tolan
Karina Vold
11
11
0
07 Jun 2018
Human-like generalization in a machine through predicate learning
Human-like generalization in a machine through predicate learning
L. Doumas
Guillermo Puebla
Andrea E. Martin
NAI
33
9
0
05 Jun 2018
Relational inductive biases, deep learning, and graph networks
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia
Jessica B. Hamrick
V. Bapst
Alvaro Sanchez-Gonzalez
V. Zambaldi
...
Pushmeet Kohli
M. Botvinick
Oriol Vinyals
Yujia Li
Razvan Pascanu
AI4CE
NAI
121
3,083
0
04 Jun 2018
Opening the black box of deep learning
Opening the black box of deep learning
Dian Lei
Xiaoxiao Chen
Jianfei Zhao
AI4CE
PINN
17
26
0
22 May 2018
Extrapolation in NLP
Extrapolation in NLP
Jeff Mitchell
Pasquale Minervini
Pontus Stenetorp
Sebastian Riedel
19
20
0
17 May 2018
Deep Nets: What have they ever done for Vision?
Deep Nets: What have they ever done for Vision?
Alan Yuille
Chenxi Liu
28
100
0
10 May 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and
  Challenges
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges
Gabrielle Ras
Marcel van Gerven
W. Haselager
XAI
17
217
0
20 Mar 2018
Innateness, AlphaZero, and Artificial Intelligence
Innateness, AlphaZero, and Artificial Intelligence
G. Marcus
30
73
0
17 Jan 2018
Solutions to problems with deep learning
Solutions to problems with deep learning
J. Wolff
32
10
0
08 Jan 2018
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