id stringlengths 9 13 | content unknown | decision stringclasses 13
values | reviews listlengths 3 12 | metareview listlengths 1 3 | sentence_texts listlengths 15 712 | opinion_groups listlengths 1 114 | conflicts_validation listlengths 1 155 | rebuttal_validation listlengths 1 155 | opinions listlengths 1 155 | PDF_path stringlengths 36 43 | PDF_version stringclasses 4
values | MD_path stringlengths 34 41 | conference stringclasses 13
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
S1D8MPxA- | {
"title": "Viterbi-based Pruning for Sparse Matrix with Fixed and High Index Compression Ratio",
"abstract": "Weight pruning has proven to be an effective method in reducing the model size and computation cost while not sacrificing the model accuracy. Conventional sparse matrix formats, however, involve irregular ... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2
]
},
"scores": {
"Solid": null,
"Presentation": null,
"Novelty": null,
"Overa... | [
[
{
"role": "PC",
"data": {
"comment": "The paper proposes a new sparse matrix representation based on Viterbi algorithm with high and fixed index compression ratio regardless of the pruning rate. The method allows for faster parallel decoding and achieves improved compression of index data... | [
"It seems like the authors have carefully thought about this problem, and have come up with some elegant solutions, but I am not sold on whether it's an appropriate match for this conference, mainly because it's not clear how many machine learning people will be interested in this approach.",
"There was a time ab... | [
[
11
],
[
14
],
[
2
],
[
12
],
[
4
],
[
5
],
[
6
],
[
8
],
[
7
],
[
9
],
[
16
],
[
17
],
[
19
],
[
20
],
[
10
],
[
18
],
[
21
],
[
22
],
[
0
],
[
13
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"incorrect",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
}
],
"category": [
"SIGN-BRD"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1,
2
]
},
{
"role": "Author",... | benchmark/PDF/ICLR2018_S1D8MPxA-.pdf | openreview | benchmark/MD/ICLR2018_S1D8MPxA-.md | ICLR 2018 |
rJTutzbA- | {
"title": "On the insufficiency of existing momentum schemes for Stochastic Optimization",
"abstract": "Momentum based stochastic gradient methods such as heavy ball (HB) and Nesterov's accelerated gradient descent (NAG) method are widely used in practice for training deep networks and other supervised learning mo... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "The reviewers unanimously recommended that this paper be accepted, as it contains an important theoretical result that there are problems for which heavy-ball momentum cannot outperform SGD. The theory is backed up by solid experimental results, and th... | [
"I like the idea of the paper. Momentum and accelerations are proved to be very useful both in deterministic and stochastic optimization. It is natural that it is understood better in the deterministic case.",
"However, this comes quite naturally, as deterministic case is a bit easier ;) Indeed, just recently peo... | [
[
11
],
[
2
],
[
3
],
[
4
],
[
7
],
[
10
],
[
12
],
[
16
],
[
0
],
[
5
],
[
1,
9
],
[
8
],
[
13
],
[
17
],
[
6
],
[
14
],
[
15
],
[
18
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
}
],
"category": [
"ORIG-ANL"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
},
{
"role": "Author",
"dat... | benchmark/PDF/ICLR2018_rJTutzbA-.pdf | openreview | benchmark/MD/ICLR2018_rJTutzbA-.md | ICLR 2018 |
SJJQVZW0b | {
"title": "Hierarchical and Interpretable Skill Acquisition in Multi-task Reinforcement Learning",
"abstract": "Learning policies for complex tasks that require multiple different skills is a major challenge in reinforcement learning (RL). It is also a requirement for its deployment in real-world scenarios. This p... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "This method has a lot of strong points, but the reviewers had concerns about baselines, comparisons, and hand-engineered aspects of the method. The authors gave a strong rebuttal and made substantial updates to the paper to address the concerns. I thin... | [
"This paper aims to learn hierarchical policies by using a recursive policy structure regulated by a stochastic temporal grammar. The experiments show that the method is better than a flat policy for learning a simple set of block-related skills in minecraft (find, get, put, stack) and generalizes better to a modif... | [
[
7
],
[
28
],
[
33
],
[
32
],
[
12
],
[
22
],
[
23
],
[
27
],
[
29
],
[
6
],
[
19
],
[
21
],
[
26
],
[
2
],
[
5
],
[
9,
34
],
[
10
],
[
13
],
[
14
],
[
16
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"QUAL-MET"
]
},
... | benchmark/PDF/ICLR2018_SJJQVZW0b.pdf | openreview | benchmark/MD/ICLR2018_SJJQVZW0b.md | ICLR 2018 |
B16yEqkCZ | {
"title": "Avoiding Catastrophic States with Intrinsic Fear",
"abstract": "Many practical reinforcement learning problems contain catastrophic states that the optimal policy visits infrequently or never. Even on toy problems, deep reinforcement learners periodically revisit these states, once they are forgotten un... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5
]
},
"scores": {
"Solid": null,
"Presentation": null... | [
[
{
"role": "PC",
"data": {
"comment": "This paper presents an interesting idea that is related to imitation learning, safe exploration,\nand intrinsic motivation. However, in its current state the paper needs improvement in clarity. There are also some concerns about the number of hyperpara... | [
"SUMMARY\nThe paper proposes an RL algorithm that combines the DQN algorithm with a fear model. The fear model is trained in parallel to predict catastrophic states. Its output is used to penalize the Q learning target.",
"COMMENTS\nNot convinced about the fact that an agent forgets about catastrophic states. B... | [
[
11
],
[
16
],
[
18
],
[
4
],
[
2
],
[
6
],
[
8
],
[
9
],
[
10,
13
],
[
17
],
[
20
],
[
1
],
[
3
],
[
12
],
[
14
],
[
15
],
[
19
],
[
0
],
[
5
],
[
7
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"incorrect",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
},
{
"role": "Author",
"data": [
... | benchmark/PDF/ICLR2018_B16yEqkCZ.pdf | openreview | benchmark/MD/ICLR2018_B16yEqkCZ.md | ICLR 2018 |
rJl3yM-Ab | {
"title": "Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering",
"abstract": "Very recently, it comes to be a popular approach for answering open-domain questions by first searching question-related passages, then applying reading comprehension models to extract answers. Existing works usu... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2
]
},
"scores": {
"Solid": null,
"Presentation": null,
"Novelty": null,
"Overa... | [
[
{
"role": "PC",
"data": {
"comment": "The pros and cons of this paper cited by the reviewers can be summarized below:\n\nPros:\n* Solid experimental results against strong baselines on a task of great interest\n* Method presented is appropriate for the task\n* Paper is presented relatively... | [
"The paper is clear, although there are many English mistakes (that should be corrected).",
"The proposed method aggregates answers from multiple passages in the context of QA. The new method is motivated well and departs from prior work. Experiments on three datasets show the proposed method to be notably better... | [
[
8
],
[
0
],
[
9
],
[
14
],
[
11
],
[
4
],
[
5,
13
],
[
1,
3
],
[
10
],
[
12
],
[
6
],
[
7
],
[
2
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0,
2
]
},
{
"role": "Author",
"data": [
3
]
}
],
"category": [
"CLAR-WRT"
]
},
{
"sentences": [
{
"role": "Reviewer 1",... | benchmark/PDF/ICLR2018_rJl3yM-Ab.pdf | openreview | benchmark/MD/ICLR2018_rJl3yM-Ab.md | ICLR 2018 |
SyJS-OgR- | {
"title": "Multi-level Residual Networks from Dynamical Systems View",
"abstract": "Deep residual networks (ResNets) and their variants are widely used in many computer vision applications and natural language processing tasks. However, the theoretical principles for designing and training ResNets are still not f... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6
]
},
"scores": {
"Solid": null,
"Pres... | [
[
{
"role": "PC",
"data": {
"comment": "this submission proposes a learning algorithm for resnets based on their interpreration of them as a discrete approximation to a continuous-time dynamical system. all the reviewers have found the submission to be clearly written, well motivated and ha... | [
"This paper interprets deep residual network as a dynamic system, and proposes a novel training algorithm to train it in a constructive way. On three image classification datasets, the proposed algorithm speeds up the training process without sacrificing accuracy. The paper is interesting and easy to follow.",
"I... | [
[
8
],
[
10
],
[
13
],
[
2
],
[
0,
12
],
[
1
],
[
3
],
[
19
],
[
4
],
[
7
],
[
16
],
[
17
],
[
18
],
[
6
],
[
9
],
[
14
],
[
5
],
[
11
],
[
15
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"ORIG-MTH"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1,
2
]
},
{
"role": "Author",
... | benchmark/PDF/ICLR2018_SyJS-OgR-.pdf | openreview | benchmark/MD/ICLR2018_SyJS-OgR-.md | ICLR 2018 |
rJzIBfZAb | {
"title": "Towards Deep Learning Models Resistant to Adversarial Attacks",
"abstract": "Recent work has demonstrated that neural networks are vulnerable to adversarial examples, i.e., inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. To address this problem, ... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "This paper presents new results on adversarial training, using the framework of robust optimization. Its minimax nature allows for principled methods of both training and attacking neural networks.\n\nThe reviewers were generally positive about its con... | [
"This paper proposes to look at making neural networks resistant to adversarial loss through the framework of saddle-point problems. They show that, on MNIST, a PGD adversary fits this framework and allows the authors to train very robust models. They also show encouraging results for robust CIFAR-10 models, but wi... | [
[
16
],
[
25
],
[
26
],
[
3
],
[
4
],
[
6
],
[
5
],
[
20
],
[
10
],
[
18
],
[
1
],
[
8
],
[
9
],
[
11
],
[
12
],
[
13
],
[
14
],
[
17
],
[
19
],
[
21
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2,
3
]
},
{
"role": "Au... | benchmark/PDF/ICLR2018_rJzIBfZAb.pdf | openreview | benchmark/MD/ICLR2018_rJzIBfZAb.md | ICLR 2018 |
ByxLBMZCb | {
"title": "Learning Deep Models: Critical Points and Local Openness",
"abstract": "With the increasing interest in deeper understanding of the loss surface of many non-convex deep models, this paper presents a unifying framework to study the local/global optima equivalence of the optimization problems arising fro... | Invite to Workshop Track | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
]
},... | [
[
{
"role": "PC",
"data": {
"comment": "The paper nicely unifies previous results and develops the property of local openness. While interesting, I find the application to multi-layer linear networks extremely limiting. There appears to be a sub-field in theory now focusing on solely multi-l... | [
"Summary: The paper focuses on the characterization of the landscape of deep neural networks; i.e., when and why local minima are global, what are the conditions for saddle critical points, etc. The paper covers a somewhat wide range of deep nets (from shallow with linear activation to deeper with non-linear activa... | [
[
12
],
[
4
],
[
5
],
[
6
],
[
16
],
[
18
],
[
1
],
[
15
],
[
2
],
[
7,
13,
20
],
[
9
],
[
10
],
[
11
],
[
17
],
[
19
],
[
3
],
[
0
],
[
8
],
[
14
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"ORIG-ANL"
]
},
... | benchmark/PDF/ICLR2018_ByxLBMZCb.pdf | openreview | benchmark/MD/ICLR2018_ByxLBMZCb.md | ICLR 2018 |
HJGv1Z-AW | {
"title": "Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input",
"abstract": "The ability of algorithms to evolve or learn (compositional) communication protocols has traditionally been studied in the language evolution literature through the use of emergent communication ta... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "Important problem (analyzing the properties of emergent languages in multi-agent reference games), a number of interesting analyses (both with symbolic and pixel inputs), reaching a finding that varying the environment and restrictions on language resu... | [
"This paper presents a set of studies on emergent communication protocols in referential games that use either symbolic object representations or pixel-level representations of generated images as input. The work is extremely creative and packed with interesting experiments.",
"I have three main comments.",
"* ... | [
[
8
],
[
19
],
[
30
],
[
31
],
[
9
],
[
10
],
[
2
],
[
11
],
[
12
],
[
14,
56
],
[
15
],
[
17
],
[
18
],
[
20
],
[
23
],
[
24
],
[
26
],
[
28
],
[
29
],
[
3... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer",
"data": [
1
]
}
],
"category": [
"N/A"
]
},
{
... | benchmark/PDF/ICLR2018_HJGv1Z-AW.pdf | openreview | benchmark/MD/ICLR2018_HJGv1Z-AW.md | ICLR 2018 |
HktRlUlAZ | {
"title": "Polar Transformer Networks",
"abstract": "Convolutional neural networks (CNNs) are inherently equivariant to translation. Efforts to embed other forms of equivariance have concentrated solely on rotation. We expand the notion of equivariance in CNNs through the Polar Transformer Network (PTN). PTN combi... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "The paper proposes a new deep architecture based on polar transformation for improving rotational invariance. The proposed method is interesting and the experimental results strong classification performance on small/medium-scale datasets (e.g., rotate... | [
"This paper proposes a method to learn networks invariant to translation and equivariant to rotation and scale of arbitrary precision. The idea is to jointly train",
"- a network predicting a polar origin,",
"- a module transforming the image into a log-polar representation according to the predicted origin,",
... | [
[
24
],
[
25
],
[
26
],
[
5
],
[
6
],
[
28
],
[
10
],
[
13
],
[
19
],
[
20
],
[
21
],
[
22
],
[
7
],
[
8,
33
],
[
9
],
[
23
],
[
27,
31
],
[
15
],
[
16
],
[... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"c... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer",
"data": [
1
]
}
],
"category": [
"N/A"
]
},
{
... | benchmark/PDF/ICLR2018_HktRlUlAZ.pdf | openreview | benchmark/MD/ICLR2018_HktRlUlAZ.md | ICLR 2018 |
B17JTOe0- | {
"title": "Emergence of grid-like representations by training recurrent neural networks to perform spatial localization",
"abstract": "Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties. The Entorhinal Cortex (EC) of the mammalian brain co... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
]
},... | [
[
{
"role": "PC",
"data": {
"comment": "This work shows how activation patterns of units reminiscent of grid and border cells emerge in RNNs trained on navigation tasks. While the ICLR audience is not mainly focused on neuroscience, the findings of the paper are quite intriguing, and grid ce... | [
"The authors train an RNN to perform deduced reckoning (ded reckoning) for spatial navigation, and then study the responses of the model neurons in the RNN. They find many properties reminiscent of neurons in the mammalian entorhinal cortex (EC): grid cells, border cells, etc.",
"When regularization of the networ... | [
[
9
],
[
15
],
[
10
],
[
11
],
[
12
],
[
1,
2,
7
],
[
13
],
[
4
],
[
5
],
[
14
],
[
16
],
[
3
],
[
8
],
[
0
],
[
6
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1,
2
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
3
]
},
{
"role": "Au... | benchmark/PDF/ICLR2018_B17JTOe0-.pdf | openreview | benchmark/MD/ICLR2018_B17JTOe0-.md | ICLR 2018 |
ByJIWUnpW | {
"title": "Automatically Inferring Data Quality for Spatiotemporal Forecasting",
"abstract": "Spatiotemporal forecasting has become an increasingly important prediction task in machine learning and statistics due to its vast applications, such as climate modeling, traffic prediction, video caching predictions, and... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "With an 8-6-6 rating all reviewers agreed that this paper is past the threshold for acceptance.\n\nThe quality of the paper appears to have increased during the review cycle due to interactions with the reviewers. The paper addresses issues related to... | [
"The paper is an application of neural nets to data quality assessment. The authors introduce a new definition of data quality that relies on the notion of local variation defined in (Zhou and Schölkopf, 2004), and they extend it to multiple heterogenous data sources. The data quality function is learned using a GC... | [
[
23
],
[
27
],
[
28
],
[
6
],
[
7,
14
],
[
11,
25
],
[
15
],
[
12
],
[
30
],
[
31
],
[
9
],
[
17
],
[
24
],
[
32
],
[
33
],
[
5
],
[
10
],
[
18
],
[
8
],
[... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"c... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
},
{
"role": "Author",
"data": [
... | benchmark/PDF/ICLR2018_ByJIWUnpW.pdf | openreview | benchmark/MD/ICLR2018_ByJIWUnpW.md | ICLR 2018 |
HJOQ7MgAW | {
"title": "Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum",
"abstract": "Long short-term memory networks (LSTMs) were introduced to combat vanishing gradients in simple recurrent neural networks (S-RNNs) by augmenting them with additive recurrent connections controlled by gates. We pres... | Reject | [
[
{
"role": "Reviewer",
"data": {
"value": {
"comment": [
0,
1,
2,
3
]
}
}
},
{
"role": "Author",
"data": {
"value": {
"comment": [
4,
5,
... | [
[
{
"role": "PC",
"data": {
"comment": "The paper performs an ablation analysis on LSTM, showing that the gating component is the most important. There is little novelty in the analysis, and in its current form, its impact is rather limited."
}
}
]
] | [
"Although I welcome this work, I'm afraid that the authors jumped to the conclusion \"that the content and output layers are redundant, and that the space of element-wise weighted sums is sufficiently powerful to compete with fully parameterized LSTMs\" too quickly.",
"All comparison against LSTMs (and of course,... | [
[
20
],
[
17
],
[
19
],
[
16
],
[
18
],
[
8
],
[
4
],
[
5
],
[
6
],
[
15
],
[
0
],
[
1
],
[
2
],
[
9
],
[
10
],
[
13
],
[
14
],
[
22
],
[
11
],
[
3
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"incorrect",
"correct",
"correct",
"incorrect",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1 Further Reply",
"data": [
0
]
},
{
"role": "Author",
"data": [
12,
17,
18,
19
]
}
],
"category": [
"QUAL-EXP"
]
},
{
"sentenc... | benchmark/PDF/ICLR2018_HJOQ7MgAW.pdf | openreview | benchmark/MD/ICLR2018_HJOQ7MgAW.md | ICLR 2018 |
SJIA6ZWC- | {
"title": "Stochastic Hyperparameter Optimization through Hypernetworks",
"abstract": "Machine learning models are usually tuned by nesting optimization of model weights inside the optimization of hyperparameters. We give a method to collapse this nested optimization into joint stochastic optimization of both wei... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "The paper is interesting, and the update to the paper and additional experiments has already improved it in many ways, but the paper still does still not have as much impact as it could, by further strengthening the comparisons and usefulness in many o... | [
"This paper introduces the use of hyper-networks for hyper-parameter optimization in the context of neural networks. A hyper-network is a network that has been trained to find optimal weights for another neural network on a particular learning task. This hyper-network can also be trained using gradient descent, and... | [
[
28
],
[
1
],
[
10
],
[
16
],
[
18
],
[
21
],
[
24
],
[
31
],
[
33
],
[
2
],
[
9
],
[
12
],
[
15,
26
],
[
25
],
[
35
],
[
3,
11
],
[
4
],
[
5,
14
],
[
7
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
]
}
],
"category": [
"CLAR-WRT"... | benchmark/PDF/ICLR2018_SJIA6ZWC-.pdf | openreview | benchmark/MD/ICLR2018_SJIA6ZWC-.md | ICLR 2018 |
Hko85plCW | {
"title": "Monotonic Chunkwise Attention",
"abstract": "Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction. To address these issues, we... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7
]
},
"scores": {
"Solid": null,
... | [
[
{
"role": "PC",
"data": {
"comment": "This clearly written paper describes a simple extension to hard monotonic attention -- the addition of a soft attention mechanism that operates over a fixed length window of inputs that ends at the point selected by the hard attention mechanism. Exper... | [
"This paper proposes a small modification to the monotonic attention in [1] by adding a soft attention to the segment predicted by the monotonic attention. The paper is very well written and easy to follow. The experiments are also convincing. Here are a few suggestions and questions to make the paper stronger.",
... | [
[
3
],
[
17
],
[
13
],
[
6
],
[
11
],
[
15
],
[
16
],
[
4,
8
],
[
7
],
[
10
],
[
14
],
[
1
],
[
2
],
[
5
],
[
9
],
[
0
],
[
12
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1,
2
]
},
{
"role": "Author",
... | benchmark/PDF/ICLR2018_Hko85plCW.pdf | openreview | benchmark/MD/ICLR2018_Hko85plCW.md | ICLR 2018 |
SyZI0GWCZ | {
"title": "Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models",
"abstract": "Many machine learning algorithms are vulnerable to almost imperceptible perturbations of their inputs. So far it was unclear how much risk adversarial perturbations carry for the safety of real-... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8
]
},
"scores": {
... | [
[
{
"role": "PC",
"data": {
"comment": "The reviewers all agree this is a well written and interesting paper describing a novel black box adversarial attack. There were missing relevant references in the original submission, but these have been added. I would suggest the authors follow th... | [
"The authors identify a new security threat for deep learning: Decision-based adversarial attacks. This new class of attacks on deep learning systems requires from an attacker only the knowledge of class labels (previous attacks required more information, e.g., access to a gradient oracle). Unsurprisingly, since th... | [
[
3
],
[
9
],
[
10
],
[
8
],
[
5
],
[
6,
12
],
[
13
],
[
0
],
[
11
],
[
15
],
[
16
],
[
1
],
[
17
],
[
2
],
[
4
],
[
7
],
[
14
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
},
{
"role": "Author",
"data": [
11
]
}
],
"category": [
"ORIG-PROB"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"d... | benchmark/PDF/ICLR2018_SyZI0GWCZ.pdf | openreview | benchmark/MD/ICLR2018_SyZI0GWCZ.md | ICLR 2018 |
rkZvSe-RZ | {
"title": "Ensemble Adversarial Training: Attacks and Defenses",
"abstract": "Adversarial examples are perturbed inputs designed to fool machine learning models. Adversarial training injects such examples into training data to increase robustness. To scale this technique to large datasets, perturbations are crafte... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6
]
},
"scores": {
"Solid": null,
"Pres... | [
[
{
"role": "PC",
"data": {
"comment": "The paper studies a defense against adversarial examples that re-trains convolutional networks on adversarial examples constructed to attack pre-trained networks. Whilst the proposed approach is not very original, the paper does present a solid empiric... | [
"This paper proposes ensemble adversarial training, in which adversarial examples crafted on other static pre-trained models are used in the training phase. Their method makes deep networks robust to black-box attacks, which was empirically demonstrated.",
"This is an empirical paper. The ideas are simple and not... | [
[
8
],
[
6
],
[
9
],
[
4
],
[
13
],
[
14
],
[
3
],
[
10
],
[
12
],
[
7
],
[
15
],
[
1
],
[
2
],
[
0
],
[
5
],
[
11
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1,
2
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
3
]
}
],
"category": [
... | benchmark/PDF/ICLR2018_rkZvSe-RZ.pdf | openreview | benchmark/MD/ICLR2018_rkZvSe-RZ.md | ICLR 2018 |
Hkp3uhxCW | {
"title": "Revisiting Bayes by Backprop",
"abstract": "In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks.\nFirstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at o... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "Thank you for submitting you paper to ICLR. The revision improved the paper e.g. moving Appendix A3 to the main text has improved clarity, but, like reviewer 3, I still found section 4 hard to follow. As the authors suggest, shifting the terminology to... | [
"*Summary*\nThe paper applies variational inference (VI) with the 'reparameterisation' trick for Bayesian recurrent neural networks (BRNNs). The paper first considers the \"Bayes by Backprop\" approach of Blundell et al. (2015) and then modifies the BRNN model with a hierarchical prior over the network parameters, ... | [
[
9
],
[
5
],
[
6
],
[
7
],
[
8
],
[
17
],
[
22
],
[
24
],
[
25
],
[
1
],
[
2
],
[
3
],
[
14
],
[
23
],
[
15
],
[
4
],
[
11
],
[
12
],
[
16
],
[
20
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"inc... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"ORIG-MTH"
]
},
... | benchmark/PDF/ICLR2018_Hkp3uhxCW.pdf | openreview | benchmark/MD/ICLR2018_Hkp3uhxCW.md | ICLR 2018 |
rkPLzgZAZ | {
"title": "Modular Continual Learning in a Unified Visual Environment",
"abstract": " A core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly. Here, we describe a modular continual reinforcement learning paradigm inspired by these abilities. We first introduce... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "Important problem (modular continual RL) and novel contributions. The initial submission was judged to be a little dense and hard to read, but the authors have been responsive in responding and updating the paper. I support accepting this paper. "
... | [
"The paper comprises several ideas to study the continual learning problem.",
"First, they show an ad-hoc designed environment, namely the Touchstream environment, in which both inputs and actions are represented in a huge space: as it happens with humans – for example when they are using a touch screen – the res... | [
[
7
],
[
14
],
[
15
],
[
16
],
[
17
],
[
19
],
[
28
],
[
1
],
[
3
],
[
8
],
[
11
],
[
12
],
[
13
],
[
18
],
[
20
],
[
24
],
[
26
],
[
30
],
[
31
],
[
32
],
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"c... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1,
2,
3,
4
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
5,
6
... | benchmark/PDF/ICLR2018_rkPLzgZAZ.pdf | openreview | benchmark/MD/ICLR2018_rkPLzgZAZ.md | ICLR 2018 |
Hk6kPgZA- | {
"title": "Certifying Some Distributional Robustness with Principled Adversarial Training",
"abstract": "Neural networks are vulnerable to adversarial examples and researchers have proposed many heuristic attack and defense mechanisms. We address this problem through the principled lens of distributionally robust ... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2
]
},
"scores": {
"Solid": null,
"Presentation": null,
"Novelty": null,
"Overa... | [
[
{
"role": "AC",
"data": {
"comment": "You have been contacted now by the Area Chair and the Program Chair and asked to respond to comments by the Area Chair. It is imperative that you respond."
}
},
{
"role": "Author",
"data": {
"value": {
"comme... | [
"This paper proposes a principled methodology to induce distributional robustness in trained neural nets with the purpose of mitigating the impact of adversarial examples. The idea is to train the model to perform well not only with respect to the unknown population distribution, but to perform well on the worst-ca... | [
[
0
]
] | [
"correct"
] | [
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1,
2,
3,
4,
5,
6,
7
]
}
],
"category": [
"ORIG-COM"
]
}
] | benchmark/PDF/ICLR2018_Hk6kPgZA-.pdf | openreview | benchmark/MD/ICLR2018_Hk6kPgZA-.md | ICLR 2018 |
H1tSsb-AW | {
"title": "Variance Reduction for Policy Gradient with Action-Dependent Factorized Baselines",
"abstract": "Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates. The high variance problem is particularly exasperated in problems with l... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "The reviewers are satisfied that this paper makes a good contribution to policy gradient methods."
}
}
]
] | [
"This paper presents methods to reduce the variance of policy gradient using an action dependent baseline. Such action dependent baseline can be used in settings where the action can be decomposed into factors that are conditionally dependent given the state. The paper:",
"(1) shows that using separate baselines ... | [
[
11
],
[
22
],
[
24
],
[
7
],
[
8
],
[
16
],
[
28
],
[
29
],
[
30
],
[
1
],
[
9
],
[
13
],
[
18
],
[
3
],
[
10
],
[
14
],
[
15
],
[
21
],
[
4
],
[
20
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer",
"data": [
1
]
}
],
"category": [
"CLAR-WRT"
]
},
{... | benchmark/PDF/ICLR2018_H1tSsb-AW.pdf | openreview | benchmark/MD/ICLR2018_H1tSsb-AW.md | ICLR 2018 |
BJIgi_eCZ | {
"title": "FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension",
"abstract": "This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives. First, it puts forward a novel concept of \"History of Word\" to charac... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "State-of-the-art results on Squad (at least at time of submission) with a nice model. Authors have since applied the model to additional tasks (SNLI). Good discussion with reviewers, well written submission and all reviewers suggest acceptance. "
... | [
"The paper first analyzes recent works in machine reading comprehension (largely centered around SQuAD), and mentions their common trait that the attention is not \"fully-aware\" of all levels of abstraction, e.g. word-level, phrase-level, etc. In turn, the paper proposes a model that performs attention at all leve... | [
[
2
],
[
7
],
[
15
],
[
1,
14
],
[
0,
3,
12
],
[
6
],
[
23
],
[
10
],
[
8,
11
],
[
16
],
[
21
],
[
5
],
[
9
],
[
19
],
[
22
],
[
24
],
[
4
],
[
13,
17
],
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"i... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"ORIG-MTH"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"CLAR-WRT"
]
... | benchmark/PDF/ICLR2018_BJIgi_eCZ.pdf | openreview | benchmark/MD/ICLR2018_BJIgi_eCZ.md | ICLR 2018 |
BkDB51WR- | {
"title": "Learning temporal evolution of probability distribution with Recurrent Neural Network",
"abstract": "We propose to tackle a time series regression problem by computing temporal evolution of a probability density function to provide a probabilistic forecast. A Recurrent Neural Network (RNN) based model i... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8
]
},
"scores": {
... | [
[
{
"role": "PC",
"data": {
"comment": "Thank you for submitting you paper to ICLR. Two of the reviewers are concerned that the paper’s contributions are not significant enough —either in terms of the theoretical or experimental contribution -- to warrant publication. The authors have improv... | [
"Interesting ideas that extend LSTM to produce probabilistic forecasts for univariate time series, experiments are okay. Unclear if this would work at all in higher-dimensional time series. It is also unclear to me what are the sources of the uncertainties captured.",
"The author proposed to incorporate 2 differe... | [
[
10
],
[
7
],
[
11
],
[
11
],
[
2
],
[
8
],
[
12
],
[
0
],
[
4
],
[
5
],
[
9
],
[
13
],
[
1
],
[
3
],
[
6
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0,
1,
3
]
},
{
"role": "Author",
"data": [
10,
11,
12,
13,
14,
15,
33
]
}
],
"cat... | benchmark/PDF/ICLR2018_BkDB51WR-.pdf | openreview | benchmark/MD/ICLR2018_BkDB51WR-.md | ICLR 2018 |
HkwZSG-CZ | {
"title": "Breaking the Softmax Bottleneck: A High-Rank RNN Language Model",
"abstract": "We formulate language modeling as a matrix factorization problem, and show that the expressiveness of Softmax-based models (including the majority of neural language models) is limited by a Softmax bottleneck. Given that natu... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "Viewing language modeling as a matrix factorization problem, the authors argue that the low rank of word embeddings used by such models limits their expressivity and show that replacing the softmax in such models with a mixture of softmaxes provides an... | [
"The authors argue in this paper that due to the limited rank of the context-to-vocabulary logit matrix in the currently used version of the softmax output layer, it is not able to capture the full complexity of language.",
"As a result, they propose to use a mixture of softmax output layers instead where the mi... | [
[
24
],
[
3
],
[
10
],
[
22
],
[
0
],
[
23
],
[
20
],
[
4,
17,
27
],
[
5,
11,
15
],
[
6
],
[
7,
25
],
[
16
],
[
18
],
[
26
],
[
19
],
[
2,
12
],
[
14
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"c... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"ORIG-MTH"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
]
}
],
"category": [
"SIGN... | benchmark/PDF/ICLR2018_HkwZSG-CZ.pdf | openreview | benchmark/MD/ICLR2018_HkwZSG-CZ.md | ICLR 2018 |
SkZxCk-0Z | {
"title": "Can Neural Networks Understand Logical Entailment?",
"abstract": "We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task. We use this task to compare a series of arch... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
]
},
"scores... | [
[
{
"role": "PC",
"data": {
"comment": "This paper studies the problem of modeling logical structure in a neural model. It introduces a data set for probing various existing models and proposes a new model that addresses shortcomings in existing ones. The reviewers point out that there is ... | [
"SUMMARY\nThe paper is fairly broad in what it is trying to achieve, but the approach is well thought out. The purpose of the paper is to investigate the effectiveness of prior machine learning methods with predicting logical entailment and then provide a new model designed for the task. Explicitly, the paper asks ... | [
[
1
],
[
2
],
[
3
],
[
4
],
[
5,
12
],
[
8
],
[
14
],
[
11
],
[
16
],
[
15
],
[
9
],
[
7
],
[
13
],
[
19
],
[
21
],
[
6,
17
],
[
18
],
[
22
],
[
20
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"CLAR-WRT",
"QUAL... | benchmark/PDF/ICLR2018_SkZxCk-0Z.pdf | openreview | benchmark/MD/ICLR2018_SkZxCk-0Z.md | ICLR 2018 |
HkCsm6lRb | {
"title": "Generative Models of Visually Grounded Imagination",
"abstract": "It is easy for people to imagine what a man with pink hair looks like, even if they have never seen such a person before. We call the ability to create images of novel semantic concepts visually grounded imagination. In this paper, we sho... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4
]
},
"scores": {
"Solid": null,
"Presentation": null,
"No... | [
[
{
"role": "PC",
"data": {
"comment": "All three reviewers recommend acceptance. Good work, accept"
}
}
]
] | [
"The authors propose a generative method that can produce images along a hierarchy of specificity, i.e. both when all relevant attributes are specified, and when some are left undefined, creating a more abstract generation task.",
"Pros:\n+ The results demonstrating the method's ability to generate results for (1... | [
[
3
],
[
4
],
[
12
],
[
15
],
[
2
],
[
6
],
[
14
],
[
16
],
[
18
],
[
7
],
[
9
],
[
10
],
[
1
],
[
11
],
[
17
],
[
19
],
[
8
],
[
0
],
[
5
],
[
13
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"QUAL-EXP"
]
},
... | benchmark/PDF/ICLR2018_HkCsm6lRb.pdf | openreview | benchmark/MD/ICLR2018_HkCsm6lRb.md | ICLR 2018 |
S19dR9x0b | {
"title": "Alternating Multi-bit Quantization for Recurrent Neural Networks",
"abstract": "Recurrent neural networks have achieved excellent performance in many applications. However, on portable devices with limited resources, the models are often too large to deploy. For applications on the server with large sca... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "The reviewers unanimously agree that this paper is worth publication at ICLR. Please address the feedback of the reviewers and discuss exactly how the potential speed up rates are computed in the appendix. I speed up rates to be different for different... | [
"Summary of the paper\nThe authors propose a new way to perform multi-bit quantization based on greedy approximation and binary search tree for RNNs. They first show how this method, applied to the parameters only, performs on pre-trained networks and show great performances compared to other existing techniques on... | [
[
13
],
[
1
],
[
5
],
[
6
],
[
7
],
[
10
],
[
11
],
[
12
],
[
16,
22
],
[
17
],
[
8
],
[
26
],
[
4
],
[
9
],
[
20
],
[
24
],
[
25
],
[
27
],
[
3
],
[
18
]... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
]
}
],
"category": [
"CLAR-WRT"... | benchmark/PDF/ICLR2018_S19dR9x0b.pdf | openreview | benchmark/MD/ICLR2018_S19dR9x0b.md | ICLR 2018 |
rkgOLb-0W | {
"title": "Neural Language Modeling by Jointly Learning Syntax and Lexicon",
"abstract": "We propose a neural language model capable of unsupervised syntactic structure induction. The model leverages the structure information to form better semantic representations and better language modeling. Standard recurrent ... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"value": {
"comment": [
0,
1,
2,
3,
4,
5,
6,
7,
8
]
}
}
},
{
"role": "Author",
"data": {
... | [
[
{
"role": "PC",
"data": {
"comment": "Nice language modeling paper with consistently high scores. The model structure is neat and the results are solid. Good ICLR-type paper with contributions mostly on the ML side and experiments on a (simple) NLP task."
}
}
]
] | [
"The paper proposes a very cute idea. My questions/comment:",
"1. can you compute p(x_t+1|x0...x_t) (eq 3) by marginalising over g?",
"2. is the idea of using \"syntactic distance\" related to any linguistic theory?",
"3. I think eq 5 has a typo: is it g_i or g_t'?",
"4. the last line on page 4: d_{K-1} (ca... | [
[
3
],
[
4
],
[
6
],
[
16,
17,
19
],
[
22
],
[
14
],
[
2
],
[
5
],
[
7
],
[
8
],
[
13
],
[
15
],
[
1,
10
],
[
11
],
[
12
],
[
0
],
[
9
],
[
18
],
[
20
],
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1 Further Reply",
"data": [
1
]
},
{
"role": "Author",
... | benchmark/PDF/ICLR2018_rkgOLb-0W.pdf | openreview | benchmark/MD/ICLR2018_rkgOLb-0W.md | ICLR 2018 |
Hkfmn5n6W | {
"title": "Exponentially vanishing sub-optimal local minima in multilayer neural networks",
"abstract": "Background: Statistical mechanics results (Dauphin et al. (2014); Choromanska et al. (2015)) suggest that local minima with high error are exponentially rare in high dimensions. However, to prove low error guar... | Invite to Workshop Track | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4
]
},
"scores": {
"Solid": null,
"Presentation": null,
"No... | [
[
{
"role": "PC",
"data": {
"comment": "The paper analyzes neural network with hidden layer of piecewise linear units, a single output, and a quadratic loss. The reviewers find the results incremental and not \"surprising\", and also complained about comparison with previous work. I think th... | [
"This paper studies the question: Why does SGD on deep network is often successful, despite the fact that the objective induces bad local minima?",
"The approach in this paper is to study a standard MNN with one hidden layer. They show that in an overparametrized regime, where the number of parameters is logarith... | [
[
2
],
[
13
],
[
19,
24
],
[
8
],
[
12
],
[
16
],
[
17
],
[
18
],
[
21
],
[
22
],
[
1,
7
],
[
6
],
[
5
],
[
9
],
[
10
],
[
11
],
[
14
],
[
20
],
[
23
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"incorrect",
"incorrect",
"incorrect",
"correct",
"incorrect",
"correct",
"incorrect",
"... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2,
3
]
}
],
"category": [
... | benchmark/PDF/ICLR2018_Hkfmn5n6W.pdf | openreview | benchmark/MD/ICLR2018_Hkfmn5n6W.md | ICLR 2018 |
HkCnm-bAb | {
"title": "Can Deep Reinforcement Learning solve Erdos-Selfridge-Spencer Games?",
"abstract": "Deep reinforcement learning has achieved many recent successes, but our understanding of its strengths and limitations is hampered by the lack of rich environments in which we can fully characterize optimal behavior, and... | Invite to Workshop Track | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "The paper introduces an interesting family of two-player zero-sum games with tunable complexity, called Erdos-Selfridge-Spencer games, as a new domain for RL. The authors report on extensive empirical results using a wide variety of training methods, ... | [
"The paper presents Erdos-Selfridge-Spencer games as environments for investigating",
"deep reinforcement learning algorithms. The proposed games are interesting and clearly challenging, but I am not sure what they tell us about the algorithms chosen to test them. There are some clarity issues with the justificat... | [
[
12,
19,
58
],
[
13
],
[
20
],
[
24,
56
],
[
28
],
[
30
],
[
43
],
[
52
],
[
57
],
[
22
],
[
42
],
[
6,
35
],
[
11,
27,
27
],
[
16,
38
],
[
17,
17,
37,
37
],... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct"... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"QUAL-EXP",
"CLAR... | benchmark/PDF/ICLR2018_HkCnm-bAb.pdf | openreview | benchmark/MD/ICLR2018_HkCnm-bAb.md | ICLR 2018 |
B1Yy1BxCZ | {
"title": "Don't Decay the Learning Rate, Increase the Batch Size",
"abstract": "It is common practice to decay the learning rate. Here we show one can usually obtain the same learning curve on both training and test sets by instead increasing the batch size during training. This procedure is successful for stocha... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
]
},
"scores... | [
[
{
"role": "PC",
"data": {
"comment": "Pros:\n+ Nice demonstration of the equivalence between scaling the learning rate and increasing the batch size in SGD optimization.\n\nCons:\n- While reporting convergence as a function of number of parameter updates is consistent, the paper would be m... | [
"The paper analyzes the the effect of increasing the batch size in stochastic gradient descent as an alternative to reducing the learning rate, while keeping the number of training epochs constant. This has the advantage that the training process can be better parallelized, allowing for faster training if hundreds ... | [
[
2
],
[
7
],
[
34
],
[
3
],
[
4
],
[
32
],
[
8
],
[
16
],
[
26
],
[
17
],
[
19
],
[
20
],
[
21
],
[
27
],
[
33
],
[
36
],
[
1
],
[
5
],
[
6
],
[
10
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1,
2
]
},
{
"role": "Author",
... | benchmark/PDF/ICLR2018_B1Yy1BxCZ.pdf | openreview | benchmark/MD/ICLR2018_B1Yy1BxCZ.md | ICLR 2018 |
BJDH5M-AW | {
"title": "Synthesizing Robust Adversarial Examples",
"abstract": "Neural network-based classifiers parallel or exceed human-level accuracy on many common tasks and are used in practical systems. Yet, neural networks are susceptible to adversarial examples, carefully perturbed inputs that cause networks to misbeha... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "This paper studies the problem of synthesizing adversarial examples that will succeed at fooling a classification system under unknown viewpoint, lighting, etc conditions. For that purpose, the authors propose a data-augmentation technique (called \"EO... | [
"Summary: This work proposes a way to create 3D objects to fool the classification of their pictures from different view points by a neural network.",
"Rather than optimizing the log-likelihood of a single example, the optimization if performed over a the expectation of a set of transformations of sample images. ... | [
[
5
],
[
7
],
[
6
],
[
3
],
[
8
],
[
11
],
[
24
],
[
12
],
[
1
],
[
2
],
[
22
],
[
23,
26
],
[
4
],
[
10
],
[
15
],
[
18
],
[
21
],
[
13
],
[
14
],
[
19
]... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
]
}
],
"category": [
"ORIG-PROB... | benchmark/PDF/ICLR2018_BJDH5M-AW.pdf | openreview | benchmark/MD/ICLR2018_BJDH5M-AW.md | ICLR 2018 |
SyX0IeWAW | {
"title": "META LEARNING SHARED HIERARCHIES",
"abstract": "We develop a metalearning approach for learning hierarchically structured poli- cies, improving sample efficiency on unseen tasks through the use of shared primitives—policies that are executed for large numbers of timesteps. Specifi- cally, a set of primi... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8
]
},
"scores": {
... | [
[
{
"role": "PC",
"data": {
"comment": "This paper presents a fairly straightforward algorithm for learning a set of sub-controllers that can be re-used between tasks. The development of these concepts in a relatively clear way is a nice contribution. However, the real problem is how niche... | [
"This paper considers the reinforcement learning problem setup in which an agent must solve not one, but a set of tasks in some domain, in which the state space and action space are fixed. The authors consider the problem of learning a useful set of ‘sub policies’ that can be shared between tasks so as to jump star... | [
[
6
],
[
14
],
[
1
],
[
9,
21
],
[
15
],
[
24
],
[
28
],
[
3,
7
],
[
4,
10
],
[
13
],
[
16,
18
],
[
11
],
[
12
],
[
20,
23
],
[
26
],
[
5
],
[
2
],
[
27
]... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"c... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"CLAR-WRT"
]
},
... | benchmark/PDF/ICLR2018_SyX0IeWAW.pdf | openreview | benchmark/MD/ICLR2018_SyX0IeWAW.md | ICLR 2018 |
SJcKhk-Ab | {
"title": "Can recurrent neural networks warp time?",
"abstract": "Successful recurrent models such as long short-term memories (LSTMs) and gated recurrent units (GRUs) use \\emph{ad hoc} gating mechanisms. Empirically these models have been found to improve the learning of medium to long term temporal dependenci... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "All the reviews like the theoretical result presented in the paper which relates the gating mechanism of LSTMS (and GRUs) to time invariance / warping. The theoretical result is great and is used to propose a heuristic for setting biases when time inva... | [
"tl;dr:",
"- The paper has a really cool theoretical contribution.",
"- The experiments do not directly test whether the theoretical insight holds in practice, but instead a derivate method is tested on various benchmarks.",
"I must say that this paper has cleared up quite a few things for me. I have always b... | [
[
4
],
[
3
],
[
6
],
[
10
],
[
14
],
[
0
],
[
8,
9
],
[
13
],
[
1,
2
],
[
5
],
[
11
],
[
15
],
[
16
],
[
18
],
[
7
],
[
12
],
[
17
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0,
1,
3,
4
]
}
],
"category": [
"ORIG-ANL",
"CLAR-WRT"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
... | benchmark/PDF/ICLR2018_SJcKhk-Ab.pdf | openreview | benchmark/MD/ICLR2018_SJcKhk-Ab.md | ICLR 2018 |
SJ-C6JbRW | {
"title": "Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent",
"abstract": "Contrary to most natural language processing research, which makes use of static datasets, humans learn language interactively, grounded in an environment. In this work we propose an interactive learning proce... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3
]
},
"scores": {
"Solid": null,
"Presentation": null,
"Novelty": null,
... | [
[
{
"role": "PC",
"data": {
"comment": "This paper provides a game-based interface to have Turkers compete to analyze data for a learning task over multiple rounds. Reviewers found the work interesting and clear written, saying \"the paper is easy to follow and the evaluation is meaningful.\... | [
"The authors propose a framework for interactive language learning, called Mechanical Turker Descent (MTD). Over multiple iterations, Turkers provide training examples for a language grounding task, and they are incentivized to provide new training examples that quickly improve generalization. The framework is stra... | [
[
5
],
[
4,
8
],
[
0
],
[
1
],
[
2
],
[
6
],
[
9
],
[
10
],
[
7
],
[
3
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"ORIG-MTH"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
]
}
],
"category": [
"QUAL... | benchmark/PDF/ICLR2018_SJ-C6JbRW.pdf | openreview | benchmark/MD/ICLR2018_SJ-C6JbRW.md | ICLR 2018 |
HktK4BeCZ | {
"title": "Learning Deep Mean Field Games for Modeling Large Population Behavior",
"abstract": "We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space. A discrete time mean field game (MFG) is motivated ... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
]
},
"scores... | [
[
{
"role": "PC",
"data": {
"comment": "The reviewers are unanimous in finding the work in this paper highly novel and significant. They have provided detailed discussions to back up this assessment. The reviewer comments surprisingly included a critique that \"the scientific content of t... | [
"The paper considers the problem of representing and learning the behavior of a large population of agents, in an attempt to construct an effective predictive model of the behavior. The main concern is with large populations where it is not possible to represent each agent individually, hence the need to use a popu... | [
[
4
],
[
6
],
[
16
],
[
17
],
[
19
],
[
22
],
[
25
],
[
26,
27
],
[
30,
31
],
[
32,
33
],
[
34,
35
],
[
40
],
[
1,
2,
20
],
[
39
],
[
5
],
[
3
],
[
15,
36
... | [
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"c... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"ORIG-ANL"
]
},
... | benchmark/PDF/ICLR2018_HktK4BeCZ.pdf | openreview | benchmark/MD/ICLR2018_HktK4BeCZ.md | ICLR 2018 |
BkLhaGZRW | {
"title": "Improving GAN Training via Binarized Representation Entropy (BRE) Regularization",
"abstract": "We propose a novel regularizer to improve the training of Generative Adversarial Networks (GANs). The motivation is that when the discriminator D spreads out its model capacity in the right way, the learning ... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": " + Original regularizer that encourages discriminator representation entropy is shown to improve GAN training.\n + good supporting empirical validation\n - While intuitively reasonable, no compelling theory is given to justify the approach\n - The ... | [
"The paper proposes a regularizer that encourages a GAN discriminator to focus its capacity in the region around the manifolds of real and generated data points, even when it would be easy to discriminate between these manifolds using only a fraction of its capacity, so that the discriminator provides a more inform... | [
[
15
],
[
11
],
[
12
],
[
13
],
[
17
],
[
21
],
[
1
],
[
2
],
[
6,
8
],
[
9
],
[
10
],
[
19
],
[
23
],
[
7
],
[
4
],
[
22
],
[
5
],
[
14
],
[
3
],
[
18
],... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"incorrect",
"incorrect",
"incorrect",
"correct",
"correct",
"correct"... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1,
2
]
},
{
"role": "Author",
... | benchmark/PDF/ICLR2018_BkLhaGZRW.pdf | openreview | benchmark/MD/ICLR2018_BkLhaGZRW.md | ICLR 2018 |
Sk2u1g-0- | {
"title": "Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments",
"abstract": "Ability to continuously learn and adapt from limited experience in nonstationary environments is an important milestone on the path towards general intelligence. In this paper, we cast the problem of con... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8
]
},
"scores": {
... | [
[
{
"role": "PC",
"data": {
"comment": "Looks like a great contribution to ICLR. Continuous adaptation in nonstationary (and competitive) environments is something that an intelligent agent acting in the real world would need to solve and this paper suggests that a meta-learning approach may... | [
"This is a dense, rich, and impressive paper on rapid meta-learning. It is already highly polished, so I have mostly minor comments.",
"Related work: I think there is a distinction between continual and life-long learning, and I think that your proposed setup is a form of continual learning (see Ring ‘94/‘97). Gi... | [
[
19
],
[
3
],
[
18
],
[
0
],
[
2
],
[
5
],
[
6
],
[
12
],
[
1
],
[
4,
9
],
[
14
],
[
15
],
[
17
],
[
20
],
[
10
],
[
16
],
[
11
],
[
8
],
[
7
],
[
13
],
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
}
],
"category": [
"CLAR-WRT"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
},
{
"role": "Author",
"dat... | benchmark/PDF/ICLR2018_Sk2u1g-0-.pdf | openreview | benchmark/MD/ICLR2018_Sk2u1g-0-.md | ICLR 2018 |
BJOFETxR- | {
"title": "Learning to Represent Programs with Graphs",
"abstract": "Learning tasks on source code (i.e., formal languages) have been considered recently, but most work has tried to transfer natural language methods and does not capitalize on the unique opportunities offered by code's known syntax. For example, lo... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6
]
},
"scores": {
"Solid": null,
"Pres... | [
[
{
"role": "PC",
"data": {
"comment": "There was some debate between the authors and an anonymous commentator on this paper. The feeling of the commentator was that existing work (mostly from the PL community) was not compared to appropriately and, in fact, performs better than this approa... | [
"Summary: The paper applies graph convolutions with deep neural networks to the problem of \"variable misuse\" (putting the wrong variable name in a program statement) in graphs created deterministically from source code. Graph structure is determined by program abstract syntax tree (AST) and next-token edges, as... | [
[
3
],
[
10
],
[
1
],
[
4,
16
],
[
6,
11
],
[
12
],
[
8
],
[
15
],
[
2
],
[
13
],
[
0
],
[
5
],
[
7
],
[
9
],
[
14
],
[
17
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1,
3,
4
]
},
{
"role": "Au... | benchmark/PDF/ICLR2018_BJOFETxR-.pdf | openreview | benchmark/MD/ICLR2018_BJOFETxR-.md | ICLR 2018 |
rkYTTf-AZ | {
"title": "Unsupervised Machine Translation Using Monolingual Corpora Only",
"abstract": "Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been numerous attempts to extend these successes to ... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
]
},
"scores... | [
[
{
"role": "AC",
"data": {
"comment": "Authors, \n\nCould I ask you to respond to the reviewers for discussion? While the reviewers here are quite positive, there are some points of clarification and concerns that would be nice to hash out. "
}
}
],
[
{
"role": "PC",... | [
"This paper describes an approach to train a neural machine translation system without parallel data. Starting from a word-to-word translation lexicon, which was also learned with unsupervised methods, this approach combines a denoising auto-encoder objective with a back-translation objective, both in two translati... | [
[
13
],
[
26
],
[
3
],
[
4
],
[
6
],
[
12,
20
],
[
23
],
[
25
],
[
31
],
[
33
],
[
21
],
[
7
],
[
16
],
[
17
],
[
1
],
[
9
],
[
14
],
[
15
],
[
28
],
[
29
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1,
2
]
}
],
"category": [
"QUAL-EXP"... | benchmark/PDF/ICLR2018_rkYTTf-AZ.pdf | openreview | benchmark/MD/ICLR2018_rkYTTf-AZ.md | ICLR 2018 |
HJGXzmspb | {
"title": "Training and Inference with Integers in Deep Neural Networks",
"abstract": "Researches on deep neural networks with discrete parameters and their deployment in embedded systems have been active and promising topics. Although previous works have successfully reduced precision in inference, transferring b... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "High quality paper, appreciated by reviewers, likely to be of substantial interest to the community. It's worth an oral to facilitate a group discussion."
}
}
]
] | [
"This paper proposes a method to train neural networks with low precision.",
"However, it is not clear if this work obtains significant improvements over previous works.\nNote that:",
"1)\tWorking with 16bit, one can train neural networks with little to no reduction in performance.",
"For example, on ImageNet... | [
[
8
],
[
14
],
[
11
],
[
2
],
[
3
],
[
5
],
[
6
],
[
10
],
[
12
],
[
13
],
[
1
],
[
4
],
[
7
],
[
0
],
[
9
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
},
{
"role": "Author",
"data": [
... | benchmark/PDF/ICLR2018_HJGXzmspb.pdf | openreview | benchmark/MD/ICLR2018_HJGXzmspb.md | ICLR 2018 |
HkL7n1-0b | {
"title": "Wasserstein Auto-Encoders",
"abstract": "We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a generative model of the data distribution. WAE minimizes a penalized form of the Wasserstein distance between the model distribution and the target distribution, which leads to a diffe... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "This paper proposes a new generative model that has the stability of variational autoencoders (VAE) while producing better samples. The authors clearly compare their work to previous efforts that combine VAEs and Generative Adversarial Networks with si... | [
"This paper satisfies the following necessary conditions for",
"acceptance. The writing is clear and I was able to understand the",
"presented method (and its motivation) despite not being too familiar",
"with the relevant literature. Explicitly writing the auto-encoder(s)",
"as pseudo-code algorithms was p... | [
[
0,
4
],
[
3
],
[
7
],
[
8
],
[
1
],
[
6
],
[
9
],
[
13
],
[
10
],
[
12
],
[
2
],
[
5
],
[
11
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0,
1,
2,
3,
4,
5,
6,
7
]
}
],
"category": [
"CLAR-WRT",
"QUAL-EXP",
"QUAL-MET"
]
},
{
"sentences": [
... | benchmark/PDF/ICLR2018_HkL7n1-0b.pdf | openreview | benchmark/MD/ICLR2018_HkL7n1-0b.md | ICLR 2018 |
Hk2aImxAb | {
"title": "Multi-Scale Dense Networks for Resource Efficient Image Classification",
"abstract": "In this paper we investigate image classification with computational resource limits at test time. Two such settings are: 1. anytime classification, where the network’s prediction for a test example is progressively up... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5
]
},
"scores": {
"Solid": null,
"Presentation": null... | [
[
{
"role": "PC",
"data": {
"comment": "As stated by reviewer 3 \"This paper introduces a new model to perform image classification with limited computational resources at test time. The model is based on a multi-scale convolutional neural network similar to the neural fabric (Saxena and Ver... | [
"This work proposes a variation of the DenseNet architecture that can cope with computational resource limits at test time. The paper is very well written, experiments are clearly presented and convincing and, most importantly, the research question is exciting (and often overlooked).",
"My only major concern is ... | [
[
2
],
[
7
],
[
21
],
[
0,
4
],
[
3
],
[
8
],
[
12
],
[
13
],
[
19
],
[
14
],
[
1
],
[
6
],
[
18
],
[
11
],
[
15
],
[
17
],
[
20
],
[
23
],
[
9
],
[
22
],... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
}
],
"category": [
"CLAR-WRT",
"QUAL-EXP",
"SIGN-BRD"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1,
2
]
... | benchmark/PDF/ICLR2018_Hk2aImxAb.pdf | openreview | benchmark/MD/ICLR2018_Hk2aImxAb.md | ICLR 2018 |
S1JHhv6TW | {
"title": "Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions",
"abstract": "The driving force behind deep networks is their ability to compactly represent rich classes of functions. The primary notion for formally reasoning about this phenomenon is expressive efficiency, which refers to a si... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4
]
},
"scores": {
"Solid": null,
"Presentation": null,
"No... | [
[
{
"role": "PC",
"data": {
"comment": "This paper proposes improvements to WaveNet by showing that increasing connectivity provides superior models to increasing network size. The reviewers found both the mathematical treatment of the topic and the experiments to be of higher quality that m... | [
"This paper theoretically validates that interconnecting networks with different dilations can lead to expressive efficiency, which indicates an interesting phenomenon that connectivity is able to enhance the expressiveness of deep networks. A key technical tool is a mixed tensor decomposition, which is shown to ha... | [
[
11
],
[
8
],
[
9
],
[
5
],
[
7
],
[
3
],
[
6
],
[
10
],
[
4
],
[
1
],
[
2,
12
],
[
13
],
[
0
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1,
2
]
}
],
"category": [
"QUAL-MET"... | benchmark/PDF/ICLR2018_S1JHhv6TW.pdf | openreview | benchmark/MD/ICLR2018_S1JHhv6TW.md | ICLR 2018 |
H196sainb | {
"title": "Word translation without parallel data",
"abstract": "State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "There is significant discussion on this paper and high variance between reviewers: one reviewer gave the paper a low score. However the committee feels that this paper should be accepted at the conference since it provides a better framework for repr... | [
"This paper presents a new method for obtaining a bilingual dictionary, without requiring any parallel data between the source and target languages. The method consists of an adversarial approach for aligning two monolingual word embedding spaces, followed by a refinement step using frequent aligned words (accordin... | [
[
1
],
[
4
],
[
10
],
[
11
],
[
12
],
[
13
],
[
14
],
[
17
],
[
2
],
[
16
],
[
18
],
[
22
],
[
23
],
[
5,
20
],
[
9
],
[
24
],
[
6
],
[
8
],
[
25
],
[
7
]... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"CLAR-WRT",
"QUAL... | benchmark/PDF/ICLR2018_H196sainb.pdf | openreview | benchmark/MD/ICLR2018_H196sainb.md | ICLR 2018 |
rkHVZWZAZ | {
"title": "The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning",
"abstract": "In this work we present a new agent architecture, called Reactor, which combines multiple algorithmic and architectural contributions to produce an agent with higher sample-efficiency than Prioritized ... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "This paper presents a nice set of results on a new RL algorithm. The main downside is the limitation to the Atari domain, but otherwise the ablation studies are nice and the results are strong."
}
}
]
] | [
"This paper proposes a novel reinforcement learning algorithm (« The Reactor ») based on the combination of several improvements to DQN: a distributional version of Retrace, a policy gradient update rule called beta-LOO aiming at variance reduction, a variant of prioritized experience replay for sequences, and a pa... | [
[
10
],
[
11
],
[
19
],
[
24
],
[
13
],
[
14
],
[
15
],
[
16
],
[
17
],
[
18
],
[
22
],
[
40
],
[
8
],
[
9
],
[
20
],
[
25
],
[
26
],
[
27
],
[
29
],
[
31
],
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"ORIG-MTH"
]
},
... | benchmark/PDF/ICLR2018_rkHVZWZAZ.pdf | openreview | benchmark/MD/ICLR2018_rkHVZWZAZ.md | ICLR 2018 |
B1QRgziT- | {
"title": "Spectral Normalization for Generative Adversarial Networks",
"abstract": "One of the challenges in the study of generative adversarial networks is the instability of its training. \nIn this paper, we propose a novel weight normalization technique called spectral normalization to stabilize the training o... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5
]
},
"scores": {
"Solid": null,
"Presentation": null... | [
[
{
"role": "PC",
"data": {
"comment": "This paper presents impressive results on scaling GANs to ILSVRC2012 dataset containing a large number of classes. To achieve this, the authors propose \"spectral normalization\" to normalize weights and stabilize training which turns out to help in ov... | [
"This paper borrows the classic idea of spectral regularization, recently applied to deep learning by Yoshida and Miyato (2017) and use it to normalize GAN objectives. The ensuing GAN, coined SN-GAN, essentially ensures the Lipschitz property of the discriminator. This Lipschitz property has already been proposed b... | [
[
5
],
[
9
],
[
10
],
[
11
],
[
12
],
[
7
],
[
14
],
[
16
],
[
1
],
[
2
],
[
3,
4
],
[
15
],
[
0
],
[
6
],
[
8
],
[
13
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"incorrect"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"QUAL-EXP"
]
},
... | benchmark/PDF/ICLR2018_B1QRgziT-.pdf | openreview | benchmark/MD/ICLR2018_B1QRgziT-.md | ICLR 2018 |
BkwHObbRZ | {
"title": "Learning One-hidden-layer Neural Networks with Landscape Design",
"abstract": "We consider the problem of learning a one-hidden-layer neural network: we assume the input x is from Gaussian distribution and the label $y = a \\sigma(Bx) + \\xi$, where a is a nonnegative vector and $B$ is a full-rank weig... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "I recommend acceptance based on the reviews. The paper makes novel contributions to learning one-hidden layer neural networks and designing new objective function with no bad local optima.\n\n There is one point that the paper is missing. It only menti... | [
"[ =========================== REVISION ===============================================================]",
"I am satisfied with the answers to my questions. The paper still needs some work on clarity, and authors defer the changes to the next version (but as I understood, they did no changes for this paper as of ... | [
[
8
],
[
0
],
[
2
],
[
12
],
[
13
],
[
16
],
[
17
],
[
5
],
[
7
],
[
6
],
[
18
],
[
4
],
[
14
],
[
10
],
[
11
],
[
1
],
[
3
],
[
9
],
[
15
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0,
1,
2
]
},
{
"role": "Author",
"data": [
25
]
}
],
"category": [
"CLAR-WRT"
]
},
{
"sentences": [
{
"role":... | benchmark/PDF/ICLR2018_BkwHObbRZ.pdf | openreview | benchmark/MD/ICLR2018_BkwHObbRZ.md | ICLR 2018 |
Bk9zbyZCZ | {
"title": " Neural Map: Structured Memory for Deep Reinforcement Learning",
"abstract": "A critical component to enabling intelligent reasoning in partially observable environments is memory. Despite this importance, Deep Reinforcement Learning (DRL) agents have so far used relatively simple memory architectures, ... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2
]
},
"scores": {
"Solid": null,
"Presentation": null,
"Novelty": null,
"Overa... | [
[
{
"role": "PC",
"data": {
"comment": "Biological memory systems are grounded in spatial representation and spatial memory, so neural methods for spatial memory are highly interesting. The proposed method is novel, well-designed and the empirical results are good on unseen environments, alt... | [
"The paper introduces a new memory mechanism specifically tailored for agent navigation in 2D environments. The memory consists of a 2D array and includes trainable read/write mechanisms. The RL agent's policy is a function of the context read, read, and next step write vectors (which are functions of the observati... | [
[
10
],
[
0
],
[
9,
13
],
[
2
],
[
17
],
[
1
],
[
4
],
[
6
],
[
12,
16
],
[
8
],
[
11,
14
],
[
15
],
[
3
],
[
5
],
[
7
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"ORIG-MTH"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
},
{
"role": "Author",
"data"... | benchmark/PDF/ICLR2018_Bk9zbyZCZ.pdf | openreview | benchmark/MD/ICLR2018_Bk9zbyZCZ.md | ICLR 2018 |
BkM3ibZRW | {
"title": "Adversarially Regularized Autoencoders",
"abstract": "While autoencoders are a key technique in representation learning for continuous structures, such as images or wave forms, developing general-purpose autoencoders for discrete structures, such as text sequence or discretized images, has proven to be ... | Invite to Workshop Track | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5
]
},
"scores": {
"Solid": null,
"Presentation": null... | [
[
{
"role": "PC",
"data": {
"comment": "In general, the reviewers and myself find this work of some interest, though potentially somewhat incremental in terms of technical novelty compared to the work for Makhzani et al. Another bothersome aspect is the question of evaluation and understandi... | [
"the paper presents a way to encode discrete distributions which is a challenging problem. they propose to use a latent variable gan with one continuous encoding and one discrete encoding.",
"two questions linger around re practices:",
"1. gan is known to struggle with discriminating distributions with differen... | [
[
11
],
[
13
],
[
15
],
[
17
],
[
2
],
[
5
],
[
9
],
[
10,
12,
14,
16
],
[
25
],
[
28
],
[
30
],
[
38
],
[
39
],
[
40
],
[
46
],
[
3
],
[
27
],
[
23
],
[
31... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"inc... | [
"correct",
"incorrect",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
]
},
{
"role": "Author",
... | benchmark/PDF/ICLR2018_BkM3ibZRW.pdf | openreview | benchmark/MD/ICLR2018_BkM3ibZRW.md | ICLR 2018 |
rJYFzMZC- | {
"title": "Simulating Action Dynamics with Neural Process Networks",
"abstract": "Understanding procedural language requires anticipating the causal effects of actions, even when they are not explicitly stated. In this work, we introduce Neural Process Networks to understand procedural text through (neural) simula... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "this submission proposes a novel extension of existing recurrent networks that focus on capturing long-term dependencies via tracking entities/their statesand tested it on a new task. there's a concern that the proposed approach is heavily engineered t... | [
"Summary\nThis paper presents Neural Process Networks, an architecture for capturing procedural knowledge stated in texts that makes use of a differentiable memory, a sentence and word attention mechanism, as well as learning action representations and their effect on entity representations. The architecture is tes... | [
[
17
],
[
18
],
[
19
],
[
23
],
[
24
],
[
10
],
[
13
],
[
15
],
[
16
],
[
20
],
[
22
],
[
25
],
[
26
],
[
30
],
[
35
],
[
37
],
[
0
],
[
1,
27
],
[
2
],
[
1... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct"... | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
},
{
"role": "Author",
"data": [
35,
36,
37,
38,
39,
40,
41,
42,
43,
44,
45,
... | benchmark/PDF/ICLR2018_rJYFzMZC-.pdf | openreview | benchmark/MD/ICLR2018_rJYFzMZC-.md | ICLR 2018 |
HJewuJWCZ | {
"title": "Learning to Teach",
"abstract": "Teaching plays a very important role in our society, by spreading human knowledge and educating our next generations. A good teacher will select appropriate teaching materials, impact suitable methodologies, and set up targeted examinations, according to the learning beh... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
]
},
"scores... | [
[
{
"role": "PC",
"data": {
"comment": "The paper addresses the problem of learning a teacher model which selects the training samples for the next mini-batch used by the student model. The proposed solution is to learn the teacher model using policy gradient. It is an interesting training s... | [
"The authors define a deep learning model composed of four components: a student model, a teacher model, a loss function, and a data set. The student model is a deep learning model (MLP, CNN, and RNN were used in the paper). The teacher model learns via reinforcement learning which items to include in each minibat... | [
[
11
],
[
17
],
[
5
],
[
6
],
[
14
],
[
1
],
[
15
],
[
19
],
[
7
],
[
9
],
[
10
],
[
12
],
[
16
],
[
18
],
[
3
],
[
8
],
[
0
],
[
2
],
[
4
],
[
13
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2,
3,
4,
5,
6,
... | benchmark/PDF/ICLR2018_HJewuJWCZ.pdf | openreview | benchmark/MD/ICLR2018_HJewuJWCZ.md | ICLR 2018 |
Sy-dQG-Rb | {
"title": "Neural Speed Reading via Skim-RNN",
"abstract": "Inspired by the principles of speed reading, we introduce Skim-RNN, a recurrent neural network (RNN) that dynamically decides to update only a small fraction of the hidden state for relatively unimportant input tokens. Skim-RNN gives a significant computa... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5
]
},
"scores": {
"Solid": null,
"Presentation": null... | [
[
{
"role": "AC",
"data": {
"comment": "Note: this is not an official meta-review\n\nthe idea in this paper looks very similar to the idea from <VARIABLE COMPUTATION IN RECURRENT NEURAL NETWORKS> which was presented at ICLR'17: https://arxiv.org/abs/1611.06188. Especially, looking at Fig. 1'... | [
"The paper proposes a way to speed up the inference time of RNN via Skim mechanism where only a small part of hidden variable is updated once the model has decided a corresponding word token seems irrelevant w.r.t. a given task.",
"While the proposed idea might be too simple, the authors show the importance of it... | [
[
23
],
[
1
],
[
9
],
[
17
],
[
22
],
[
24
],
[
25
],
[
26
],
[
16
],
[
10
],
[
12
],
[
20
],
[
0
],
[
3
],
[
7
],
[
8,
19
],
[
11
],
[
13
],
[
14
],
[
18
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0,
1
]
}
],
"category": [
"QUAL-EXP"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
]
},
{
"role": "Author",... | benchmark/PDF/ICLR2018_Sy-dQG-Rb.pdf | openreview | benchmark/MD/ICLR2018_Sy-dQG-Rb.md | ICLR 2018 |
Hyp-JJJRW | {
"title": "Style Memory: Making a Classifier Network Generative",
"abstract": "Deep networks have shown great performance in classification tasks. However, the parameters learned by the classifier networks usually discard stylistic information of the input, in favour of information strictly relevant to classificat... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7
]
},
"scores": {
"Solid": null,
... | [
[
{
"role": "PC",
"data": {
"comment": " + Paper proposes simple joint deep autoencoder + classifier training where the hidden representation is split between (observed) class and (unobserved) style nodes.\n - Empirical evaluation is very limited, focusing on only qualitative evaluation of r... | [
"This paper proposes to train a classifier neural network not just to classifier, but also to reconstruct a representation of its input, in order to factorize the class information from the appearance (or \"style\" as used in this paper). This is done by first using unsupervised pretraining and then fine-tuning usi... | [
[
10
],
[
9
],
[
8
],
[
2
],
[
3
],
[
4,
6
],
[
7
],
[
1
],
[
0
],
[
5
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"SIGN-BRD"
]
},
... | benchmark/PDF/ICLR2018_Hyp-JJJRW.pdf | openreview | benchmark/MD/ICLR2018_Hyp-JJJRW.md | ICLR 2018 |
Bk8ZcAxR- | {
"title": "Eigenoption Discovery through the Deep Successor Representation",
"abstract": "Options in reinforcement learning allow agents to hierarchically decompose a task into subtasks, having the potential to speed up learning and planning. However, autonomously learning effective sets of options is still a majo... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "This paper on automatic option discovery connects recent research on successor representations with eigenoptions. This is a solidly presented, conceptual paper with results in tabular and atari environments. "
}
}
]
] | [
"The paper extends the idea of eigenoptions, recently proposed by Machado et al. to domains with stochastic transitions and where state features are learned. An eigenoption is defined as an optimal policy for a reward function defined by an eigenvector of the matrix of successor representation (SR), which is an occ... | [
[
8
],
[
4
],
[
9
],
[
10
],
[
11
],
[
12
],
[
1
],
[
2
],
[
3
],
[
14
],
[
22
],
[
6
],
[
15
],
[
16
],
[
18
],
[
19
],
[
20
],
[
21
],
[
5
],
[
7
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"CLAR-WRT",
"ORIG... | benchmark/PDF/ICLR2018_Bk8ZcAxR-.pdf | openreview | benchmark/MD/ICLR2018_Bk8ZcAxR-.md | ICLR 2018 |
SkA-IE06W | {
"title": "When is a Convolutional Filter Easy to Learn?",
"abstract": "We analyze the convergence of (stochastic) gradient descent algorithm for learning a convolutional filter with Rectified Linear Unit (ReLU) activation function. Our analysis does not rely on any specific form of the input distribution and our ... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
]
},... | [
[
{
"role": "PC",
"data": {
"comment": "Dear authors,\n\nThe reviewers all appreciated your work and agree that this a very good first step in an interesting direction."
}
}
]
] | [
"This paper studies the problem of learning a single convolutional filter using SGD. The main result is: if the \"patches\" of the convolution are sufficiently aligned with each other, then SGD with a random initialization can recover the ground-truth parameter of a convolutional filter (single filter, ReLU, averag... | [
[
2
],
[
15
],
[
1
],
[
16
],
[
19
],
[
20
],
[
0
],
[
7
],
[
14
],
[
18
],
[
21
],
[
11,
17
],
[
12
],
[
13
],
[
3
],
[
4
],
[
5
],
[
6
],
[
8
],
[
10
],... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
},
{
"role": "Author",
"data": [
12,
14,
15,
18
]
}
],
"category": [
"CLAR-WRT",
"ORIG-ANL",
"QUAL-MET"
]... | benchmark/PDF/ICLR2018_SkA-IE06W.pdf | openreview | benchmark/MD/ICLR2018_SkA-IE06W.md | ICLR 2018 |
Hkn7CBaTW | {
"title": "Learning how to explain neural networks: PatternNet and PatternAttribution",
"abstract": "DeConvNet, Guided BackProp, LRP, were invented to better understand deep neural networks. We show that these methods do not produce the theoretically correct explanation for a linear model. Yet they are used on mul... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8
]
},
"scores": {
... | [
[
{
"role": "PC",
"data": {
"comment": "The paper shows that many of the current state-of-the-art interpretability methods are inaccurate even for linear models. Then based on their analysis of linear models they propose a technique that is thus accurate for them and also empirically provide... | [
"The authors analyze show theoretical shortcomings in previous methods of explaining neural networks and propose an elegant way to remove these shortcomings in their methods PatternNet and PatternAttribution.",
"The quest of visualizing neural network decision is now a very active field with many contributions. T... | [
[
5
],
[
20
],
[
26
],
[
27
],
[
31
],
[
6
],
[
30
],
[
14
],
[
21,
22,
23
],
[
32
],
[
17
],
[
15
],
[
1
],
[
4,
9
],
[
8
],
[
29
],
[
2
],
[
3
],
[
13
]... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
]
},
{
"role": "Author",
... | benchmark/PDF/ICLR2018_Hkn7CBaTW.pdf | openreview | benchmark/MD/ICLR2018_Hkn7CBaTW.md | ICLR 2018 |
BJhxcGZCW | {
"title": "Generative Discovery of Relational Medical Entity Pairs",
"abstract": "Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce the information access cost for both individuals and societies. To promote these benefits, it is desired to effectively ... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "The authors seem to miss important related literature for their comparison.\nThey also tuned hyperparameters and tested on the same validation set.\nThey should split between train/validation/test.\n\nReviews are just too low across the board to accept... | [
"SUMMARY.\nThe paper presents a variational autoencoder for generating entity pairs given a relation in a medical setting.",
"The model strictly follows the standard VAE architecture with an encoder that takes as input an entity pair and a relation between the entities.",
"The encoder maps the input to a probab... | [
[
1
],
[
9
],
[
11
],
[
10
],
[
12
],
[
13
],
[
15
],
[
2
],
[
4
],
[
6
],
[
8
],
[
14
],
[
3
],
[
7
],
[
5
],
[
0
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1,
2,
3,
4
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
5
]
}
... | benchmark/PDF/ICLR2018_BJhxcGZCW.pdf | openreview | benchmark/MD/ICLR2018_BJhxcGZCW.md | ICLR 2018 |
BJJLHbb0- | {
"title": "Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection",
"abstract": "Unsupervised anomaly detection on multi- or high-dimensional data is of great importance in both fundamental machine learning research and industrial applications, for which density estimation lies at the core. Al... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3
]
},
"scores": {
"Solid": null,
"Presentation": null,
"Novelty": null,
... | [
[
{
"role": "PC",
"data": {
"comment": " + Empirically convincing and clearly explained application: a novel deep learning architecture and approach is shown to significantly outperform state-of-the-art in unsupervised anomaly detection.\n - No clear theoretical foundation and justification ... | [
"1. This is a good paper, makes an interesting algorithmic contribution in the sense of joint clustering-dimension reduction for unsupervised anomaly detection",
"2. It demonstrates clear performance improvement via comprehensive comparison with state-of-the-art methods",
"3. Is the number of Gaussian Mixtures ... | [
[
12
],
[
14
],
[
15
],
[
18
],
[
19
],
[
24
],
[
28
],
[
0
],
[
20
],
[
1
],
[
8
],
[
9
],
[
3
],
[
4
],
[
5
],
[
6
],
[
10,
21
],
[
11
],
[
13
],
[
16
]... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"c... | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
}
],
"category": [
"ORIG-MTH"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"QUAL-CMP",
... | benchmark/PDF/ICLR2018_BJJLHbb0-.pdf | openreview | benchmark/MD/ICLR2018_BJJLHbb0-.md | ICLR 2018 |
ryQu7f-RZ | {
"title": "On the Convergence of Adam and Beyond",
"abstract": " Several recently proposed stochastic optimization methods that have been successfully used in training deep networks such as RMSProp, Adam, Adadelta, Nadam are based on using gradient updates scaled by square roots of exponential moving averages of s... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "This paper analyzes a problem with the convergence of Adam, and presents a solution. It identifies an error in the convergence proof of Adam (which also applies to related methods such as RMSProp) and gives a simple example where it fails to converge. ... | [
"The paper presents three contributions: 1) it shows that the proof of convergence Adam is wrong; 2) it presents adversarial and stochastic examples on which Adam converges to the worst possible solution (i.e. there is no hope to just fix Adam's proof); 3) it proposes a variant of Adam called AMSGrad that fixes the... | [
[
9
],
[
8
],
[
11,
30
],
[
12
],
[
14
],
[
22
],
[
23
],
[
25
],
[
26
],
[
27
],
[
28
],
[
29
],
[
34
],
[
37
],
[
38
],
[
7
],
[
10
],
[
13
],
[
17
],
[
3... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"c... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"ORIG-MTH"
]
},
... | benchmark/PDF/ICLR2018_ryQu7f-RZ.pdf | openreview | benchmark/MD/ICLR2018_ryQu7f-RZ.md | ICLR 2018 |
H1Dy---0Z | {
"title": "Distributed Prioritized Experience Replay",
"abstract": "We propose a distributed architecture for deep reinforcement learning at scale, that enables agents to learn effectively from orders of magnitude more data than previously possible. The algorithm decouples acting from learning: the actors interact... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8
]
},
"scores": {
... | [
[
{
"role": "PC",
"data": {
"comment": "meta score: 8\n\nThe paper present a distributed architecture using prioritized experience replay for deep reinforcement learning. It is well-written and the experimentation is extremely strong. The main issue is the originality - technically, it ext... | [
"This paper examines a distributed Deep RL system in which experiences, rather than gradients, are shared between the parallel workers and the centralized learner. The experiences are accumulated into a central replay memory and prioritized replay is used to update the policy based on the diverse experience accumul... | [
[
12
],
[
15
],
[
19
],
[
20
],
[
5
],
[
23
],
[
3
],
[
1
],
[
2
],
[
4
],
[
6
],
[
8,
22
],
[
10
],
[
16
],
[
13
],
[
14
],
[
7
],
[
11
],
[
17
],
[
18
]... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"c... | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
}
],
"category": [
"SIGN-SOT",
"QUAL-EXP"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
... | benchmark/PDF/ICLR2018_H1Dy---0Z.pdf | openreview | benchmark/MD/ICLR2018_H1Dy---0Z.md | ICLR 2018 |
HyydRMZC- | {
"title": "Spatially Transformed Adversarial Examples",
"abstract": "Recent studies show that widely used Deep neural networks (DNNs) are vulnerable to the carefully crafted adversarial examples.\nMany advanced algorithms have been proposed to generate adversarial examples by leveraging the L_p distance for penali... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2
]
},
"scores": {
"Solid": null,
"Presentation": null,
"Novelty": null,
"Overa... | [
[
{
"role": "PC",
"data": {
"comment": "All reviewers gave \"accept\" ratings.\nit seems that everyone thinks this is interesting work.\n\nThe paper generated a large number of anonymous comments and these were addressed by the authors. "
}
}
]
] | [
"This paper explores a new way of generating adversarial examples by slightly morphing the image to get misclassified by the model. Most other adversarial example generation methods tend to rely on generating high frequency noise patterns based by optimizing the perturbation on an individual pixel level. The new ap... | [
[
6
],
[
0,
1
],
[
10
],
[
13
],
[
14
],
[
16
],
[
2
],
[
4
],
[
5
],
[
7
],
[
8
],
[
12
],
[
3
],
[
9
],
[
11
],
[
15
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
}
],
"category": [
"ORIG-MTH"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"ORIG-MTH",
... | benchmark/PDF/ICLR2018_HyydRMZC-.pdf | openreview | benchmark/MD/ICLR2018_HyydRMZC-.md | ICLR 2018 |
r1SnX5xCb | {
"title": "Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks",
"abstract": "For every prediction we might wish to make, we must decide what to observe (what source of information) and when to observe it. Because making observations is costly, this decision must trade off the value of i... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6
]
},
"scores": {
"Solid": null,
"Pres... | [
[
{
"role": "PC",
"data": {
"comment": "This paper is well written, addresses and interesting problem, and provides an interesting solution."
}
}
]
] | [
"This paper presents a new approach to determining what to measure and when to measure it, using a novel deep learning architecture. The problem addressed is important and timely and advances here may have an impact on many application areas outside medicine. The approach is evaluated on real-world medical datasets... | [
[
40
],
[
34
],
[
4
],
[
12,
14
],
[
18,
42,
48
],
[
19
],
[
32
],
[
33
],
[
36
],
[
37
],
[
38,
50
],
[
0,
11
],
[
22,
41
],
[
2
],
[
3
],
[
26,
43
],
[
35... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
}
],
"category": [
"ORIG-MTH",
"SIGN-BRD",
"QUAL-EXP"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
... | benchmark/PDF/ICLR2018_r1SnX5xCb.pdf | openreview | benchmark/MD/ICLR2018_r1SnX5xCb.md | ICLR 2018 |
HyiRazbRb | {
"title": "Demystifying overcomplete nonlinear auto-encoders: fast SGD convergence towards sparse representation from random initialization",
"abstract": "Auto-encoders are commonly used for unsupervised representation learning and for pre-training deeper neural networks.\nWhen its activation function is linear an... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "The reviewers have unanimously expressed strong concerns about the technical correctness of the theoretical results in the paper. The paper should be carefully revised and checked for technical errors. In its current form, the paper is not suitable for... | [
"The authors study the convergence of a procedure for learning",
"an autoencoder with a ReLu non-linearity. The procedure is akin",
"to stochastic gradient descent, with some parameters updated at",
"each iteration in a manner that performs optimization with respect\nto the population risk.",
"The autoenco... | [
[
7
],
[
14
],
[
24
],
[
25
],
[
28
],
[
2
],
[
3,
30
],
[
29
],
[
26
],
[
27
],
[
1,
12
],
[
4,
9
],
[
5
],
[
18
],
[
19
],
[
20
],
[
21
],
[
22
],
[
15
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"c... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1,
2,
3,
4
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
5,
6,
... | benchmark/PDF/ICLR2018_HyiRazbRb.pdf | openreview | benchmark/MD/ICLR2018_HyiRazbRb.md | ICLR 2018 |
HJ1HFlZAb | {
"title": "Evaluation of generative networks through their data augmentation capacity",
"abstract": "Generative networks are known to be difficult to assess. Recent works on generative models, especially on generative adversarial networks, produce nice samples of varied categories of images. But the validation of ... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5
]
},
"scores": {
"Solid": null,
"Presentation": null... | [
[
{
"role": "PC",
"data": {
"comment": "Given that the paper proposes a new evaluation scheme for generative models, I agree with the reviewers that it is essential that the paper compare with existing metrics (even if they are imperfect). The choice of datasets was very limited as well, giv... | [
"The main idea is to use the accuracy of a classifier trained on synthetic training examples produced by a generative model to define an evaluation metric for the generative model.",
"Specifically, compare the accuracy of a classifier trained on a noise-perturbed version of the real dataset to that of a classifie... | [
[
15
],
[
5,
7
],
[
8
],
[
11
],
[
1
],
[
2
],
[
3
],
[
4,
10
],
[
6
],
[
9
],
[
13
],
[
14
],
[
17,
21
],
[
19
],
[
22
],
[
23
],
[
24
],
[
12
],
[
18
],... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
]
},
{
"role": "Author",
... | benchmark/PDF/ICLR2018_HJ1HFlZAb.pdf | openreview | benchmark/MD/ICLR2018_HJ1HFlZAb.md | ICLR 2018 |
H1q-TM-AW | {
"title": "A DIRT-T Approach to Unsupervised Domain Adaptation",
"abstract": "Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable. A recent approach for finding a common representation of the two ... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "Well motivated and well written, with extensive results. The paper also received positive comments from all reviewers. The AC recommends that the paper be accepted."
}
}
]
] | [
"As there are many kinds of domain adaptation problems, the need to mix several learning strategies to improve the existing approaches is obvious.",
"However, this task is not necessarily easy to succeed. The authors proposed a sound approach to learn a proper representation (in an adversarial way) and comply the... | [
[
12
],
[
25
],
[
26
],
[
13
],
[
17
],
[
7,
11
],
[
24
],
[
2
],
[
8,
15
],
[
27
],
[
5
],
[
14
],
[
21
],
[
22
],
[
1
],
[
4
],
[
9
],
[
10
],
[
18
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
}
],
"category": [
"SIGN-BRD"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"QUAL-MET"
]... | benchmark/PDF/ICLR2018_H1q-TM-AW.pdf | openreview | benchmark/MD/ICLR2018_H1q-TM-AW.md | ICLR 2018 |
Hyg0vbWC- | {
"title": "Generating Wikipedia by Summarizing Long Sequences",
"abstract": "We show that generating English Wikipedia articles can be approached as a multi-\ndocument summarization of source documents. We use extractive summarization\nto coarsely identify salient information and a neural abstractive model to gene... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
]
},... | [
[
{
"role": "PC",
"data": {
"comment": "This paper presents a new multi-document summarization task of trying to write a wikipedia article based on its sources. Reviewers found the paper and the task clear to understand and well-explained. The modeling aspects are clear as well, although lac... | [
"This paper considers the task of generating Wikipedia articles as a combination of extractive and abstractive multi-document summarization task where input is the content of reference articles listed in a Wikipedia page along with the content collected from Web search and output is the generated content for a targ... | [
[
7
],
[
1
],
[
15
],
[
13
],
[
2
],
[
3
],
[
16
],
[
4
],
[
6
],
[
10
],
[
11
],
[
12
],
[
17
],
[
18
],
[
19
],
[
9
],
[
5
],
[
0
],
[
8
],
[
14
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"CLAR-WRT"
]
},
... | benchmark/PDF/ICLR2018_Hyg0vbWC-.pdf | openreview | benchmark/MD/ICLR2018_Hyg0vbWC-.md | ICLR 2018 |
BJ8vJebC- | {
"title": "Synthetic and Natural Noise Both Break Neural Machine Translation",
"abstract": "Character-based neural machine translation (NMT) models alleviate out-of-vocabulary issues, learn morphology, and move us closer to completely end-to-end translation systems. Unfortunately, they are also very brittle and e... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "The pros and cons of this paper cited by the reviewers can be summarized below:\n\nPros:\n* The paper is a first attempt to investigate an under-studied area in neural MT (and potentially other applications of sequence-to-sequence models as well)\n* Th... | [
"This paper empirically investigates the performance of character-level NMT systems in the face of character-level noise, both synthesized and natural. The results are not surprising:",
"* NMT is terrible with noise.",
"* But it improves on each noise type when it is trained on that noise type.",
"What I like... | [
[
21
],
[
15
],
[
17
],
[
19
],
[
10
],
[
18
],
[
2
],
[
9
],
[
11
],
[
12
],
[
13
],
[
22
],
[
8
],
[
20
],
[
4
],
[
5
],
[
7
],
[
14
],
[
3
],
[
23
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer",
"data": [
1,
2,
3
]
}
],
"category": [
... | benchmark/PDF/ICLR2018_BJ8vJebC-.pdf | openreview | benchmark/MD/ICLR2018_BJ8vJebC-.md | ICLR 2018 |
B1ae1lZRb | {
"title": "Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy",
"abstract": "Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection. The performant systems, however, typically involve bi... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7
]
},
"scores": {
"Solid": null,
... | [
[
{
"role": "PC",
"data": {
"comment": "Meta score: 7\n\nThe paper combined low precision computation with different approaches to teacher-student knowledge distillation. The experimentation is good, with good experimental analysis. Very clearly written. The main contribution is in the di... | [
"The authors investigate knowledge distillation as a way to learn low precision networks. They propose three training schemes to train a low precision student network from a teacher network. They conduct experiments on ImageNet-1k with variants of ResNets and multiple low precision regimes and compare performance w... | [
[
1,
10,
16
],
[
12
],
[
18
],
[
9
],
[
14
],
[
2
],
[
4,
7
],
[
5
],
[
6
],
[
13
],
[
17
],
[
3,
11
],
[
0
],
[
8
],
[
15
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"CLAR-WRT"
]
},
... | benchmark/PDF/ICLR2018_B1ae1lZRb.pdf | openreview | benchmark/MD/ICLR2018_B1ae1lZRb.md | ICLR 2018 |
HkbJTYyAb | {
"title": "Convolutional Normalizing Flows",
"abstract": "Bayesian posterior inference is prevalent in various machine learning problems. Variational inference provides one way to approximate the posterior distribution, however its expressive power is limited and so is the accuracy of resulting approximation. Rece... | Reject | [
[
{
"role": "Reviewer",
"data": {
"value": {
"comment": [
0
]
}
}
},
{
"role": "Author",
"data": {
"value": {
"comment": [
1,
2,
3,
4,
5
]... | [
[
{
"role": "PC",
"data": {
"comment": "Thank you for submitting you paper to ICLR. ICLR. Although there revision has improved the paper, the consensus from the reviewers is that this is not quite ready for publication."
}
}
]
] | [
"The conclusion of the paper says \"density estimates on MNIST show significant improvements over state-of-the-art methods\". This is misleading, as the results table ignores all recent results in this area. E.g. PixelVAE, Lossy VAE, PixelCNN, and IAF-VAE (some of which are cited) all obtain much better results. Is... | [
[
4,
12
],
[
7
],
[
8
],
[
0,
2,
10
],
[
9
],
[
5
],
[
11
],
[
13
],
[
3
],
[
1
],
[
6
]
] | [
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"incorrect",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1 Further Reply",
"data": [
0
]
},
{
"role": "Author",
"data": [
1,
2,
3,
4,
5
]
}
],
"category": [
"QUAL-CMP"
]
},
{
... | benchmark/PDF/ICLR2018_HkbJTYyAb.pdf | openreview | benchmark/MD/ICLR2018_HkbJTYyAb.md | ICLR 2018 |
HJzgZ3JCW | {
"title": "Efficient Sparse-Winograd Convolutional Neural Networks",
"abstract": "Convolutional Neural Networks (CNNs) are computationally intensive, which limits their application on mobile devices. Their energy is dominated by the number of multiplies needed to perform the convolutions. Winograd’s minimal filter... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5
]
},
"scores": {
"Solid": null,
"Presentation": null... | [
[
{
"role": "PC",
"data": {
"comment": "The paper presents a modification of the Winograd convolution algorithm that reduces the number of multiplications in a forward pass of a CNN with minimal loss of accuracy. The reviewers brought up the strong results, the readability of the paper, and ... | [
"This paper proposes to combine Winograd transformation with sparsity to reduce the computation for deep convolutional neural network.",
"Specifically, ReLU nonlinearity was moved after Winograd transformation to increase the dynamic sparsity in the Winograd domain, while an additional pruning on low magnitude we... | [
[
13
],
[
22
],
[
2,
6
],
[
17
],
[
19
],
[
20
],
[
3
],
[
16
],
[
21
],
[
1
],
[
4
],
[
9
],
[
10
],
[
12
],
[
11
],
[
15
],
[
7
],
[
8
],
[
0
],
[
5
],
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
]
}
],
"category": [
"QUAL-EXP"... | benchmark/PDF/ICLR2018_HJzgZ3JCW.pdf | openreview | benchmark/MD/ICLR2018_HJzgZ3JCW.md | ICLR 2018 |
ryH20GbRW | {
"title": "Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions",
"abstract": "Common-sense physical reasoning is an essential ingredient for any intelligent agent operating in the real-world. For example, it can be used to simulate the environment, or to infer the s... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "All three reviewers recommend acceptance. The authors did a good job at the rebuttal which swayed the first reviewer to increase the final rating. This is a clear accept."
}
}
]
] | [
"Summary\nThis work applies a representaion learning technique that segments entities to learn simple 2d intuitive physics without per-entity supervision. It adds a relational mechanism to Neural Expectation Maximization and shows that this mechanism provides a better simulation of bouncing balls in a synthetic env... | [
[
12
],
[
6
],
[
8
],
[
11
],
[
13
],
[
14,
15
],
[
16
],
[
18
],
[
25,
30
],
[
26
],
[
28,
31
],
[
3
],
[
4
],
[
5
],
[
7
],
[
9,
19
],
[
10
],
[
21
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role":... | benchmark/PDF/ICLR2018_ryH20GbRW.pdf | openreview | benchmark/MD/ICLR2018_ryH20GbRW.md | ICLR 2018 |
HkfXMz-Ab | {
"title": "Neural Sketch Learning for Conditional Program Generation",
"abstract": "We study the problem of generating source code in a strongly typed,\nJava-like programming language, given a label (for example a set of\nAPI calls or types) carrying a small amount of information about the\ncode that is desired. T... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "This paper presents a novel and interesting sketch-based approach to conditional program generation. I will say upfront that it is worth of acceptance, based on its contribution and the positivity of the reviews. I am annoyed to see that the review pro... | [
"The authors introduce an algorithm in the subfield of conditional program generation that is able to create programs in a rich java like programming language. In this setting, they propose an algorithm based on sketches- abstractions of programs that capture the structure but discard program specific information t... | [
[
11
],
[
6
],
[
2
],
[
3,
23,
24
],
[
4
],
[
5
],
[
16
],
[
19
],
[
0,
1
],
[
22
],
[
7,
28
],
[
21
],
[
12
],
[
13
],
[
14
],
[
18,
26
],
[
20
],
[
8
],... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"ORIG-MTH"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"ORIG-MTH"
]
... | benchmark/PDF/ICLR2018_HkfXMz-Ab.pdf | openreview | benchmark/MD/ICLR2018_HkfXMz-Ab.md | ICLR 2018 |
BkisuzWRW | {
"title": "Zero-Shot Visual Imitation",
"abstract": "The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both 'what' and 'how' to imitate. We pursue an alternative paradigm wherein an agent first explores the world without any expert supervision and then dis... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6
]
},
"scores": {
"Solid": null,
"Pres... | [
[
{
"role": "PC",
"data": {
"comment": "The authors have proposed a method for imitating a given control trajectory even if it is sparsely sampled. The method relies on a parametrized skill function and uses a triplet loss for learning a stopping metric and for a dynamics consistency loss. T... | [
"The authors propose an approach for zero-shot visual learning. The robot learns inverse and forward models through autonomous exploration. The robot then uses the learned parametric skill functions to reach goal states (images) provided by the demonstrator. The “zero-shot” refers to the fact that all learning is p... | [
[
7
],
[
2
],
[
17
],
[
18
],
[
19
],
[
3
],
[
4
],
[
5
],
[
9
],
[
10
],
[
12
],
[
15
],
[
1
],
[
11
],
[
13
],
[
14
],
[
21
],
[
8
],
[
0
],
[
6
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"incorrect",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"QUAL-MET",
"QUAL... | benchmark/PDF/ICLR2018_BkisuzWRW.pdf | openreview | benchmark/MD/ICLR2018_BkisuzWRW.md | ICLR 2018 |
BkeqO7x0- | {
"title": "Unsupervised Cipher Cracking Using Discrete GANs",
"abstract": "This work details CipherGAN, an architecture inspired by CycleGAN used for inferring the underlying cipher mapping given banks of unpaired ciphertext and plaintext. We demonstrate that CipherGAN is capable of cracking language data encipher... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2
]
},
"scores": {
"Solid": null,
"Presentation": null,
"Novelty": null,
"Overa... | [
[
{
"role": "PC",
"data": {
"comment": "this work adapts cycle GAN to the problem of decipherment with some success. it's still an early result, but all the reviewers have found it to be interesting and worthwhile for publication."
}
}
]
] | [
"The paper shows an application of GANs to deciphering text. The goal is to arrive at a ```\"hands free\" approach to this problem; i.e., an approach that does not require any knowledge of the language being deciphered such as letter frequency and such. The authors start from a CycleGAN architecture, which may be u... | [
[
5
],
[
3
],
[
29
],
[
31
],
[
37
],
[
1
],
[
2
],
[
4
],
[
11
],
[
13
],
[
14
],
[
16
],
[
17,
35
],
[
20
],
[
22
],
[
30
],
[
0
],
[
9
],
[
12,
33
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"ORIG-MTH"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1,
2
]
},
{
"role": "Author",
... | benchmark/PDF/ICLR2018_BkeqO7x0-.pdf | openreview | benchmark/MD/ICLR2018_BkeqO7x0-.md | ICLR 2018 |
Sk0pHeZAW | {
"title": "Sparse Regularized Deep Neural Networks For Efficient Embedded Learning",
"abstract": "Deep learning is becoming more widespread in its application due to its power in solving complex classification problems. However, deep learning models often require large memory and energy consumption, which may prev... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8
]
},
"scores": {
... | [
[
{
"role": "PC",
"data": {
"comment": "Dear authors,\n\nI agree with the reviewers that the paper tries to do several things at once and the results are not that convincing. Overall, this work is mostly incremental, which is fine if there is no issue in the execution. Thus, I regret to info... | [
"Summary:\nPaper proposes the compression method Delicate-SVRG-cumulative-L1 (combining minibatch SVRG with cumulative L1 regularization) which can significantly reduce the number of weights without affecting the test accuracy. Paper provides numerical experiments for MNIST and CIRAR10 on LeNet-300-100 and LeNet-5.... | [
[
5,
10
],
[
13,
25
],
[
14
],
[
15
],
[
16
],
[
19
],
[
20
],
[
21
],
[
22
],
[
23
],
[
8
],
[
1,
24
],
[
7,
11,
12
],
[
9
],
[
18
],
[
3
],
[
2
],
[
4
]... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1,
2,
3
]
},
{
"role": "Au... | benchmark/PDF/ICLR2018_Sk0pHeZAW.pdf | openreview | benchmark/MD/ICLR2018_Sk0pHeZAW.md | ICLR 2018 |
BJE-4xW0W | {
"title": "CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training",
"abstract": "We introduce causal implicit generative models (CiGMs): models that allow sampling from not only the true observational but also the true interventional distributions. We show that adversarial training can be ... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "This paper proposes an interesting machinery around Generative Adversarial Networks to enable sampling not only from conditional observational distributions but also from interventional distributions. This is an important contribution as this means th... | [
"In their paper \"CausalGAN: Learning Causal implicit Generative Models with adv. training\" the authors address the following issue: Given a causal structure between \"labels\" of an image (e.g. gender, mustache, smiling, etc.), one tries to learn a causal model between these variables and the image itself from ob... | [
[
3
],
[
4
],
[
11
],
[
22
],
[
23
],
[
25
],
[
30
],
[
8
],
[
12
],
[
14
],
[
15
],
[
17
],
[
20
],
[
21
],
[
1
],
[
2
],
[
6
],
[
7
],
[
9
],
[
10
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
},
{
"role": "Author",
"data": [
... | benchmark/PDF/ICLR2018_BJE-4xW0W.pdf | openreview | benchmark/MD/ICLR2018_BJE-4xW0W.md | ICLR 2018 |
H11lAfbCW | {
"title": "On Characterizing the Capacity of Neural Networks Using Algebraic Topology",
"abstract": "The learnability of different neural architectures can be characterized directly by computable measures of data complexity. In this paper, we reframe the problem of architecture selection as understanding how data ... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
]
},... | [
[
{
"role": "PC",
"data": {
"comment": "This paper attempts to connect the expressivity of neural networks with a measure of topological complexity. The authors present some empirical results on simplified datasets.\nAll reviewers agreed that this is an intriguing line of research, but that ... | [
"Paper Summary:\nThis paper looks at empirically measuring neural network architecture expressivity by examining performance on a variety of complex datasets, measuring dataset complexity with algebraic topology. The paper first introduces the notion of topological equivalence for datasets -- a desirable measure to... | [
[
29,
31
],
[
30
],
[
32
],
[
33
],
[
3
],
[
4,
18,
34
],
[
25
],
[
7
],
[
12
],
[
20
],
[
15
],
[
16
],
[
17
],
[
1
],
[
2,
13
],
[
21
],
[
26
],
[
10
],
[... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1,
2,
3
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
4,
5
]
},
... | benchmark/PDF/ICLR2018_H11lAfbCW.pdf | openreview | benchmark/MD/ICLR2018_H11lAfbCW.md | ICLR 2018 |
rkfbLilAb | {
"title": "Improving Search Through A3C Reinforcement Learning Based Conversational Agent",
"abstract": "We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective sea... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5
]
},
"scores": {
"Solid": null,
"Presentation": null... | [
[
{
"role": "PC",
"data": {
"comment": "meta score: 4\n\nThis paper is primarily an application paper applying known RL techniques to dialogue. Very little reference to the extensive literature in this area.\n\nPros:\n - interesting application (digital search)\n - revised version contain... | [
"This paper proposes to use RL (Q-learning and A3C) to optimize the interaction strategy of a search assistant. The method is trained against a simulated user to bootstrap the learning process. The algorithm is tested on some search base of assets such as images or videos.",
"My first concern is about the propose... | [
[
14
],
[
3
],
[
12
],
[
13
],
[
2
],
[
7
],
[
1
],
[
5
],
[
9
],
[
10
],
[
11
],
[
6
],
[
0
],
[
4
],
[
8
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1,
2
]
},
{
"role": "Author",
... | benchmark/PDF/ICLR2018_rkfbLilAb.pdf | openreview | benchmark/MD/ICLR2018_rkfbLilAb.md | ICLR 2018 |
SkERSm-0- | {
"title": "Preliminary theoretical troubleshooting in Variational Autoencoder",
"abstract": "What would be learned by variational autoencoder(VAE) and what influence the disentanglement of VAE? This paper tries to preliminarily address VAE's intrinsic dimension, real factor, disentanglement and indicator issues th... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2
]
},
"scores": {
"Solid": null,
"Presentation": null,
"Novelty": null,
"Overa... | [
[
{
"role": "PC",
"data": {
"comment": "The reviewers agreed that the paper was too long (more than twice the recommended page limit not counting the appendix) and difficult to follow. They also pointed out that its central idea of learning the noise distribution in a VAE was not novel. Whil... | [
"This paper studies the importance of the noise modelling in Gaussian VAE. The original Gaussian VAE proposes to use the inference network for the noise that takes latent variables as inputs and outputs the variances, but most of the existing works on Gaussian VAE just use fixed noise probably because the inference... | [
[
2,
13
],
[
4,
5,
6
],
[
7
],
[
9
],
[
0,
8
],
[
1
],
[
10
],
[
12
],
[
11
],
[
3
]
] | [
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"ORIG-MTH"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
},
{
"role": "Author",
"data"... | benchmark/PDF/ICLR2018_SkERSm-0-.pdf | openreview | benchmark/MD/ICLR2018_SkERSm-0-.md | ICLR 2018 |
r1Dx7fbCW | {
"title": "Generalizing Across Domains via Cross-Gradient Training",
"abstract": "We present CROSSGRAD , a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain.... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "Well motivated and well received by all of the expert reviewers. The AC recommends that the paper be accepted."
}
}
]
] | [
"This paper proposed a domain generalization approach by domain-dependent data augmentation. The augmentation is guided by a network that is trained to classify a data point to different domains. Experiments on four datasets verify the effectiveness of the proposed approach.",
"Strengths:\n+ The proposed classifi... | [
[
26
],
[
20,
25
],
[
24
],
[
5
],
[
9,
10
],
[
12
],
[
19
],
[
23
],
[
1
],
[
13,
21
],
[
15
],
[
3
],
[
4,
7
],
[
2
],
[
6
],
[
11
],
[
17
],
[
16
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"ORIG-MTH"
]
},
... | benchmark/PDF/ICLR2018_r1Dx7fbCW.pdf | openreview | benchmark/MD/ICLR2018_r1Dx7fbCW.md | ICLR 2018 |
HJsjkMb0Z | {
"title": "i-RevNet: Deep Invertible Networks",
"abstract": "It is widely believed that the success of deep convolutional networks is based on progressively discarding uninformative variability about the input with respect to the problem at hand. This is supported empirically by the difficulty of recovering images... | Accept (Poster) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
]
},... | [
[
{
"role": "PC",
"data": {
"comment": "This paper constructs a variant of deep CNNs which is provably invertible, by replacing spatial pooling with multiple shifted spatial downsampling, and capitalizing on residual layers to define a simple, invertible representation. The authors show that... | [
"In this paper, the authors propose deep architecture that preserves mutual information between the input and the hidden representation and show that the loss of information can only occur at the final layer. They illustrate empirically that the loss of information can be avoided on large-scale classification such ... | [
[
11
],
[
4
],
[
10
],
[
12
],
[
14
],
[
20
],
[
3,
6
],
[
15
],
[
21
],
[
1
],
[
2
],
[
9
],
[
16
],
[
17
],
[
19
],
[
22
],
[
7
],
[
18
],
[
0
],
[
5
],... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1,
2
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
3
]
}
],
"category": [
... | benchmark/PDF/ICLR2018_HJsjkMb0Z.pdf | openreview | benchmark/MD/ICLR2018_HJsjkMb0Z.md | ICLR 2018 |
rkYgAJWCZ | {
"title": "One-shot and few-shot learning of word embeddings",
"abstract": "Standard deep learning systems require thousands or millions of examples to learn a concept, and cannot integrate new concepts easily. By contrast, humans have an incredible ability to do one-shot or few-shot learning. For instance, from j... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3
]
},
"scores": {
"Solid": null,
"Presentation": null,
"Novelty": null,
... | [
[
{
"role": "PC",
"data": {
"comment": "The paper is looking at an interesting problem, but it seems too early. The approach requires training a new language model from scratch for each new word, rendering it completely impractical for real use. The main evaluation therefore only considers ... | [
"I am highly sympathetic to the goals of this paper, and the authors do a good job of contrasting human learning with current deep learning systems, arguing that the lack of a mechanism for few-shot learning in such systems is a barrier to applying them in realistic scenarios.",
"However, the main evaluation only... | [
[
2
],
[
3
],
[
12
],
[
7
],
[
9
],
[
1,
6,
11
],
[
5
],
[
10
],
[
4
],
[
0
],
[
8
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
},
{
"role": "Author",
"data": [
... | benchmark/PDF/ICLR2018_rkYgAJWCZ.pdf | openreview | benchmark/MD/ICLR2018_rkYgAJWCZ.md | ICLR 2018 |
H1u8fMW0b | {
"title": "Toward predictive machine learning for active vision",
"abstract": "We develop a comprehensive description of the active inference framework, as proposed by Friston (2010), under a machine-learning compliant perspective. Stemming from a biological inspiration and the auto-encoding principles, a sketch o... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4
]
},
"scores": {
"Solid": null,
"Presentation": null,
"No... | [
[
{
"role": "PC",
"data": {
"comment": "All 3 reviewers consider the paper insufficiently good, including a post-rebuttal updated score.\nAll reviewers + anonymous comment find that the paper isn't well-enough situated with the appropriate literature.\nTwo reviewers cite poor presentation - ... | [
"It is rather difficult to evaluate the manuscript. A large part of the manuscript reviews various papers from the active vision domain and subsequently proposes that this can directly be modeled using Friston’s free energy principle, essentially, by “analogy”, as the authors state. This extends up to page 4. I wou... | [
[
10
],
[
5
],
[
11
],
[
14
],
[
13
],
[
15
],
[
16
],
[
17
],
[
2
],
[
6
],
[
7
],
[
8
],
[
0,
9
],
[
1
],
[
4
],
[
3
],
[
12
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
},
{
"role": "Author",
"data": [
5,
6,
11,
12,
13,
14,
17,
18
]
}
],
"category": [
... | benchmark/PDF/ICLR2018_H1u8fMW0b.pdf | openreview | benchmark/MD/ICLR2018_H1u8fMW0b.md | ICLR 2018 |
r1AMITFaW | {
"title": "Dependent Bidirectional RNN with Extended-long Short-term Memory",
"abstract": "In this work, we first conduct mathematical analysis on the memory, which is\ndefined as a function that maps an element in a sequence to the current output,\nof three RNN cells; namely, the simple recurrent neural network (... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7
]
},
"scores": {
"Solid": null,
... | [
[
{
"role": "PC",
"data": {
"comment": "The reviewers of the paper are not very enthusiastic of the new model proposed, nor are they very happy with the experiments presented. It is unclear from both the POS tagging and dependency parsing results where they stand with respect to state of t... | [
"The paper proposes a new recurrent cell and a new way to make predictions for sequence tagging. It starts with a theoretical analysis of memory capabilities in different RNN cells and goes on with experiments on POS tagging and dependency parsing. There are serious presentation issues in the paper, which make it h... | [
[
1
],
[
0,
3
],
[
5
],
[
15
],
[
8
],
[
10
],
[
11
],
[
4
],
[
6,
9,
14
],
[
7
],
[
16
],
[
2
],
[
12
],
[
13
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
},
{
"role": "Author",
"data": [
8,
120
]
}
],
"category": [
"CLAR-WRT"
]
},
{
"sentences": [
{
"role": "Reviewer 1... | benchmark/PDF/ICLR2018_r1AMITFaW.pdf | openreview | benchmark/MD/ICLR2018_r1AMITFaW.md | ICLR 2018 |
Hkbd5xZRb | {
"title": "Spherical CNNs",
"abstract": "Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional visi... | Accept (Oral) | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "This work introduces a trainable signal representation for spherical signals (functions defined in the sphere) which are rotationally equivariant by design, by extending CNNs to the corresponding group SO(3). The method is implemented efficiently using... | [
"Summary:\nThe paper proposes a framework for constructing spherical convolutional networks (ConvNets) based on a novel synthesis of several existing concepts. The goal is to detect patterns in spherical signals irrespective of how they are rotated on the sphere. The key is to make the convolutional architecture ... | [
[
5
],
[
6
],
[
10
],
[
11
],
[
12
],
[
13
],
[
14
],
[
15
],
[
16
],
[
19
],
[
1,
17,
21
],
[
2
],
[
3
],
[
22
],
[
4
],
[
9
],
[
7
],
[
8
],
[
0
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"ORIG-MTH",
"CLAR... | benchmark/PDF/ICLR2018_Hkbd5xZRb.pdf | openreview | benchmark/MD/ICLR2018_Hkbd5xZRb.md | ICLR 2018 |
rJIgf7bAZ | {
"title": "An inference-based policy gradient method for learning options",
"abstract": "In the pursuit of increasingly intelligent learning systems, abstraction plays a vital role in enabling sophisticated decisions to be made in complex environments. The options framework provides formalism for such abstraction ... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3
]
},
"scores": {
"Solid": null,
"Presentation": null,
"Novelty": null,
... | [
[
{
"role": "PC",
"data": {
"comment": "The reviewers are unanimous that this is an interesting paper, but that ultimately the empirical results are not sufficiently promising to warrant the added complexity."
}
}
]
] | [
"This paper treats option discovery as being analogous to discovering useful latent variables. The proposed formulation assumes there is a policy over options, which invokes an option’s policy to select actions at each timestep until the option’s termination function is activated. A contribution of this paper is ... | [
[
10
],
[
8
],
[
1,
9
],
[
3
],
[
5
],
[
6
],
[
0
],
[
2
],
[
4
],
[
7
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1,
2
]
},
{
"role": "Author",
... | benchmark/PDF/ICLR2018_rJIgf7bAZ.pdf | openreview | benchmark/MD/ICLR2018_rJIgf7bAZ.md | ICLR 2018 |
SkYXvCR6W | {
"title": "Compact Encoding of Words for Efficient Character-level Convolutional Neural Networks Text Classification",
"abstract": "This paper puts forward a new text to tensor representation that relies on information compression techniques to assign shorter codes to the most frequently used characters. This repr... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4
]
},
"scores": {
"Solid": null,
"Presentation": null,
"No... | [
[
{
"role": "PC",
"data": {
"comment": "meta score: 4\n\nThe paper has been extensively edited during the review process - the edits are so extensive that I think the paper requires a re-review, which is not possible for ICLR 2018\n\nPros:\n - potentially interesting and novel approach to pr... | [
"The paper proposed to encode text into a binary matrix by using a compressing code for each word in each matrix row. The idea is interesting, and overall introduction is clear.",
"However, the work lacks justification for this particular way of encoding, and no comparison for any other encoding mechanism is prov... | [
[
21
],
[
0
],
[
7
],
[
13,
19
],
[
15
],
[
14
],
[
1
],
[
4
],
[
10
],
[
11
],
[
12
],
[
18
],
[
20
],
[
2
],
[
3
],
[
6,
16
],
[
8
],
[
9
],
[
5
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
}
],
"category": [
"CLAR-WRT"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
},
{
"role": "Author",
"dat... | benchmark/PDF/ICLR2018_SkYXvCR6W.pdf | openreview | benchmark/MD/ICLR2018_SkYXvCR6W.md | ICLR 2018 |
HJg1NTGZRZ | {
"title": "Bit-Regularized Optimization of Neural Nets",
"abstract": "We present a novel regularization strategy for training neural networks which we call ``BitNet''. The parameters of neural networks are usually unconstrained and have a dynamic range dispersed over a real valued range. Our key idea is to control... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5
]
},
"scores": {
"Solid": null,
"Presentation": null... | [
[
{
"role": "PC",
"data": {
"comment": "Pros:\n+ The idea of end-to-end training that simultaneously learns the weights and appropriate precision for those weights is very appealing.\n\nCons:\n- Experimental results are far from the state-of-the-art, which makes the empirical evaluation unco... | [
"This paper proposes a direct way to learn low-bit neural nets. The idea is introduced clearly and rather straightforward.\npros:",
"(1) The idea is introduced clearly and rather straightforward.",
"(2) The introduction and related work are well written.",
"cons:\nThe provided experiments are weak to demonstr... | [
[
0
],
[
1
],
[
8
],
[
12
],
[
2,
7
],
[
3,
14
],
[
4,
19
],
[
5
],
[
6
],
[
11
],
[
13
],
[
15
],
[
9
],
[
10
],
[
17
],
[
18
],
[
20
],
[
16
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0,
1
]
}
],
"category": [
"CLAR-WRT"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
]
}
],
"category": [
"CL... | benchmark/PDF/ICLR2018_HJg1NTGZRZ.pdf | openreview | benchmark/MD/ICLR2018_HJg1NTGZRZ.md | ICLR 2018 |
r1cLblgCZ | {
"title": "Recurrent Auto-Encoder Model for Multidimensional Time Series Representation",
"abstract": "Recurrent auto-encoder model can summarise sequential data through an encoder structure into a fixed-length vector and then reconstruct into its original sequential form through the decoder structure. The summari... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0
]
},
"scores": {
"Solid": null,
"Presentation": null,
"Novelty": null,
"Overall": 2,
"Confidence"... | [
[
{
"role": "PC",
"data": {
"comment": "This paper applies a form of recurrent autoencoder for a specific type of industrial sensor signal analysis. The application is very narrow and the data set is proprietary. The approach is not clearly described, but seems very straightforward and is ... | [
"This writeup describes an application of recurrent autoencoder to analysis of multidimensional time series. The quality of writing, experimentation and scholarship is clearly below than what is expected from a scientific article. The method is explained in a very unclear way, there is no mention of any related wor... | [
[
3
],
[
0
],
[
2
],
[
5
],
[
4
],
[
7
],
[
6
],
[
9
],
[
10
],
[
8,
11
],
[
1
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
},
{
"role": "Author",
"data": [
1,
2
]
}
],
"category": [
"CLAR-WRT",
"QUAL-EXP",
"QUAL-CMP"
]
},
{
"sentences": [
... | benchmark/PDF/ICLR2018_r1cLblgCZ.pdf | openreview | benchmark/MD/ICLR2018_r1cLblgCZ.md | ICLR 2018 |
ry4S90l0b | {
"title": "A Self-Training Method for Semi-Supervised GANs",
"abstract": "Since the creation of Generative Adversarial Networks (GANs), much work has been done to improve their training stability, their generated image quality, their range of application but nearly none of them explored their self-training potenti... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7
]
},
"scores": {
"Solid": null,
... | [
[
{
"role": "PC",
"data": {
"comment": "The paper presents self-training scheme for GANs. The proposed idea is simple but reasonable, and the experimental results show promise for MNIST and CIFAR10. However, the novelty of the proposed method seems relatively small and experimental results l... | [
"This paper proposes to use self-training strategies for using unlabeled data in GAN. Experiments on only one data set, i.e., MNIST, are conducted\nPros:",
"* Studying how to use unlabeled data to improve performance of GAN is of technical importance. The use of the self-training in GAN for exploiting unlabeled d... | [
[
12
],
[
14
],
[
16,
19
],
[
2
],
[
13
],
[
6,
9
],
[
11
],
[
0,
7
],
[
3
],
[
5,
8
],
[
10
],
[
17
],
[
18
],
[
1
],
[
4
],
[
15
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
},
{
"role": "Author",
"data": [
14
]
}
],
"category": [
"QUAL-EXP"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"da... | benchmark/PDF/ICLR2018_ry4S90l0b.pdf | openreview | benchmark/MD/ICLR2018_ry4S90l0b.md | ICLR 2018 |
Sktm4zWRb | {
"title": "Soft Value Iteration Networks for Planetary Rover Path Planning",
"abstract": "Value iteration networks are an approximation of the value iteration (VI) algorithm implemented with convolutional neural networks to make VI fully differentiable. In this work, we study these networks in the context of robot... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
]
},... | [
[
{
"role": "PC",
"data": {
"comment": "The authors have proposed a 'soft' version of VIN which is differentiable, where the cost function is trained by behavior cloning / imitation learning from expert/computer trajectories. The method is applied to a toy problem and to real historical data... | [
"Summary:\nThe Value-Iteration-Network (VIN) architecture is modified to have a softmax loss function at the end. This is termed SVIN. It is then applied in a behavior cloning manner to the task of rover path planning from start to goal from overhead imagery.",
"Simulation results on binary obstacle maps and usin... | [
[
6
],
[
7
],
[
22
],
[
23
],
[
19
],
[
3
],
[
18
],
[
20
],
[
21
],
[
28,
32
],
[
12
],
[
16
],
[
2
],
[
4
],
[
5
],
[
27
],
[
33
],
[
34
],
[
37,
38
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"c... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
]
},
{
"role": "Author",
... | benchmark/PDF/ICLR2018_Sktm4zWRb.pdf | openreview | benchmark/MD/ICLR2018_Sktm4zWRb.md | ICLR 2018 |
ByL48G-AW | {
"title": "Simple Nearest Neighbor Policy Method for Continuous Control Tasks",
"abstract": "We design a new policy, called a nearest neighbor policy, that does not require any optimization for simple, low-dimensional continuous control tasks. As this policy does not require any optimization, it allows us to inves... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4
]
},
"scores": {
"Solid": null,
"Presentation": null,
"No... | [
[
{
"role": "PC",
"data": {
"comment": "Evaluating simple baselines for continuous control is important and nearest neighbor search methods are interesting. However, the reviewers think that the paper lacks citation and comparison to some prior work and evaluation on more challenging benchma... | [
"SUMMARY\nThe paper deal with the problem of RL. It proposes a non-parametric approach that maps trajectories to the optimal policy. It avoids learning parameterized policies. The fundamental idea is to store passed trajectories.",
"When a policy is to be executed, it does nearest neighbor search to find then ... | [
[
2
],
[
5
],
[
6,
10
],
[
14
],
[
8,
9,
12
],
[
1
],
[
3
],
[
4
],
[
7
],
[
0
],
[
11
],
[
13
]
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
]
},
{
"role": "Author",
... | benchmark/PDF/ICLR2018_ByL48G-AW.pdf | openreview | benchmark/MD/ICLR2018_ByL48G-AW.md | ICLR 2018 |
H1Ww66x0- | {
"title": "Lifelong Learning with Output Kernels",
"abstract": "Lifelong learning poses considerable challenges in terms of effectiveness (minimizing prediction errors for all tasks) and overall computational tractability for real-time performance. This paper addresses continuous lifelong multitask learning by jo... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "The output kernel idea for lifelong learning is interesting, but insufficiently developed in the current draft."
}
}
]
] | [
"CONTRIBUTION\nThe main contribution of the paper is not clearly stated. To the reviewer, It seems “life-long learning” is the same as “online learning”.",
"However, the whole paper does not define what “life-long learning” is.",
"The limited budget scheme is well established in the literature.",
"1. J. Hu, ... | [
[
5,
10
],
[
6,
16
],
[
7
],
[
8
],
[
13
],
[
0,
4
],
[
9
],
[
14,
15
],
[
20
],
[
21
],
[
1
],
[
2
],
[
3
],
[
23
],
[
17,
22
],
[
19
],
[
11
],
[
12
],
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct"
] | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0,
1
]
},
{
"role": "Author",
"data": [
18,
19,
56,
57,
58,
59,
60,
61,
62,
63
... | benchmark/PDF/ICLR2018_H1Ww66x0-.pdf | openreview | benchmark/MD/ICLR2018_H1Ww66x0-.md | ICLR 2018 |
S1Ow_e-Rb | {
"title": "How do deep convolutional neural networks learn from raw audio waveforms?",
"abstract": "Prior work on speech and audio processing has demonstrated the ability to obtain excellent performance when learning directly from raw audio waveforms using convolutional neural networks (CNNs). However, the exact i... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4
]
},
"scores": {
"Solid": null,
"Presentation": null,
"No... | [
[
{
"role": "PC",
"data": {
"comment": "The reviewers rightly point out that presented analysis is limiting and that the experimental results are not extensive enough. Moreover, several existing work that use raw waveforms have interesting analysis of what the network is trying to learn. Giv... | [
"Summary:\nThe authors aim to analyze what deep CNNs learn, and end up proposing “SimpleNet”, which essentially reduces the early feature extraction stage of the network to a single convolutional layer, which is initialized using pre-defined filters. The authors step through a specific example involving bandpass fi... | [
[
11
],
[
23
],
[
7,
29
],
[
21
],
[
31
],
[
34
],
[
40
],
[
1
],
[
17
],
[
12
],
[
2
],
[
6,
30
],
[
10
],
[
14
],
[
20
],
[
22
],
[
24
],
[
27
],
[
33
],
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
},
{
"role": "Author",
"data": [
... | benchmark/PDF/ICLR2018_S1Ow_e-Rb.pdf | openreview | benchmark/MD/ICLR2018_S1Ow_e-Rb.md | ICLR 2018 |
H1kMMmb0- | {
"title": "Sequential Coordination of Deep Models for Learning Visual Arithmetic",
"abstract": "Achieving machine intelligence requires a smooth integration of perception and reasoning, yet models developed to date tend to specialize in one or the other; sophisticated manipulation of symbols acquired from rich per... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "The consensus among the reviewers is that this paper is not quite ready for publication for reasons I will summarize in more detail below. However, I think there are some things that are really nice about this approach, and worth calling out:\n\nPROS:\... | [
"Summary: This work is a variant of previous work (Zaremba et al. 2016) that enables the use of (noisy) operators that invoke pre-trained neural networks and is trained with Actor-Critic. In this regard it lacks a bit of originality. The quality of the experimental evaluation is not great. The clarity of the paper ... | [
[
23
],
[
24
],
[
7
],
[
13
],
[
21
],
[
26
],
[
1
],
[
27
],
[
0,
22,
28
],
[
3
],
[
5
],
[
9
],
[
10
],
[
12
],
[
19
],
[
6
],
[
8
],
[
14
],
[
16
],
[
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0,
1,
3,
5,
6,
7,
8
]
},
{
"role": "Author",
"data": [
46,
47,
61,
62,
63,
... | benchmark/PDF/ICLR2018_H1kMMmb0-.pdf | openreview | benchmark/MD/ICLR2018_H1kMMmb0-.md | ICLR 2018 |
SyF7Erp6W | {
"title": "Learning to play slot cars and Atari 2600 games in just minutes",
"abstract": "Machine learning algorithms for controlling devices will need to learn quickly, with few trials. Such a goal can be attained with concepts borrowed from continental philosophy and formalized using tools from the mathematical ... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "This paper does not seem completely appropriate for ICLR."
}
}
]
] | [
"The authors argue that many machine learning systems need a large amount of data and long training times. To mend those shortcomings their proposed algorithm takes the novel approach of combining mathematical category theory and continental philosophy.",
"Instead of computation units, the concept of entities an... | [
[
2
],
[
3
],
[
4
],
[
5
],
[
6
],
[
7
],
[
10
],
[
11
],
[
12
],
[
15
],
[
16
],
[
17
],
[
19
],
[
22
],
[
23
],
[
24
],
[
25
],
[
0
],
[
1,
21
],
[
8
],... | [
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"c... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"incorrect",
"incorrect",
"correct",
"correc... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"ORIG-COM"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
2
]
}
],
"category": [
"ORIG... | benchmark/PDF/ICLR2018_SyF7Erp6W.pdf | openreview | benchmark/MD/ICLR2018_SyF7Erp6W.md | ICLR 2018 |
rJIN_4lA- | {
"title": "Maintaining cooperation in complex social dilemmas using deep reinforcement learning",
"abstract": "Social dilemmas are situations where individuals face a temptation to increase their payoffs at a cost to total welfare. Building artificially intelligent agents that achieve good outcomes in these situat... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "The reviewers found numerous issues in the paper, including unclear problem definitions, lack of motivation, no support for desiderata, clarity issues, points in discussion appearing to be technically incorrect, restrictive setting, sloppy definitions,... | [
"This paper addresses multiagent learning problems in which there is a social dilemma: settings where there are no 'cooperative polices' that form an equilibrium. The paper proposes a way of dealing with these problems via amTFT, a variation of the well-known tit-for-that strategy, and presents some empirical resul... | [
[
14
],
[
5
],
[
32,
34
],
[
36
],
[
1
],
[
2,
16,
26
],
[
4
],
[
13,
19
],
[
18
],
[
21
],
[
24
],
[
35
],
[
37
],
[
28
],
[
27,
29
],
[
31
],
[
15
],
[
30... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct"... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0
]
}
],
"category": [
"N/A"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
},
{
"role": "Author",
"data": [
... | benchmark/PDF/ICLR2018_rJIN_4lA-.pdf | openreview | benchmark/MD/ICLR2018_rJIN_4lA-.md | ICLR 2018 |
BkS3fnl0W | {
"title": "Semi-supervised Outlier Detection using Generative And Adversary Framework",
"abstract": "In a conventional binary/multi-class classification task, the decision boundary is supported by data from two or more classes. However, in one-class classification task, only data from one class are available. To b... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "This paper presents a framework where GANs are used to improve detection of outliers (in this context, instances of the “background class”). This is a very interesting and, as demonstrated, promising idea. However, the general feeling of the reviewers ... | [
"The idea of the paper is to use a GAN-like training to learn a novelty detection approach.",
"In contrast to traditional GANs, this approach does not aim at convergence, where the generator has nicely learned to fool the discriminator with examples from the same data distribution. The goal is to build up a serie... | [
[
21
],
[
8
],
[
9
],
[
10
],
[
11
],
[
20
],
[
23
],
[
24
],
[
27
],
[
29
],
[
0
],
[
2
],
[
12
],
[
18
],
[
4
],
[
5
],
[
17
],
[
19,
28
],
[
25
],
[
3
... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"cor... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
... | [
{
"sentences": [
{
"role": "Reviewer",
"data": [
0,
1
]
}
],
"category": [
"ORIG-MTH"
]
},
{
"sentences": [
{
"role": "Reviewer",
"data": [
2
]
}
],
"category": [
"N/A"
... | benchmark/PDF/ICLR2018_BkS3fnl0W.pdf | openreview | benchmark/MD/ICLR2018_BkS3fnl0W.md | ICLR 2018 |
rkWN3g-AZ | {
"title": "XGAN: Unsupervised Image-to-Image Translation for many-to-many Mappings",
"abstract": "Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter. Here we tackle the more generic pr... | Reject | [
[
{
"role": "Reviewer",
"data": {
"summary_of_the_paper": null,
"value": {
"review": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
... | [
[
{
"role": "PC",
"data": {
"comment": "This paper was reviewed by 3 expert reviewers. All three recommend rejection citing significant concerns (e.g. missing baselines)."
}
}
]
] | [
"- Lack of novelty\nThe paper has very limited novelty since the proposed method is a straightforward combination of two prior works on the same topic (unpair/unsupervised image translation or cross-domain image generation) where the two prior works are the DTN work [a] and the UNIT [b] work. To be more precise, th... | [
[
35
],
[
40
],
[
5
],
[
15,
23
],
[
19
],
[
30
],
[
31
],
[
34
],
[
36
],
[
38
],
[
39
],
[
0,
1
],
[
8
],
[
14
],
[
20
],
[
3
],
[
13,
16,
21
],
[
24
],
[... | [
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"c... | [
"correct",
"correct",
"incorrect",
"incorrect",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
"correct",
... | [
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
0
]
}
],
"category": [
"ORIG-COM"
]
},
{
"sentences": [
{
"role": "Reviewer 1",
"data": [
1
]
}
],
"category": [
"ORIG-COM"
]... | benchmark/PDF/ICLR2018_rkWN3g-AZ.pdf | openreview | benchmark/MD/ICLR2018_rkWN3g-AZ.md | ICLR 2018 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.