paper_id stringlengths 13 13 | title stringlengths 14 123 | abstract stringlengths 72 1.87k | full_text stringlengths 0 89.3k | authors stringlengths 10 110 | decision stringclasses 1
value | year int64 2.01k 2.01k | api_raw_submission stringlengths 798 2.87k | review stringlengths 12 16.6k | reviewer_id stringlengths 2 110 | rating null | confidence null | api_raw_review stringlengths 455 17.4k | criteria_count dict | reward_value float64 0 3.25 | reward_value_length_adjusted float64 -303.12 2.54 | length_penalty float64 0 305 | reward_u float64 0 3.27 | reward_h float64 0.01 1.63 | meteor_score float64 0 0.18 | criticism float64 0 1 | example float64 0 0.5 | importance_and_relevance float64 0 1 | materials_and_methods float64 0 1 | praise float64 0 0.67 | presentation_and_reporting float64 0 1 | results_and_discussion float64 0 1 | suggestion_and_solution float64 0 1 | dimension_scores dict | overall_score float64 0 3.25 | source stringclasses 1
value | review_src stringclasses 1
value | relative_rank int64 0 0 | win_prob float64 0 0 | thinking_trace stringclasses 1
value | prompt stringclasses 1
value | prompt_length int64 0 0 | conversations null |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
zzy0H3ZbWiHsS | Audio Artist Identification by Deep Neural Network | Since officially began in 2005, the annual Music Information Retrieval Evaluation eXchange (MIREX) has made great contributions to the Music Information Retrieval (MIR) research. By defining some important tasks and providing a meaningful comparison system, the International Music Information Retrieval Systems Evaluati... | 胡振, Kun Fu, Changshui Zhang | Unknown | 2,013 | {"id": "zzy0H3ZbWiHsS", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358325000000, "tmdate": 1358325000000, "ddate": null, "number": 1, "content": {"decision": "reject", "title": "Audio Artist Identification by Deep Neural Network", "abstract": "Since officially began in 2005... | [Review]:
Thank you. We will revise our paper as soon as possible.
Zhen | 胡振 | null | null | {"id": "qbjSYWhow-bDl", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362725700000, "tmdate": 1362725700000, "ddate": null, "number": 3, "content": {"title": "", "review": "Thank you. We will revise our paper as soon as possible.\r\n\r\nZhen"}, "forum": "zzy0H3ZbWiHsS", "refer... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 1,
"total": 3
} | 0.333333 | -1.24491 | 1.578243 | 0.333333 | 0.053333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.333333 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0.3333333333333333
} | 0.333333 | iclr2013 | openreview | 0 | 0 | 0 | null | |||
zzy0H3ZbWiHsS | Audio Artist Identification by Deep Neural Network | Since officially began in 2005, the annual Music Information Retrieval Evaluation eXchange (MIREX) has made great contributions to the Music Information Retrieval (MIR) research. By defining some important tasks and providing a meaningful comparison system, the International Music Information Retrieval Systems Evaluati... | 胡振, Kun Fu, Changshui Zhang | Unknown | 2,013 | {"id": "zzy0H3ZbWiHsS", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358325000000, "tmdate": 1358325000000, "ddate": null, "number": 1, "content": {"decision": "reject", "title": "Audio Artist Identification by Deep Neural Network", "abstract": "Since officially began in 2005... | [Review]:
This paper present an application of an hybrid deep learning model to the task of audio artist identification.
Novelty:
+ The novelty of the paper comes from using an hybrid unsupervised learning approach by stacking Denoising Auto-Encoders (DA) and Restricted Boltzman Machines (RBM).
= Another minor no... | anonymous reviewer 8eb9 | null | null | {"id": "obqUAuHWC9mWc", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362137160000, "tmdate": 1362137160000, "ddate": null, "number": 2, "content": {"title": "review of Audio Artist Identification by Deep Neural Network", "review": "This paper present an application of an hybr... | {
"criticism": 9,
"example": 2,
"importance_and_relevance": 3,
"materials_and_methods": 13,
"praise": 4,
"presentation_and_reporting": 11,
"results_and_discussion": 1,
"suggestion_and_solution": 3,
"total": 35
} | 1.314286 | -6.32441 | 7.638696 | 1.314286 | 0.034571 | 0 | 0.257143 | 0.057143 | 0.085714 | 0.371429 | 0.114286 | 0.314286 | 0.028571 | 0.085714 | {
"criticism": 0.2571428571428571,
"example": 0.05714285714285714,
"importance_and_relevance": 0.08571428571428572,
"materials_and_methods": 0.37142857142857144,
"praise": 0.11428571428571428,
"presentation_and_reporting": 0.3142857142857143,
"results_and_discussion": 0.02857142857142857,
"suggestion_an... | 1.314286 | iclr2013 | openreview | 0 | 0 | 0 | null | |||
zzy0H3ZbWiHsS | Audio Artist Identification by Deep Neural Network | Since officially began in 2005, the annual Music Information Retrieval Evaluation eXchange (MIREX) has made great contributions to the Music Information Retrieval (MIR) research. By defining some important tasks and providing a meaningful comparison system, the International Music Information Retrieval Systems Evaluati... | 胡振, Kun Fu, Changshui Zhang | Unknown | 2,013 | {"id": "zzy0H3ZbWiHsS", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358325000000, "tmdate": 1358325000000, "ddate": null, "number": 1, "content": {"decision": "reject", "title": "Audio Artist Identification by Deep Neural Network", "abstract": "Since officially began in 2005... | [Review]:
This paper describes work to collect a new dataset with music from 11 classical composers for the task of audio composer identification (although the title, abstract, and introduction use the phrase 'audio artist identification' which is a different task). It describes experiments training a few different de... | anonymous reviewer b7e1 | null | null | {"id": "k3fr32tl6qARo", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362226800000, "tmdate": 1362226800000, "ddate": null, "number": 4, "content": {"title": "review of Audio Artist Identification by Deep Neural Network", "review": "This paper describes work to collect a new d... | {
"criticism": 6,
"example": 1,
"importance_and_relevance": 0,
"materials_and_methods": 10,
"praise": 0,
"presentation_and_reporting": 3,
"results_and_discussion": 3,
"suggestion_and_solution": 2,
"total": 20
} | 1.25 | 0.476661 | 0.773339 | 1.25 | 0.0275 | 0 | 0.3 | 0.05 | 0 | 0.5 | 0 | 0.15 | 0.15 | 0.1 | {
"criticism": 0.3,
"example": 0.05,
"importance_and_relevance": 0,
"materials_and_methods": 0.5,
"praise": 0,
"presentation_and_reporting": 0.15,
"results_and_discussion": 0.15,
"suggestion_and_solution": 0.1
} | 1.25 | iclr2013 | openreview | 0 | 0 | 0 | null | |||
zzy0H3ZbWiHsS | Audio Artist Identification by Deep Neural Network | Since officially began in 2005, the annual Music Information Retrieval Evaluation eXchange (MIREX) has made great contributions to the Music Information Retrieval (MIR) research. By defining some important tasks and providing a meaningful comparison system, the International Music Information Retrieval Systems Evaluati... | 胡振, Kun Fu, Changshui Zhang | Unknown | 2,013 | {"id": "zzy0H3ZbWiHsS", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358325000000, "tmdate": 1358325000000, "ddate": null, "number": 1, "content": {"decision": "reject", "title": "Audio Artist Identification by Deep Neural Network", "abstract": "Since officially began in 2005... | [Review]:
A brief summary of the paper’s contributions. In the context of prior work:
This paper builds a hybrid model based on Deep Belief Network (DBN) and Stacked Denoising Autoencoder (SDA) and applies it to Audio Artist Identification (AAI) task. Specifically, the proposed model is constructed with a two-layer SD... | anonymous reviewer 589d | null | null | {"id": "Zg8fgYb5dAUiY", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362479820000, "tmdate": 1362479820000, "ddate": null, "number": 1, "content": {"title": "review of Audio Artist Identification by Deep Neural Network", "review": "A brief summary of the paper\u2019s contribu... | {
"criticism": 11,
"example": 3,
"importance_and_relevance": 3,
"materials_and_methods": 11,
"praise": 3,
"presentation_and_reporting": 4,
"results_and_discussion": 3,
"suggestion_and_solution": 4,
"total": 19
} | 2.210526 | 1.642359 | 0.568167 | 2.210526 | 0.069474 | 0 | 0.578947 | 0.157895 | 0.157895 | 0.578947 | 0.157895 | 0.210526 | 0.157895 | 0.210526 | {
"criticism": 0.5789473684210527,
"example": 0.15789473684210525,
"importance_and_relevance": 0.15789473684210525,
"materials_and_methods": 0.5789473684210527,
"praise": 0.15789473684210525,
"presentation_and_reporting": 0.21052631578947367,
"results_and_discussion": 0.15789473684210525,
"suggestion_an... | 2.210526 | iclr2013 | openreview | 0 | 0 | 0 | null | |||
zzKhQhsTYlzAZ | Regularized Discriminant Embedding for Visual Descriptor Learning | Images can vary according to changes in viewpoint, resolution, noise, and illumination. In this paper, we aim to learn representations for an image, which are robust to wide changes in such environmental conditions, using training pairs of matching and non-matching local image patches that are collected under various e... | Regularized Discriminant Embedding for Visual
Descriptor Learning
Kye-Hyeon Kim,a Rui Cai,b Lei Zhang,b Seungjin Choia∗
a Department of Computer Science, POSTECH, Pohang 790-784, Korea
b Microsoft Research Asia, Beijing 100080, China
fenrir@postech.ac.kr, {ruicai, leizhang}@microsoft.com,
seungjin@postech.ac.kr
Abstrac... | Kye-Hyeon Kim, Rui Cai, Lei Zhang, Seungjin Choi | Unknown | 2,013 | {"id": "zzKhQhsTYlzAZ", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358487000000, "tmdate": 1358487000000, "ddate": null, "number": 18, "content": {"title": "Regularized Discriminant Embedding for Visual Descriptor Learning", "decision": "conferencePoster-iclr2013-workshop",... | [Review]:
We sincerely appreciate all the reviewers for their time and comments to this manuscript.
We fully agree that it is really hard to find maningful contributions from this short paper, while we tried our best to emphasize them. As we have noted, the full version of this manuscript is currently under review in ... | Kye-Hyeon Kim, Rui Cai, Lei Zhang, Seungjin Choi | null | null | {"id": "Xf5Pf5SWhtEYT", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363779180000, "tmdate": 1363779180000, "ddate": null, "number": 3, "content": {"title": "", "review": "We sincerely appreciate all the reviewers for their time and comments to this manuscript.\r\nWe fully ag... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 4,
"praise": 2,
"presentation_and_reporting": 1,
"results_and_discussion": 2,
"suggestion_and_solution": 1,
"total": 9
} | 1.222222 | 0.969703 | 0.252519 | 1.273886 | 0.487922 | 0.051663 | 0 | 0 | 0.111111 | 0.444444 | 0.222222 | 0.111111 | 0.222222 | 0.111111 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0.1111111111111111,
"materials_and_methods": 0.4444444444444444,
"praise": 0.2222222222222222,
"presentation_and_reporting": 0.1111111111111111,
"results_and_discussion": 0.2222222222222222,
"suggestion_and_solution": 0.1111111111111111
} | 1.222222 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
zzKhQhsTYlzAZ | Regularized Discriminant Embedding for Visual Descriptor Learning | Images can vary according to changes in viewpoint, resolution, noise, and illumination. In this paper, we aim to learn representations for an image, which are robust to wide changes in such environmental conditions, using training pairs of matching and non-matching local image patches that are collected under various e... | Regularized Discriminant Embedding for Visual
Descriptor Learning
Kye-Hyeon Kim,a Rui Cai,b Lei Zhang,b Seungjin Choia∗
a Department of Computer Science, POSTECH, Pohang 790-784, Korea
b Microsoft Research Asia, Beijing 100080, China
fenrir@postech.ac.kr, {ruicai, leizhang}@microsoft.com,
seungjin@postech.ac.kr
Abstrac... | Kye-Hyeon Kim, Rui Cai, Lei Zhang, Seungjin Choi | Unknown | 2,013 | {"id": "zzKhQhsTYlzAZ", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358487000000, "tmdate": 1358487000000, "ddate": null, "number": 18, "content": {"title": "Regularized Discriminant Embedding for Visual Descriptor Learning", "decision": "conferencePoster-iclr2013-workshop",... | [Review]:
The paper aims to present a method for discriminant analysis for image
descriptors. The formulation splits a given dataset of labeled images
into 4 categories, Relevant/Irrelevant and Near/Far pairs
(RN,RF,IN,IF). The final form of the objective aims to maximize the
ratio of sum of distances of irrelevant... | anonymous reviewer 1e7c | null | null | {"id": "FBx7CpGZiEA32", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362287940000, "tmdate": 1362287940000, "ddate": null, "number": 1, "content": {"title": "review of Regularized Discriminant Embedding for Visual Descriptor Learning", "review": "The paper aims to present a m... | {
"criticism": 2,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 9,
"praise": 0,
"presentation_and_reporting": 3,
"results_and_discussion": 2,
"suggestion_and_solution": 0,
"total": 10
} | 1.7 | 1.557958 | 0.142042 | 1.743967 | 0.408195 | 0.043967 | 0.2 | 0 | 0.1 | 0.9 | 0 | 0.3 | 0.2 | 0 | {
"criticism": 0.2,
"example": 0,
"importance_and_relevance": 0.1,
"materials_and_methods": 0.9,
"praise": 0,
"presentation_and_reporting": 0.3,
"results_and_discussion": 0.2,
"suggestion_and_solution": 0
} | 1.7 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
zzKhQhsTYlzAZ | Regularized Discriminant Embedding for Visual Descriptor Learning | Images can vary according to changes in viewpoint, resolution, noise, and illumination. In this paper, we aim to learn representations for an image, which are robust to wide changes in such environmental conditions, using training pairs of matching and non-matching local image patches that are collected under various e... | Regularized Discriminant Embedding for Visual
Descriptor Learning
Kye-Hyeon Kim,a Rui Cai,b Lei Zhang,b Seungjin Choia∗
a Department of Computer Science, POSTECH, Pohang 790-784, Korea
b Microsoft Research Asia, Beijing 100080, China
fenrir@postech.ac.kr, {ruicai, leizhang}@microsoft.com,
seungjin@postech.ac.kr
Abstrac... | Kye-Hyeon Kim, Rui Cai, Lei Zhang, Seungjin Choi | Unknown | 2,013 | {"id": "zzKhQhsTYlzAZ", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358487000000, "tmdate": 1358487000000, "ddate": null, "number": 18, "content": {"title": "Regularized Discriminant Embedding for Visual Descriptor Learning", "decision": "conferencePoster-iclr2013-workshop",... | [Review]:
This paper describes a method for learning visual feature descriptors that are invariant to changes in illumination, viewpoint, and image quality. The method can be used for multi-view matching and alignment, or for robust image retrieval. The method computes a regularized linear projection of SIFT feature de... | anonymous reviewer 39f1 | null | null | {"id": "-7pc74mqcO-Mr", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362186780000, "tmdate": 1362186780000, "ddate": null, "number": 2, "content": {"title": "review of Regularized Discriminant Embedding for Visual Descriptor Learning", "review": "This paper describes a method... | {
"criticism": 2,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 7,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 1,
"suggestion_and_solution": 1,
"total": 7
} | 1.714286 | 1.146118 | 0.568167 | 1.752292 | 0.382369 | 0.038006 | 0.285714 | 0 | 0.142857 | 1 | 0 | 0 | 0.142857 | 0.142857 | {
"criticism": 0.2857142857142857,
"example": 0,
"importance_and_relevance": 0.14285714285714285,
"materials_and_methods": 1,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0.14285714285714285,
"suggestion_and_solution": 0.14285714285714285
} | 1.714286 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
zzEf5eKLmAG0o | Learning Features with Structure-Adapting Multi-view Exponential Family
Harmoniums | We proposea graphical model for multi-view feature extraction that automatically adapts its structure to achieve better representation of data distribution. The proposed model, structure-adapting multi-view harmonium (SA-MVH) has switch parameters that control the connection between hidden nodes and input views, and le... | arXiv:1301.3539v1 [cs.LG] 16 Jan 2013
Learning Features with Structure-Adapting
Multi-view Exponential Family Harmoniums
Yoonseop Kang1 Seungjin Choi1,2,3
Department of Computer Science and Engineering1,
Division of IT Convergence Engineering2,
Department of Creative Excellence Engineering3,
Pohang University of Scie... | YoonSeop Kang, Seungjin Choi | Unknown | 2,013 | {"id": "zzEf5eKLmAG0o", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358403300000, "tmdate": 1358403300000, "ddate": null, "number": 47, "content": {"title": "Learning Features with Structure-Adapting Multi-view Exponential Family\r\n Harmoniums", "decision": "conferencePo... | [Review]:
The paper introduces an new algorithm for simultaneously learning a hidden layer (latent representation) for multiple data views as well as automatically segmenting that hidden layer into shared and view-specific nodes. It builds on the previous multi-view harmonium (MVH) algorithm by adding (sigmoidal) switc... | anonymous reviewer d966 | null | null | {"id": "UUlHmZjBOIUBb", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362353160000, "tmdate": 1362353160000, "ddate": null, "number": 2, "content": {"title": "review of Learning Features with Structure-Adapting Multi-view Exponential Family\r\n Harmoniums", "review": "The p... | {
"criticism": 3,
"example": 1,
"importance_and_relevance": 2,
"materials_and_methods": 14,
"praise": 4,
"presentation_and_reporting": 4,
"results_and_discussion": 5,
"suggestion_and_solution": 5,
"total": 26
} | 1.461538 | -1.205692 | 2.667231 | 1.573664 | 1.023278 | 0.112125 | 0.115385 | 0.038462 | 0.076923 | 0.538462 | 0.153846 | 0.153846 | 0.192308 | 0.192308 | {
"criticism": 0.11538461538461539,
"example": 0.038461538461538464,
"importance_and_relevance": 0.07692307692307693,
"materials_and_methods": 0.5384615384615384,
"praise": 0.15384615384615385,
"presentation_and_reporting": 0.15384615384615385,
"results_and_discussion": 0.19230769230769232,
"suggestion_... | 1.461538 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
zzEf5eKLmAG0o | Learning Features with Structure-Adapting Multi-view Exponential Family
Harmoniums | We proposea graphical model for multi-view feature extraction that automatically adapts its structure to achieve better representation of data distribution. The proposed model, structure-adapting multi-view harmonium (SA-MVH) has switch parameters that control the connection between hidden nodes and input views, and le... | arXiv:1301.3539v1 [cs.LG] 16 Jan 2013
Learning Features with Structure-Adapting
Multi-view Exponential Family Harmoniums
Yoonseop Kang1 Seungjin Choi1,2,3
Department of Computer Science and Engineering1,
Division of IT Convergence Engineering2,
Department of Creative Excellence Engineering3,
Pohang University of Scie... | YoonSeop Kang, Seungjin Choi | Unknown | 2,013 | {"id": "zzEf5eKLmAG0o", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358403300000, "tmdate": 1358403300000, "ddate": null, "number": 47, "content": {"title": "Learning Features with Structure-Adapting Multi-view Exponential Family\r\n Harmoniums", "decision": "conferencePo... | [Review]:
The authors propose a bipartite, undirected graphical model for multiview learning, called structure-adapting multiview harmonimum (SA-MVH). The model is based on their earlier model called multiview harmonium (MVH) (Kang&Choi, 2011) where hidden units were separated into a shared set and view-specific sets. ... | anonymous reviewer 0e7e | null | null | {"id": "DNKnDqeVJmgPF", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1360866060000, "tmdate": 1360866060000, "ddate": null, "number": 1, "content": {"title": "review of Learning Features with Structure-Adapting Multi-view Exponential Family\r\n Harmoniums", "review": "The a... | {
"criticism": 2,
"example": 1,
"importance_and_relevance": 0,
"materials_and_methods": 9,
"praise": 0,
"presentation_and_reporting": 4,
"results_and_discussion": 1,
"suggestion_and_solution": 4,
"total": 13
} | 1.615385 | 1.615385 | 0 | 1.690931 | 0.717291 | 0.075546 | 0.153846 | 0.076923 | 0 | 0.692308 | 0 | 0.307692 | 0.076923 | 0.307692 | {
"criticism": 0.15384615384615385,
"example": 0.07692307692307693,
"importance_and_relevance": 0,
"materials_and_methods": 0.6923076923076923,
"praise": 0,
"presentation_and_reporting": 0.3076923076923077,
"results_and_discussion": 0.07692307692307693,
"suggestion_and_solution": 0.3076923076923077
} | 1.615385 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
yyC_7RZTkUD5- | Deep Predictive Coding Networks | The quality of data representation in deep learning methods is directly related to the prior model imposed on the representations; however, generally used fixed priors are not capable of adjusting to the context in the data. To address this issue, we propose deep predictive coding networks, a hierarchical generative mo... | arXiv:1301.3541v3 [cs.LG] 15 Mar 2013
Deep Predictive Coding Networks
Rakesh Chalasani Jose C. Principe
Department of Electrical and Computer Engineering
University of Florida, Gainesville, FL 32611
rakeshch@ufl.edu, principe@cnel.ufl.edu
Abstract
The quality of data representation in deep learning methods is directl... | Rakesh Chalasani, Jose C. Principe | Unknown | 2,013 | {"id": "yyC_7RZTkUD5-", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358405100000, "tmdate": 1358405100000, "ddate": null, "number": 27, "content": {"title": "Deep Predictive Coding Networks", "decision": "conferencePoster-iclr2013-workshop", "abstract": "The quality of data ... | [Review]:
Deep predictive coding networks
This paper introduces a new model which combines bottom-up, top-down, and temporal information to learning a generative model in an unsupervised fashion on videos. The model is formulated in terms of states, which carry temporal consistency information between time steps, an... | anonymous reviewer ac47 | null | null | {"id": "d6u7vbCNJV6Q8", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1361968020000, "tmdate": 1361968020000, "ddate": null, "number": 3, "content": {"title": "review of Deep Predictive Coding Networks", "review": "Deep predictive coding networks\r\n\r\nThis paper introduces a ... | {
"criticism": 8,
"example": 0,
"importance_and_relevance": 2,
"materials_and_methods": 13,
"praise": 2,
"presentation_and_reporting": 3,
"results_and_discussion": 4,
"suggestion_and_solution": 3,
"total": 18
} | 1.944444 | 1.549884 | 0.394561 | 1.96768 | 0.25701 | 0.023236 | 0.444444 | 0 | 0.111111 | 0.722222 | 0.111111 | 0.166667 | 0.222222 | 0.166667 | {
"criticism": 0.4444444444444444,
"example": 0,
"importance_and_relevance": 0.1111111111111111,
"materials_and_methods": 0.7222222222222222,
"praise": 0.1111111111111111,
"presentation_and_reporting": 0.16666666666666666,
"results_and_discussion": 0.2222222222222222,
"suggestion_and_solution": 0.166666... | 1.944444 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
yyC_7RZTkUD5- | Deep Predictive Coding Networks | The quality of data representation in deep learning methods is directly related to the prior model imposed on the representations; however, generally used fixed priors are not capable of adjusting to the context in the data. To address this issue, we propose deep predictive coding networks, a hierarchical generative mo... | arXiv:1301.3541v3 [cs.LG] 15 Mar 2013
Deep Predictive Coding Networks
Rakesh Chalasani Jose C. Principe
Department of Electrical and Computer Engineering
University of Florida, Gainesville, FL 32611
rakeshch@ufl.edu, principe@cnel.ufl.edu
Abstract
The quality of data representation in deep learning methods is directl... | Rakesh Chalasani, Jose C. Principe | Unknown | 2,013 | {"id": "yyC_7RZTkUD5-", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358405100000, "tmdate": 1358405100000, "ddate": null, "number": 27, "content": {"title": "Deep Predictive Coding Networks", "decision": "conferencePoster-iclr2013-workshop", "abstract": "The quality of data ... | [Review]:
A brief summary of the paper's contributions, in the context of prior work.
The paper proposes a hierarchical sparse generative model in the context of a dynamical system. The model can capture temporal dependencies in time-varying data, and top-down information (from high-level contextual/causal units) can ... | anonymous reviewer 1829 | null | null | {"id": "Za8LX-xwgqXw5", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362498780000, "tmdate": 1362498780000, "ddate": null, "number": 4, "content": {"title": "review of Deep Predictive Coding Networks", "review": "A brief summary of the paper's contributions, in the context of... | {
"criticism": 7,
"example": 0,
"importance_and_relevance": 5,
"materials_and_methods": 18,
"praise": 3,
"presentation_and_reporting": 5,
"results_and_discussion": 8,
"suggestion_and_solution": 7,
"total": 27
} | 1.962963 | -1.130393 | 3.093356 | 1.987781 | 0.29221 | 0.024818 | 0.259259 | 0 | 0.185185 | 0.666667 | 0.111111 | 0.185185 | 0.296296 | 0.259259 | {
"criticism": 0.25925925925925924,
"example": 0,
"importance_and_relevance": 0.18518518518518517,
"materials_and_methods": 0.6666666666666666,
"praise": 0.1111111111111111,
"presentation_and_reporting": 0.18518518518518517,
"results_and_discussion": 0.2962962962962963,
"suggestion_and_solution": 0.2592... | 1.962963 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
yyC_7RZTkUD5- | Deep Predictive Coding Networks | The quality of data representation in deep learning methods is directly related to the prior model imposed on the representations; however, generally used fixed priors are not capable of adjusting to the context in the data. To address this issue, we propose deep predictive coding networks, a hierarchical generative mo... | arXiv:1301.3541v3 [cs.LG] 15 Mar 2013
Deep Predictive Coding Networks
Rakesh Chalasani Jose C. Principe
Department of Electrical and Computer Engineering
University of Florida, Gainesville, FL 32611
rakeshch@ufl.edu, principe@cnel.ufl.edu
Abstract
The quality of data representation in deep learning methods is directl... | Rakesh Chalasani, Jose C. Principe | Unknown | 2,013 | {"id": "yyC_7RZTkUD5-", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358405100000, "tmdate": 1358405100000, "ddate": null, "number": 27, "content": {"title": "Deep Predictive Coding Networks", "decision": "conferencePoster-iclr2013-workshop", "abstract": "The quality of data ... | [Review]:
The revised paper is uploaded onto arXiv. It will be announced on 18th March.
In the mean time, the paper is also made available at
https://www.dropbox.com/s/klmpu482q6nt1ws/DPCN.pdf | Rakesh Chalasani, Jose C. Principe | null | null | {"id": "Xu4KaWxqIDurf", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363393200000, "tmdate": 1363393200000, "ddate": null, "number": 2, "content": {"title": "", "review": "The revised paper is uploaded onto arXiv. It will be announced on 18th March.\r\n\r\nIn the mean time, t... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0,
"total": 3
} | 0 | -1.578243 | 1.578243 | 0.001723 | 0.014942 | 0.001723 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0
} | 0 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
yyC_7RZTkUD5- | Deep Predictive Coding Networks | The quality of data representation in deep learning methods is directly related to the prior model imposed on the representations; however, generally used fixed priors are not capable of adjusting to the context in the data. To address this issue, we propose deep predictive coding networks, a hierarchical generative mo... | arXiv:1301.3541v3 [cs.LG] 15 Mar 2013
Deep Predictive Coding Networks
Rakesh Chalasani Jose C. Principe
Department of Electrical and Computer Engineering
University of Florida, Gainesville, FL 32611
rakeshch@ufl.edu, principe@cnel.ufl.edu
Abstract
The quality of data representation in deep learning methods is directl... | Rakesh Chalasani, Jose C. Principe | Unknown | 2,013 | {"id": "yyC_7RZTkUD5-", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358405100000, "tmdate": 1358405100000, "ddate": null, "number": 27, "content": {"title": "Deep Predictive Coding Networks", "decision": "conferencePoster-iclr2013-workshop", "abstract": "The quality of data ... | [Review]:
This paper attempts to capture both the temporal dynamics of signals and the contribution of top down connections for inference using a deep model. The experimental results are qualitatively encouraging, and the model structure seems like a sensible direction to pursue. I like the connection to dynamical sy... | anonymous reviewer 62ac | null | null | {"id": "EEhwkCLtAuko7", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362405300000, "tmdate": 1362405300000, "ddate": null, "number": 5, "content": {"title": "review of Deep Predictive Coding Networks", "review": "This paper attempts to capture both the temporal dynamics of si... | {
"criticism": 4,
"example": 1,
"importance_and_relevance": 1,
"materials_and_methods": 7,
"praise": 1,
"presentation_and_reporting": 5,
"results_and_discussion": 4,
"suggestion_and_solution": 4,
"total": 19
} | 1.421053 | 0.852885 | 0.568167 | 1.43727 | 0.191659 | 0.016218 | 0.210526 | 0.052632 | 0.052632 | 0.368421 | 0.052632 | 0.263158 | 0.210526 | 0.210526 | {
"criticism": 0.21052631578947367,
"example": 0.05263157894736842,
"importance_and_relevance": 0.05263157894736842,
"materials_and_methods": 0.3684210526315789,
"praise": 0.05263157894736842,
"presentation_and_reporting": 0.2631578947368421,
"results_and_discussion": 0.21052631578947367,
"suggestion_an... | 1.421053 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
yyC_7RZTkUD5- | Deep Predictive Coding Networks | The quality of data representation in deep learning methods is directly related to the prior model imposed on the representations; however, generally used fixed priors are not capable of adjusting to the context in the data. To address this issue, we propose deep predictive coding networks, a hierarchical generative mo... | arXiv:1301.3541v3 [cs.LG] 15 Mar 2013
Deep Predictive Coding Networks
Rakesh Chalasani Jose C. Principe
Department of Electrical and Computer Engineering
University of Florida, Gainesville, FL 32611
rakeshch@ufl.edu, principe@cnel.ufl.edu
Abstract
The quality of data representation in deep learning methods is directl... | Rakesh Chalasani, Jose C. Principe | Unknown | 2,013 | {"id": "yyC_7RZTkUD5-", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358405100000, "tmdate": 1358405100000, "ddate": null, "number": 27, "content": {"title": "Deep Predictive Coding Networks", "decision": "conferencePoster-iclr2013-workshop", "abstract": "The quality of data ... | [Review]:
Thank you for review and comments. We revised the paper to address most of your concerns. Following is our response to some specific point you have raised.
>>> ' The clarity of the paper needs to be improved. For example, it will be helpful to motivate more clearly about the specific formulation of the mod... | Rakesh Chalasani, Jose C. Principe | null | null | {"id": "3vEUvBbCrO8cu", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363392960000, "tmdate": 1363392960000, "ddate": null, "number": 1, "content": {"title": "", "review": "Thank you for review and comments. We revised the paper to address most of your concerns. Following is o... | {
"criticism": 2,
"example": 2,
"importance_and_relevance": 2,
"materials_and_methods": 6,
"praise": 0,
"presentation_and_reporting": 10,
"results_and_discussion": 1,
"suggestion_and_solution": 8,
"total": 20
} | 1.55 | 0.776661 | 0.773339 | 1.566585 | 0.229293 | 0.016585 | 0.1 | 0.1 | 0.1 | 0.3 | 0 | 0.5 | 0.05 | 0.4 | {
"criticism": 0.1,
"example": 0.1,
"importance_and_relevance": 0.1,
"materials_and_methods": 0.3,
"praise": 0,
"presentation_and_reporting": 0.5,
"results_and_discussion": 0.05,
"suggestion_and_solution": 0.4
} | 1.55 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
yGgjGkkbeFSbt | Saturating Auto-Encoder | We introduce a simple new regularizer for auto-encoders whose hidden-unit activation functions contain at least one zero-gradient (saturated) region. This regularizer explicitly encourages activations in the saturated region(s) of the corresponding activation function. We call these Saturating Auto-Encoders (SATAE). We... | Saturating Auto-Encoders
Rostislav Goroshin∗
Courant Institute of Mathematical Science
New York University
goroshin@cs.nyu.edu
Yann LeCun
Courant Institute of Mathematical Science
New York University
yann@cs.nyu.edu
Abstract
We introduce a simple new regularizer for auto-encoders whose hidden-unit ac-
tivation function... | Ross Goroshin, Yann LeCun | Unknown | 2,013 | {"id": "yGgjGkkbeFSbt", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358468100000, "tmdate": 1358468100000, "ddate": null, "number": 39, "content": {"title": "Saturating Auto-Encoder", "decision": "conferencePoster-iclr2013-conference", "abstract": "We introduce a simple new ... | [Review]:
Although this paper proposes an original (yet trivial) approach to regularize auto-encoders, it does not bring sufficient insights as to why saturating the hidden units should yield a better representation. The authors do not elaborate on whether the SATAE is a more general principle than previously proposed ... | anonymous reviewer 5bc2 | null | null | {"id": "zOUdY11jd_zJr", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362593760000, "tmdate": 1362593760000, "ddate": null, "number": 5, "content": {"title": "review of Saturating Auto-Encoder", "review": "Although this paper proposes an original (yet trivial) approach to regu... | {
"criticism": 3,
"example": 0,
"importance_and_relevance": 3,
"materials_and_methods": 7,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 2,
"total": 7
} | 2.142857 | 1.57469 | 0.568167 | 2.16742 | 0.320103 | 0.024563 | 0.428571 | 0 | 0.428571 | 1 | 0 | 0 | 0 | 0.285714 | {
"criticism": 0.42857142857142855,
"example": 0,
"importance_and_relevance": 0.42857142857142855,
"materials_and_methods": 1,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0.2857142857142857
} | 2.142857 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
yGgjGkkbeFSbt | Saturating Auto-Encoder | We introduce a simple new regularizer for auto-encoders whose hidden-unit activation functions contain at least one zero-gradient (saturated) region. This regularizer explicitly encourages activations in the saturated region(s) of the corresponding activation function. We call these Saturating Auto-Encoders (SATAE). We... | Saturating Auto-Encoders
Rostislav Goroshin∗
Courant Institute of Mathematical Science
New York University
goroshin@cs.nyu.edu
Yann LeCun
Courant Institute of Mathematical Science
New York University
yann@cs.nyu.edu
Abstract
We introduce a simple new regularizer for auto-encoders whose hidden-unit ac-
tivation function... | Ross Goroshin, Yann LeCun | Unknown | 2,013 | {"id": "yGgjGkkbeFSbt", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358468100000, "tmdate": 1358468100000, "ddate": null, "number": 39, "content": {"title": "Saturating Auto-Encoder", "decision": "conferencePoster-iclr2013-conference", "abstract": "We introduce a simple new ... | [Review]:
This is a cool investigation in a direction that I find fascinating, and I only have two remarks about minor points made in the paper.
* Regarding the energy-based interpretation (that reconstruction error can be thought of as an energy function associated with an estimated probability function), there was... | Yoshua Bengio | null | null | {"id": "x9pbTj7Nbg9Qs", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1361902200000, "tmdate": 1361902200000, "ddate": null, "number": 7, "content": {"title": "", "review": "This is a cool investigation in a direction that I find fascinating, and I only have two remarks about m... | {
"criticism": 4,
"example": 0,
"importance_and_relevance": 2,
"materials_and_methods": 4,
"praise": 1,
"presentation_and_reporting": 2,
"results_and_discussion": 6,
"suggestion_and_solution": 1,
"total": 11
} | 1.818182 | 1.755052 | 0.06313 | 1.857044 | 0.386931 | 0.038862 | 0.363636 | 0 | 0.181818 | 0.363636 | 0.090909 | 0.181818 | 0.545455 | 0.090909 | {
"criticism": 0.36363636363636365,
"example": 0,
"importance_and_relevance": 0.18181818181818182,
"materials_and_methods": 0.36363636363636365,
"praise": 0.09090909090909091,
"presentation_and_reporting": 0.18181818181818182,
"results_and_discussion": 0.5454545454545454,
"suggestion_and_solution": 0.09... | 1.818182 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
yGgjGkkbeFSbt | Saturating Auto-Encoder | We introduce a simple new regularizer for auto-encoders whose hidden-unit activation functions contain at least one zero-gradient (saturated) region. This regularizer explicitly encourages activations in the saturated region(s) of the corresponding activation function. We call these Saturating Auto-Encoders (SATAE). We... | Saturating Auto-Encoders
Rostislav Goroshin∗
Courant Institute of Mathematical Science
New York University
goroshin@cs.nyu.edu
Yann LeCun
Courant Institute of Mathematical Science
New York University
yann@cs.nyu.edu
Abstract
We introduce a simple new regularizer for auto-encoders whose hidden-unit ac-
tivation function... | Ross Goroshin, Yann LeCun | Unknown | 2,013 | {"id": "yGgjGkkbeFSbt", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358468100000, "tmdate": 1358468100000, "ddate": null, "number": 39, "content": {"title": "Saturating Auto-Encoder", "decision": "conferencePoster-iclr2013-conference", "abstract": "We introduce a simple new ... | [Review]:
In response to 5bc2: the principle behind SATAE is a unification of the principles behind sparse autoencoders (and sparse coding in general) and contracting autoencoders.
Basically, the main question with unsupervised learning is how to learn a contrast function (energy function in the energy-based framew... | Rostislav Goroshin, Yann LeCun | null | null | {"id": "pn6HDOWYfCDYA", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362779100000, "tmdate": 1362779100000, "ddate": null, "number": 2, "content": {"title": "", "review": "In response to 5bc2: the principle behind SATAE is a unification of the principles behind sparse autoenc... | {
"criticism": 0,
"example": 1,
"importance_and_relevance": 1,
"materials_and_methods": 6,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 1,
"suggestion_and_solution": 2,
"total": 13
} | 0.846154 | 0.846154 | 0 | 0.8917 | 0.434118 | 0.045546 | 0 | 0.076923 | 0.076923 | 0.461538 | 0 | 0 | 0.076923 | 0.153846 | {
"criticism": 0,
"example": 0.07692307692307693,
"importance_and_relevance": 0.07692307692307693,
"materials_and_methods": 0.46153846153846156,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0.07692307692307693,
"suggestion_and_solution": 0.15384615384615385
} | 0.846154 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
yGgjGkkbeFSbt | Saturating Auto-Encoder | We introduce a simple new regularizer for auto-encoders whose hidden-unit activation functions contain at least one zero-gradient (saturated) region. This regularizer explicitly encourages activations in the saturated region(s) of the corresponding activation function. We call these Saturating Auto-Encoders (SATAE). We... | Saturating Auto-Encoders
Rostislav Goroshin∗
Courant Institute of Mathematical Science
New York University
goroshin@cs.nyu.edu
Yann LeCun
Courant Institute of Mathematical Science
New York University
yann@cs.nyu.edu
Abstract
We introduce a simple new regularizer for auto-encoders whose hidden-unit ac-
tivation function... | Ross Goroshin, Yann LeCun | Unknown | 2,013 | {"id": "yGgjGkkbeFSbt", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358468100000, "tmdate": 1358468100000, "ddate": null, "number": 39, "content": {"title": "Saturating Auto-Encoder", "decision": "conferencePoster-iclr2013-conference", "abstract": "We introduce a simple new ... | [Review]:
We thank the reviewers for their constructive comments.
A revised version of the paper has been submitted to arXiv and should be available shortly.
In addition to minor corrections and additions throughout the paper, we have added three new subsections:
(1) a potential extension of the SATAE framewo... | Ross Goroshin | null | null | {"id": "__krPw9SreVyO", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363749480000, "tmdate": 1363749480000, "ddate": null, "number": 1, "content": {"title": "", "review": "We thank the reviewers for their constructive comments.\r\n\r\nA revised version of the paper has been s... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 1,
"praise": 2,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 2,
"total": 4
} | 1.25 | -0.028377 | 1.278377 | 1.259465 | 0.16956 | 0.009465 | 0 | 0 | 0 | 0.25 | 0.5 | 0 | 0 | 0.5 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0.25,
"praise": 0.5,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0.5
} | 1.25 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
yGgjGkkbeFSbt | Saturating Auto-Encoder | We introduce a simple new regularizer for auto-encoders whose hidden-unit activation functions contain at least one zero-gradient (saturated) region. This regularizer explicitly encourages activations in the saturated region(s) of the corresponding activation function. We call these Saturating Auto-Encoders (SATAE). We... | Saturating Auto-Encoders
Rostislav Goroshin∗
Courant Institute of Mathematical Science
New York University
goroshin@cs.nyu.edu
Yann LeCun
Courant Institute of Mathematical Science
New York University
yann@cs.nyu.edu
Abstract
We introduce a simple new regularizer for auto-encoders whose hidden-unit ac-
tivation function... | Ross Goroshin, Yann LeCun | Unknown | 2,013 | {"id": "yGgjGkkbeFSbt", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358468100000, "tmdate": 1358468100000, "ddate": null, "number": 39, "content": {"title": "Saturating Auto-Encoder", "decision": "conferencePoster-iclr2013-conference", "abstract": "We introduce a simple new ... | [Review]:
The revised paper is now available on arXiv. | Ross Goroshin | null | null | {"id": "UNlcNgK7BCN9v", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363840020000, "tmdate": 1363840020000, "ddate": null, "number": 6, "content": {"title": "", "review": "The revised paper is now available on arXiv."}, "forum": "yGgjGkkbeFSbt", "referent": null, "invitation"... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0,
"total": 1
} | 0 | -2.27267 | 2.27267 | 0.00082 | 0.007108 | 0.00082 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0
} | 0 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
yGgjGkkbeFSbt | Saturating Auto-Encoder | We introduce a simple new regularizer for auto-encoders whose hidden-unit activation functions contain at least one zero-gradient (saturated) region. This regularizer explicitly encourages activations in the saturated region(s) of the corresponding activation function. We call these Saturating Auto-Encoders (SATAE). We... | Saturating Auto-Encoders
Rostislav Goroshin∗
Courant Institute of Mathematical Science
New York University
goroshin@cs.nyu.edu
Yann LeCun
Courant Institute of Mathematical Science
New York University
yann@cs.nyu.edu
Abstract
We introduce a simple new regularizer for auto-encoders whose hidden-unit ac-
tivation function... | Ross Goroshin, Yann LeCun | Unknown | 2,013 | {"id": "yGgjGkkbeFSbt", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358468100000, "tmdate": 1358468100000, "ddate": null, "number": 39, "content": {"title": "Saturating Auto-Encoder", "decision": "conferencePoster-iclr2013-conference", "abstract": "We introduce a simple new ... | [Review]:
This paper proposes a novel kind of penalty for regularizing autoencoder training, that encourages activations to move towards flat (saturated) regions of the unit's activation function. It is related to sparse autoencoders and contractive autoencoders that also happen to encourage saturation. But the propose... | anonymous reviewer 5955 | null | null | {"id": "NNd3mgfs39NaH", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362361200000, "tmdate": 1362361200000, "ddate": null, "number": 4, "content": {"title": "review of Saturating Auto-Encoder", "review": "This paper proposes a novel kind of penalty for regularizing autoencode... | {
"criticism": 1,
"example": 0,
"importance_and_relevance": 4,
"materials_and_methods": 6,
"praise": 2,
"presentation_and_reporting": 1,
"results_and_discussion": 4,
"suggestion_and_solution": 2,
"total": 11
} | 1.818182 | 1.755052 | 0.06313 | 1.843334 | 0.299886 | 0.025152 | 0.090909 | 0 | 0.363636 | 0.545455 | 0.181818 | 0.090909 | 0.363636 | 0.181818 | {
"criticism": 0.09090909090909091,
"example": 0,
"importance_and_relevance": 0.36363636363636365,
"materials_and_methods": 0.5454545454545454,
"praise": 0.18181818181818182,
"presentation_and_reporting": 0.09090909090909091,
"results_and_discussion": 0.36363636363636365,
"suggestion_and_solution": 0.18... | 1.818182 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
yGgjGkkbeFSbt | Saturating Auto-Encoder | We introduce a simple new regularizer for auto-encoders whose hidden-unit activation functions contain at least one zero-gradient (saturated) region. This regularizer explicitly encourages activations in the saturated region(s) of the corresponding activation function. We call these Saturating Auto-Encoders (SATAE). We... | Saturating Auto-Encoders
Rostislav Goroshin∗
Courant Institute of Mathematical Science
New York University
goroshin@cs.nyu.edu
Yann LeCun
Courant Institute of Mathematical Science
New York University
yann@cs.nyu.edu
Abstract
We introduce a simple new regularizer for auto-encoders whose hidden-unit ac-
tivation function... | Ross Goroshin, Yann LeCun | Unknown | 2,013 | {"id": "yGgjGkkbeFSbt", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358468100000, "tmdate": 1358468100000, "ddate": null, "number": 39, "content": {"title": "Saturating Auto-Encoder", "decision": "conferencePoster-iclr2013-conference", "abstract": "We introduce a simple new ... | [Review]:
This paper proposes a regularizer for auto-encoders with
nonlinearities that have a zegion with zero-gradient. The paper
mentions three nonlinearities that fit into that category: shrinkage,
saturated linear, rectified linear.
The regularizer basically penalizes how much the activation deviates
from sa... | anonymous reviewer 3942 | null | null | {"id": "BSYbBsx9_5Suw", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1361946900000, "tmdate": 1361946900000, "ddate": null, "number": 3, "content": {"title": "review of Saturating Auto-Encoder", "review": "This paper proposes a regularizer for auto-encoders with\r\nnonlinearit... | {
"criticism": 1,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 8,
"praise": 0,
"presentation_and_reporting": 1,
"results_and_discussion": 2,
"suggestion_and_solution": 5,
"total": 12
} | 1.5 | 1.484218 | 0.015782 | 1.526436 | 0.315033 | 0.026436 | 0.083333 | 0 | 0.083333 | 0.666667 | 0 | 0.083333 | 0.166667 | 0.416667 | {
"criticism": 0.08333333333333333,
"example": 0,
"importance_and_relevance": 0.08333333333333333,
"materials_and_methods": 0.6666666666666666,
"praise": 0,
"presentation_and_reporting": 0.08333333333333333,
"results_and_discussion": 0.16666666666666666,
"suggestion_and_solution": 0.4166666666666667
} | 1.5 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
ttxM6DQKghdOi | Discrete Restricted Boltzmann Machines | In this paper we describe discrete restricted Boltzmann machines: graphical probability models with bipartite interactions between discrete visible and hidden variables. These models generalize standard binary restricted Boltzmann machines and discrete na'ive Bayes models. For a given number of visible variables and ca... | Discrete Restricted Boltzmann Machines
Guido Mont´ufar GFM 10@ PSU .EDU
Jason Morton MORTON @MATH .PSU .EDU
Department of Mathematics
Pennsylvania State University
University Park, PA 16802, USA
Abstract
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipar-
tite interactions bet... | Guido F. Montufar, Jason Morton | Unknown | 2,013 | {"id": "ttxM6DQKghdOi", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358411400000, "tmdate": 1358411400000, "ddate": null, "number": 59, "content": {"title": "Discrete Restricted Boltzmann Machines", "decision": "conferenceOral-iclr2013-conference", "abstract": "In this paper... | [Review]:
We appreciate the comments of all three reviewers. We posted a revised version of the paper to the arxiv (scheduled to be announced March 18 2013).
While reviewer 1922 found the paper ``comprehensive'' and ``clearly written'', reviewers e437 and fce0 were very concerned with the presentation of the paper,... | Guido F. Montufar, Jason Morton | null | null | {"id": "uc6XK8UgDGKmi", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363572060000, "tmdate": 1363572060000, "ddate": null, "number": 2, "content": {"title": "", "review": "We appreciate the comments of all three reviewers. We posted a revised version of the paper to the arxiv... | {
"criticism": 2,
"example": 1,
"importance_and_relevance": 2,
"materials_and_methods": 0,
"praise": 2,
"presentation_and_reporting": 2,
"results_and_discussion": 4,
"suggestion_and_solution": 4,
"total": 12
} | 1.416667 | 1.400884 | 0.015782 | 1.430652 | 0.202083 | 0.013985 | 0.166667 | 0.083333 | 0.166667 | 0 | 0.166667 | 0.166667 | 0.333333 | 0.333333 | {
"criticism": 0.16666666666666666,
"example": 0.08333333333333333,
"importance_and_relevance": 0.16666666666666666,
"materials_and_methods": 0,
"praise": 0.16666666666666666,
"presentation_and_reporting": 0.16666666666666666,
"results_and_discussion": 0.3333333333333333,
"suggestion_and_solution": 0.33... | 1.416667 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
ttxM6DQKghdOi | Discrete Restricted Boltzmann Machines | In this paper we describe discrete restricted Boltzmann machines: graphical probability models with bipartite interactions between discrete visible and hidden variables. These models generalize standard binary restricted Boltzmann machines and discrete na'ive Bayes models. For a given number of visible variables and ca... | Discrete Restricted Boltzmann Machines
Guido Mont´ufar GFM 10@ PSU .EDU
Jason Morton MORTON @MATH .PSU .EDU
Department of Mathematics
Pennsylvania State University
University Park, PA 16802, USA
Abstract
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipar-
tite interactions bet... | Guido F. Montufar, Jason Morton | Unknown | 2,013 | {"id": "ttxM6DQKghdOi", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358411400000, "tmdate": 1358411400000, "ddate": null, "number": 59, "content": {"title": "Discrete Restricted Boltzmann Machines", "decision": "conferenceOral-iclr2013-conference", "abstract": "In this paper... | [Review]:
The paper provides a theoretical analysis of Restricted Boltzmann Machines with multivalued discrete units, with the emphasis on representation capacity of such models.
Discrete RBMs are a special case of exponential family harmoniums introduced by Welling et al. [1] and have been known under the name of m... | anonymous reviewer e437 | null | null | {"id": "gE0uE2A98H59Y", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1360957080000, "tmdate": 1360957080000, "ddate": null, "number": 3, "content": {"title": "review of Discrete Restricted Boltzmann Machines", "review": "The paper provides a theoretical analysis of Restricted ... | {
"criticism": 7,
"example": 0,
"importance_and_relevance": 4,
"materials_and_methods": 3,
"praise": 2,
"presentation_and_reporting": 3,
"results_and_discussion": 3,
"suggestion_and_solution": 2,
"total": 15
} | 1.6 | 1.53687 | 0.06313 | 1.616254 | 0.203588 | 0.016254 | 0.466667 | 0 | 0.266667 | 0.2 | 0.133333 | 0.2 | 0.2 | 0.133333 | {
"criticism": 0.4666666666666667,
"example": 0,
"importance_and_relevance": 0.26666666666666666,
"materials_and_methods": 0.2,
"praise": 0.13333333333333333,
"presentation_and_reporting": 0.2,
"results_and_discussion": 0.2,
"suggestion_and_solution": 0.13333333333333333
} | 1.6 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
ttxM6DQKghdOi | Discrete Restricted Boltzmann Machines | In this paper we describe discrete restricted Boltzmann machines: graphical probability models with bipartite interactions between discrete visible and hidden variables. These models generalize standard binary restricted Boltzmann machines and discrete na'ive Bayes models. For a given number of visible variables and ca... | Discrete Restricted Boltzmann Machines
Guido Mont´ufar GFM 10@ PSU .EDU
Jason Morton MORTON @MATH .PSU .EDU
Department of Mathematics
Pennsylvania State University
University Park, PA 16802, USA
Abstract
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipar-
tite interactions bet... | Guido F. Montufar, Jason Morton | Unknown | 2,013 | {"id": "ttxM6DQKghdOi", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358411400000, "tmdate": 1358411400000, "ddate": null, "number": 59, "content": {"title": "Discrete Restricted Boltzmann Machines", "decision": "conferenceOral-iclr2013-conference", "abstract": "In this paper... | [Review]:
To the reviewers of this paper,
There appear to be some disagreement of the utility of the contributions of this paper to a machine learning audience.
Please read over the comments of the other reviewers and submit comment as you see fit. | Aaron Courville | null | null | {"id": "_YRe0x39e7YBa", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363534860000, "tmdate": 1363534860000, "ddate": null, "number": 1, "content": {"title": "", "review": "To the reviewers of this paper,\r\n\r\nThere appear to be some disagreement of the utility of the contri... | {
"criticism": 1,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 1,
"total": 2
} | 1.5 | -0.409674 | 1.909674 | 1.502109 | 0.158289 | 0.002109 | 0.5 | 0 | 0.5 | 0 | 0 | 0 | 0 | 0.5 | {
"criticism": 0.5,
"example": 0,
"importance_and_relevance": 0.5,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0.5
} | 1.5 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
ttxM6DQKghdOi | Discrete Restricted Boltzmann Machines | In this paper we describe discrete restricted Boltzmann machines: graphical probability models with bipartite interactions between discrete visible and hidden variables. These models generalize standard binary restricted Boltzmann machines and discrete na'ive Bayes models. For a given number of visible variables and ca... | Discrete Restricted Boltzmann Machines
Guido Mont´ufar GFM 10@ PSU .EDU
Jason Morton MORTON @MATH .PSU .EDU
Department of Mathematics
Pennsylvania State University
University Park, PA 16802, USA
Abstract
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipar-
tite interactions bet... | Guido F. Montufar, Jason Morton | Unknown | 2,013 | {"id": "ttxM6DQKghdOi", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358411400000, "tmdate": 1358411400000, "ddate": null, "number": 59, "content": {"title": "Discrete Restricted Boltzmann Machines", "decision": "conferenceOral-iclr2013-conference", "abstract": "In this paper... | [Review]:
This paper reviews properties of the Naive Bayes models and Binary RBMs before moving on to introducing discrete RBMs for which they extend universal approximation and other properties.
I think such a review and extensions are extremely interesting for the more theoretical fields such as algebraical geomet... | anonymous reviewer fce0 | null | null | {"id": "AAvOd8oYsZAh8", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362487980000, "tmdate": 1362487980000, "ddate": null, "number": 4, "content": {"title": "review of Discrete Restricted Boltzmann Machines", "review": "This paper reviews properties of the Naive Bayes models ... | {
"criticism": 2,
"example": 1,
"importance_and_relevance": 2,
"materials_and_methods": 1,
"praise": 3,
"presentation_and_reporting": 2,
"results_and_discussion": 2,
"suggestion_and_solution": 2,
"total": 8
} | 1.875 | 1.480439 | 0.394561 | 1.884088 | 0.160046 | 0.009088 | 0.25 | 0.125 | 0.25 | 0.125 | 0.375 | 0.25 | 0.25 | 0.25 | {
"criticism": 0.25,
"example": 0.125,
"importance_and_relevance": 0.25,
"materials_and_methods": 0.125,
"praise": 0.375,
"presentation_and_reporting": 0.25,
"results_and_discussion": 0.25,
"suggestion_and_solution": 0.25
} | 1.875 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
ttxM6DQKghdOi | Discrete Restricted Boltzmann Machines | In this paper we describe discrete restricted Boltzmann machines: graphical probability models with bipartite interactions between discrete visible and hidden variables. These models generalize standard binary restricted Boltzmann machines and discrete na'ive Bayes models. For a given number of visible variables and ca... | Discrete Restricted Boltzmann Machines
Guido Mont´ufar GFM 10@ PSU .EDU
Jason Morton MORTON @MATH .PSU .EDU
Department of Mathematics
Pennsylvania State University
University Park, PA 16802, USA
Abstract
We describe discrete restricted Boltzmann machines: probabilistic graphical models with bipar-
tite interactions bet... | Guido F. Montufar, Jason Morton | Unknown | 2,013 | {"id": "ttxM6DQKghdOi", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358411400000, "tmdate": 1358411400000, "ddate": null, "number": 59, "content": {"title": "Discrete Restricted Boltzmann Machines", "decision": "conferenceOral-iclr2013-conference", "abstract": "In this paper... | [Review]:
This paper presents a comprehensive theoretical discussion on the approximation properties of discrete restricted Boltzmann machines. The paper is clearly written. It provides a contextual introduction to the theoretical results by reviewing approximation results for Naive Bayes models and binary restricted B... | anonymous reviewer 1922 | null | null | {"id": "86Fqwo3AqRw0s", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362471060000, "tmdate": 1362471060000, "ddate": null, "number": 5, "content": {"title": "review of Discrete Restricted Boltzmann Machines", "review": "This paper presents a comprehensive theoretical discussi... | {
"criticism": 2,
"example": 1,
"importance_and_relevance": 2,
"materials_and_methods": 13,
"praise": 6,
"presentation_and_reporting": 3,
"results_and_discussion": 10,
"suggestion_and_solution": 3,
"total": 36
} | 1.111111 | -7.237794 | 8.348905 | 1.135552 | 0.241071 | 0.024441 | 0.055556 | 0.027778 | 0.055556 | 0.361111 | 0.166667 | 0.083333 | 0.277778 | 0.083333 | {
"criticism": 0.05555555555555555,
"example": 0.027777777777777776,
"importance_and_relevance": 0.05555555555555555,
"materials_and_methods": 0.3611111111111111,
"praise": 0.16666666666666666,
"presentation_and_reporting": 0.08333333333333333,
"results_and_discussion": 0.2777777777777778,
"suggestion_a... | 1.111111 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
ttnAE7vaATtaK | Indoor Semantic Segmentation using depth information | This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on hand-crafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. ... | Indoor Semantic Segmentation
using depth information
Camille Couprie1∗ Cl´ement Farabet2,3 Laurent Najman3 Yann LeCun2
1 IFP Energies Nouvelles
Technology, Computer Science and Applied Mathematics Division
Rueil Malmaison, France
2 Courant Institute of Mathematical Sciences
New York University
New York, NY 10003, USA
3... | Camille Couprie, Clement Farabet, Laurent Najman, Yann LeCun | Unknown | 2,013 | {"id": "ttnAE7vaATtaK", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358409600000, "tmdate": 1358409600000, "ddate": null, "number": 40, "content": {"title": "Indoor Semantic Segmentation using depth information", "decision": "conferenceOral-iclr2013-conference", "abstract": ... | [Review]:
Segmentation with multi-scale max pooling CNN, applied to indoor vision, using depth information. Interesting paper! Fine results.
Question: how does that compare to multi-scale max pooling CNN for a previous award-winning application, namely, segmentation of neuronal membranes (Ciresan et al, NIPS 2012)? | anonymous reviewer 777f | null | null | {"id": "qO9gWZZ1gfqhl", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362163380000, "tmdate": 1362163380000, "ddate": null, "number": 1, "content": {"title": "review of Indoor Semantic Segmentation using depth information", "review": "Segmentation with multi-scale max pooling ... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 2,
"praise": 2,
"presentation_and_reporting": 0,
"results_and_discussion": 1,
"suggestion_and_solution": 0,
"total": 4
} | 1.5 | 0.221623 | 1.278377 | 1.507743 | 0.107135 | 0.007743 | 0 | 0 | 0.25 | 0.5 | 0.5 | 0 | 0.25 | 0 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0.25,
"materials_and_methods": 0.5,
"praise": 0.5,
"presentation_and_reporting": 0,
"results_and_discussion": 0.25,
"suggestion_and_solution": 0
} | 1.5 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
ttnAE7vaATtaK | Indoor Semantic Segmentation using depth information | This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on hand-crafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. ... | Indoor Semantic Segmentation
using depth information
Camille Couprie1∗ Cl´ement Farabet2,3 Laurent Najman3 Yann LeCun2
1 IFP Energies Nouvelles
Technology, Computer Science and Applied Mathematics Division
Rueil Malmaison, France
2 Courant Institute of Mathematical Sciences
New York University
New York, NY 10003, USA
3... | Camille Couprie, Clement Farabet, Laurent Najman, Yann LeCun | Unknown | 2,013 | {"id": "ttnAE7vaATtaK", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358409600000, "tmdate": 1358409600000, "ddate": null, "number": 40, "content": {"title": "Indoor Semantic Segmentation using depth information", "decision": "conferenceOral-iclr2013-conference", "abstract": ... | [Review]:
This work builds on recent object-segmentation work by Farabet et al., by augmenting the pixel-processing pathways with ones that processes a depth map from a Kinect RGBD camera. This work seems to me a well-motivated and natural extension now that RGBD sensors are readily available.
The incremental value ... | anonymous reviewer 5193 | null | null | {"id": "Ub0AUfEOKkRO1", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362368040000, "tmdate": 1362368040000, "ddate": null, "number": 3, "content": {"title": "review of Indoor Semantic Segmentation using depth information", "review": "This work builds on recent object-segmenta... | {
"criticism": 3,
"example": 3,
"importance_and_relevance": 1,
"materials_and_methods": 7,
"praise": 2,
"presentation_and_reporting": 1,
"results_and_discussion": 3,
"suggestion_and_solution": 5,
"total": 13
} | 1.923077 | 1.923077 | 0 | 1.954307 | 0.355382 | 0.03123 | 0.230769 | 0.230769 | 0.076923 | 0.538462 | 0.153846 | 0.076923 | 0.230769 | 0.384615 | {
"criticism": 0.23076923076923078,
"example": 0.23076923076923078,
"importance_and_relevance": 0.07692307692307693,
"materials_and_methods": 0.5384615384615384,
"praise": 0.15384615384615385,
"presentation_and_reporting": 0.07692307692307693,
"results_and_discussion": 0.23076923076923078,
"suggestion_a... | 1.923077 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
ttnAE7vaATtaK | Indoor Semantic Segmentation using depth information | This work addresses multi-class segmentation of indoor scenes with RGB-D inputs. While this area of research has gained much attention recently, most works still rely on hand-crafted features. In contrast, we apply a multiscale convolutional network to learn features directly from the images and the depth information. ... | Indoor Semantic Segmentation
using depth information
Camille Couprie1∗ Cl´ement Farabet2,3 Laurent Najman3 Yann LeCun2
1 IFP Energies Nouvelles
Technology, Computer Science and Applied Mathematics Division
Rueil Malmaison, France
2 Courant Institute of Mathematical Sciences
New York University
New York, NY 10003, USA
3... | Camille Couprie, Clement Farabet, Laurent Najman, Yann LeCun | Unknown | 2,013 | {"id": "ttnAE7vaATtaK", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358409600000, "tmdate": 1358409600000, "ddate": null, "number": 40, "content": {"title": "Indoor Semantic Segmentation using depth information", "decision": "conferenceOral-iclr2013-conference", "abstract": ... | [Review]:
This work applies convolutional neural networks to the task of RGB-D indoor scene segmentation. The authors previously evaulated the same multi-scale conv net architecture on the data using only RGB information, this work demonstrates that for most segmentation classes providing depth information to the conv ... | anonymous reviewer 03ba | null | null | {"id": "2-VeRGGdvD-58", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362213660000, "tmdate": 1362213660000, "ddate": null, "number": 2, "content": {"title": "review of Indoor Semantic Segmentation using depth information", "review": "This work applies convolutional neural net... | {
"criticism": 1,
"example": 2,
"importance_and_relevance": 1,
"materials_and_methods": 9,
"praise": 2,
"presentation_and_reporting": 1,
"results_and_discussion": 2,
"suggestion_and_solution": 4,
"total": 12
} | 1.833333 | 1.817551 | 0.015782 | 1.862878 | 0.332823 | 0.029545 | 0.083333 | 0.166667 | 0.083333 | 0.75 | 0.166667 | 0.083333 | 0.166667 | 0.333333 | {
"criticism": 0.08333333333333333,
"example": 0.16666666666666666,
"importance_and_relevance": 0.08333333333333333,
"materials_and_methods": 0.75,
"praise": 0.16666666666666666,
"presentation_and_reporting": 0.08333333333333333,
"results_and_discussion": 0.16666666666666666,
"suggestion_and_solution": ... | 1.833333 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
tFbuFKWX3MFC8 | Training Neural Networks with Stochastic Hessian-Free Optimization | Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property a... | arXiv:1301.3641v3 [cs.LG] 1 May 2013
T raining Neural Networks with Stochastic
Hessian-Free Optimization
Ryan Kiros
Department of Computing Science
University of Alberta
Edmonton, AB, Canada
rkiros@ualberta.ca
Abstract
Hessian-free (HF) optimization has been successfully used for training deep au-
toencoders and recu... | Ryan Kiros | Unknown | 2,013 | {"id": "tFbuFKWX3MFC8", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358410500000, "tmdate": 1358410500000, "ddate": null, "number": 48, "content": {"title": "Training Neural Networks with Stochastic Hessian-Free Optimization", "decision": "conferencePoster-iclr2013-conferenc... | [Review]:
I want to say thanks again to the conference organizers, reviewers and openreview.net developers for doing a great job.
I have updated the code on my webpage to include two additional features: max norm weight clipping and training deep autoencoders. Autoencoder training uses symmetric encoding / decoding ... | Ryan Kiros | null | null | {"id": "nYshYtAXG48ze", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1364786880000, "tmdate": 1364786880000, "ddate": null, "number": 1, "content": {"title": "", "review": "I want to say thanks again to the conference organizers, reviewers and openreview.net developers for doi... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 2,
"praise": 1,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 1,
"total": 3
} | 1.666667 | 0.088424 | 1.578243 | 1.671209 | 0.139383 | 0.004542 | 0 | 0 | 0.333333 | 0.666667 | 0.333333 | 0 | 0 | 0.333333 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0.3333333333333333,
"materials_and_methods": 0.6666666666666666,
"praise": 0.3333333333333333,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0.3333333333333333
} | 1.666667 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
tFbuFKWX3MFC8 | Training Neural Networks with Stochastic Hessian-Free Optimization | Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property a... | arXiv:1301.3641v3 [cs.LG] 1 May 2013
T raining Neural Networks with Stochastic
Hessian-Free Optimization
Ryan Kiros
Department of Computing Science
University of Alberta
Edmonton, AB, Canada
rkiros@ualberta.ca
Abstract
Hessian-free (HF) optimization has been successfully used for training deep au-
toencoders and recu... | Ryan Kiros | Unknown | 2,013 | {"id": "tFbuFKWX3MFC8", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358410500000, "tmdate": 1358410500000, "ddate": null, "number": 48, "content": {"title": "Training Neural Networks with Stochastic Hessian-Free Optimization", "decision": "conferencePoster-iclr2013-conferenc... | [Review]:
I have submitted an updated version to arxiv and should appear shortly. My apologies for the delay. From the suggestion of reviewer 0a71 I've renamed the paper to 'Training Neural Networks with Dropout Stochastic Hessian-Free Optimization'. | Ryan Kiros | null | null | {"id": "mm_3mNH6nD4hc", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363601400000, "tmdate": 1363601400000, "ddate": null, "number": 4, "content": {"title": "", "review": "I have submitted an updated version to arxiv and should appear shortly. My apologies for the delay. From... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 1,
"total": 3
} | 0.333333 | -1.24491 | 1.578243 | 0.33576 | 0.074372 | 0.002427 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.333333 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0.3333333333333333
} | 0.333333 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
tFbuFKWX3MFC8 | Training Neural Networks with Stochastic Hessian-Free Optimization | Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property a... | arXiv:1301.3641v3 [cs.LG] 1 May 2013
T raining Neural Networks with Stochastic
Hessian-Free Optimization
Ryan Kiros
Department of Computing Science
University of Alberta
Edmonton, AB, Canada
rkiros@ualberta.ca
Abstract
Hessian-free (HF) optimization has been successfully used for training deep au-
toencoders and recu... | Ryan Kiros | Unknown | 2,013 | {"id": "tFbuFKWX3MFC8", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358410500000, "tmdate": 1358410500000, "ddate": null, "number": 48, "content": {"title": "Training Neural Networks with Stochastic Hessian-Free Optimization", "decision": "conferencePoster-iclr2013-conferenc... | [Review]:
Dear reviewers,
To better account for the mentioned weaknesses of the paper, I've re-implemented SHF with GPU compatibility and evaluated the algorithm on the CURVES and MNIST deep autoencoder tasks. I'm using the same setup as in Chapter 7 of Ilya Sutskever's PhD thesis, which allows for comparison agains... | Ryan Kiros | null | null | {"id": "lcfIcbYPqX3P7", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1367022720000, "tmdate": 1367022720000, "ddate": null, "number": 5, "content": {"title": "", "review": "Dear reviewers,\r\n\r\nTo better account for the mentioned weaknesses of the paper, I've re-implemented ... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 2,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 1,
"suggestion_and_solution": 1,
"total": 3
} | 1.333333 | -0.24491 | 1.578243 | 1.339503 | 0.116827 | 0.00617 | 0 | 0 | 0 | 0.666667 | 0 | 0 | 0.333333 | 0.333333 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0.6666666666666666,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0.3333333333333333,
"suggestion_and_solution": 0.3333333333333333
} | 1.333333 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
tFbuFKWX3MFC8 | Training Neural Networks with Stochastic Hessian-Free Optimization | Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property a... | arXiv:1301.3641v3 [cs.LG] 1 May 2013
T raining Neural Networks with Stochastic
Hessian-Free Optimization
Ryan Kiros
Department of Computing Science
University of Alberta
Edmonton, AB, Canada
rkiros@ualberta.ca
Abstract
Hessian-free (HF) optimization has been successfully used for training deep au-
toencoders and recu... | Ryan Kiros | Unknown | 2,013 | {"id": "tFbuFKWX3MFC8", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358410500000, "tmdate": 1358410500000, "ddate": null, "number": 48, "content": {"title": "Training Neural Networks with Stochastic Hessian-Free Optimization", "decision": "conferencePoster-iclr2013-conferenc... | [Review]:
Summary and general overview:
----------------------------------------------
The paper tries to explore an online regime for Hessian Free as well as using drop outs. The new method is called Stochastic Hessian Free and is tested on a few datasets (MNIST, USPS and Reuters).
The approach is interesting and ... | anonymous reviewer 0a71 | null | null | {"id": "gehZgYtw_1v8S", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362161760000, "tmdate": 1362161760000, "ddate": null, "number": 6, "content": {"title": "review of Training Neural Networks with Stochastic Hessian-Free Optimization", "review": "Summary and general overview... | {
"criticism": 10,
"example": 10,
"importance_and_relevance": 1,
"materials_and_methods": 16,
"praise": 2,
"presentation_and_reporting": 14,
"results_and_discussion": 9,
"suggestion_and_solution": 10,
"total": 48
} | 1.5 | -17.833476 | 19.333476 | 1.557268 | 0.544843 | 0.057268 | 0.208333 | 0.208333 | 0.020833 | 0.333333 | 0.041667 | 0.291667 | 0.1875 | 0.208333 | {
"criticism": 0.20833333333333334,
"example": 0.20833333333333334,
"importance_and_relevance": 0.020833333333333332,
"materials_and_methods": 0.3333333333333333,
"praise": 0.041666666666666664,
"presentation_and_reporting": 0.2916666666666667,
"results_and_discussion": 0.1875,
"suggestion_and_solution"... | 1.5 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
tFbuFKWX3MFC8 | Training Neural Networks with Stochastic Hessian-Free Optimization | Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property a... | arXiv:1301.3641v3 [cs.LG] 1 May 2013
T raining Neural Networks with Stochastic
Hessian-Free Optimization
Ryan Kiros
Department of Computing Science
University of Alberta
Edmonton, AB, Canada
rkiros@ualberta.ca
Abstract
Hessian-free (HF) optimization has been successfully used for training deep au-
toencoders and recu... | Ryan Kiros | Unknown | 2,013 | {"id": "tFbuFKWX3MFC8", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358410500000, "tmdate": 1358410500000, "ddate": null, "number": 48, "content": {"title": "Training Neural Networks with Stochastic Hessian-Free Optimization", "decision": "conferencePoster-iclr2013-conferenc... | [Review]:
Thank you for your comments!
To Anonymous 0a71:
---------------------------------
(1,8): I agree. Indeed, it is straightforward to add an additional experiment without the use of dropout. At the least, the experimental section can be modified to indicate whether the method is using dropout or not inst... | Ryan Kiros | null | null | {"id": "av7x0igQwD0M-", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362494640000, "tmdate": 1362494640000, "ddate": null, "number": 8, "content": {"title": "", "review": "Thank you for your comments! \r\n\r\nTo Anonymous 0a71:\r\n---------------------------------\r\n\r\n(1,8... | {
"criticism": 8,
"example": 10,
"importance_and_relevance": 4,
"materials_and_methods": 35,
"praise": 3,
"presentation_and_reporting": 13,
"results_and_discussion": 15,
"suggestion_and_solution": 15,
"total": 66
} | 1.560606 | -42.772239 | 44.332845 | 1.653209 | 0.858625 | 0.092603 | 0.121212 | 0.151515 | 0.060606 | 0.530303 | 0.045455 | 0.19697 | 0.227273 | 0.227273 | {
"criticism": 0.12121212121212122,
"example": 0.15151515151515152,
"importance_and_relevance": 0.06060606060606061,
"materials_and_methods": 0.5303030303030303,
"praise": 0.045454545454545456,
"presentation_and_reporting": 0.19696969696969696,
"results_and_discussion": 0.22727272727272727,
"suggestion_... | 1.560606 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
tFbuFKWX3MFC8 | Training Neural Networks with Stochastic Hessian-Free Optimization | Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property a... | arXiv:1301.3641v3 [cs.LG] 1 May 2013
T raining Neural Networks with Stochastic
Hessian-Free Optimization
Ryan Kiros
Department of Computing Science
University of Alberta
Edmonton, AB, Canada
rkiros@ualberta.ca
Abstract
Hessian-free (HF) optimization has been successfully used for training deep au-
toencoders and recu... | Ryan Kiros | Unknown | 2,013 | {"id": "tFbuFKWX3MFC8", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358410500000, "tmdate": 1358410500000, "ddate": null, "number": 48, "content": {"title": "Training Neural Networks with Stochastic Hessian-Free Optimization", "decision": "conferencePoster-iclr2013-conferenc... | [Review]:
This paper makes an attempt at extending the Hessian-free learning work to a stochastic setting. In a nutshell, the changes are:
- shorter CG runs
- cleverer information sharing across CG runs that has an annealing effect
- using differently-sized mini-batches for gradient and curvature estimation (form... | anonymous reviewer 4709 | null | null | {"id": "UJZtu0oLtcJh1", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362391800000, "tmdate": 1362391800000, "ddate": null, "number": 2, "content": {"title": "review of Training Neural Networks with Stochastic Hessian-Free Optimization", "review": "This paper makes an attempt ... | {
"criticism": 4,
"example": 0,
"importance_and_relevance": 3,
"materials_and_methods": 6,
"praise": 2,
"presentation_and_reporting": 0,
"results_and_discussion": 4,
"suggestion_and_solution": 1,
"total": 10
} | 2 | 1.857958 | 0.142042 | 2.019264 | 0.232016 | 0.019264 | 0.4 | 0 | 0.3 | 0.6 | 0.2 | 0 | 0.4 | 0.1 | {
"criticism": 0.4,
"example": 0,
"importance_and_relevance": 0.3,
"materials_and_methods": 0.6,
"praise": 0.2,
"presentation_and_reporting": 0,
"results_and_discussion": 0.4,
"suggestion_and_solution": 0.1
} | 2 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
tFbuFKWX3MFC8 | Training Neural Networks with Stochastic Hessian-Free Optimization | Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property a... | arXiv:1301.3641v3 [cs.LG] 1 May 2013
T raining Neural Networks with Stochastic
Hessian-Free Optimization
Ryan Kiros
Department of Computing Science
University of Alberta
Edmonton, AB, Canada
rkiros@ualberta.ca
Abstract
Hessian-free (HF) optimization has been successfully used for training deep au-
toencoders and recu... | Ryan Kiros | Unknown | 2,013 | {"id": "tFbuFKWX3MFC8", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358410500000, "tmdate": 1358410500000, "ddate": null, "number": 48, "content": {"title": "Training Neural Networks with Stochastic Hessian-Free Optimization", "decision": "conferencePoster-iclr2013-conferenc... | [Review]:
This paper looks at designing an SGD-like version of the 'Hessian-free' (HF) optimization approach which is applied to training shallow to moderately deep neural nets for classification tasks. The approach consists of the usual HF algorithm, but with smaller minibatches and with CG terminated after only 3-5 ... | anonymous reviewer f834 | null | null | {"id": "TF3miswPCQiau", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362400260000, "tmdate": 1362400260000, "ddate": null, "number": 3, "content": {"title": "review of Training Neural Networks with Stochastic Hessian-Free Optimization", "review": "This paper looks at designin... | {
"criticism": 11,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 22,
"praise": 0,
"presentation_and_reporting": 6,
"results_and_discussion": 13,
"suggestion_and_solution": 6,
"total": 30
} | 1.966667 | -2.594456 | 4.561122 | 2.020166 | 0.51684 | 0.053499 | 0.366667 | 0 | 0.033333 | 0.733333 | 0 | 0.2 | 0.433333 | 0.2 | {
"criticism": 0.36666666666666664,
"example": 0,
"importance_and_relevance": 0.03333333333333333,
"materials_and_methods": 0.7333333333333333,
"praise": 0,
"presentation_and_reporting": 0.2,
"results_and_discussion": 0.43333333333333335,
"suggestion_and_solution": 0.2
} | 1.966667 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
tFbuFKWX3MFC8 | Training Neural Networks with Stochastic Hessian-Free Optimization | Hessian-free (HF) optimization has been successfully used for training deep autoencoders and recurrent networks. HF uses the conjugate gradient algorithm to construct update directions through curvature-vector products that can be computed on the same order of time as gradients. In this paper we exploit this property a... | arXiv:1301.3641v3 [cs.LG] 1 May 2013
T raining Neural Networks with Stochastic
Hessian-Free Optimization
Ryan Kiros
Department of Computing Science
University of Alberta
Edmonton, AB, Canada
rkiros@ualberta.ca
Abstract
Hessian-free (HF) optimization has been successfully used for training deep au-
toencoders and recu... | Ryan Kiros | Unknown | 2,013 | {"id": "tFbuFKWX3MFC8", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358410500000, "tmdate": 1358410500000, "ddate": null, "number": 48, "content": {"title": "Training Neural Networks with Stochastic Hessian-Free Optimization", "decision": "conferencePoster-iclr2013-conferenc... | [Review]:
Code is now available: http://www.ualberta.ca/~rkiros/
Included are scripts to reproduce the results in the paper. | Ryan Kiros | null | null | {"id": "CUXbqkRcJWqcy", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1360514640000, "tmdate": 1360514640000, "ddate": null, "number": 7, "content": {"title": "", "review": "Code is now available: http://www.ualberta.ca/~rkiros/\r\n\r\nIncluded are scripts to reproduce the resu... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 1,
"suggestion_and_solution": 0,
"total": 1
} | 1 | -1.27267 | 2.27267 | 1.00125 | 0.020842 | 0.00125 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 1,
"suggestion_and_solution": 0
} | 1 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
rtGYtZ-ZKSMzk | Tree structured sparse coding on cubes | A brief description of tree structured sparse coding on the binary cube. | arXiv:1301.3590v1 [cs.IT] 16 Jan 2013
Tree structured sparse coding on cubes
Arthur Szlam
City College of New York
aszlam@ccny.cuny.edu
Several recent works have discussed tree structured sparsecoding [8, 10, 7, 3], where N data points
in Rd written as the d × N matrix X are approximately decomposed into the product ... | Arthur Szlam | Unknown | 2,013 | {"id": "rtGYtZ-ZKSMzk", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358405100000, "tmdate": 1358405100000, "ddate": null, "number": 7, "content": {"title": "Tree structured sparse coding on cubes", "decision": "conferencePoster-iclr2013-workshop", "abstract": "A brief descri... | [Review]:
The paper extends the widely known idea of tree-structured sparse coding to the Hamming space. Instead for each node being represented by the best linear fit of the corresponding sub-space, it is represented by the best sub-cube. The idea is valid if not extremely original.
I’m not sure it has too many ap... | anonymous reviewer fd41 | null | null | {"id": "axSGN5lBGINJm", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362831180000, "tmdate": 1362831180000, "ddate": null, "number": 1, "content": {"title": "review of Tree structured sparse coding on cubes", "review": "The paper extends the widely known idea of tree-structur... | {
"criticism": 3,
"example": 0,
"importance_and_relevance": 2,
"materials_and_methods": 7,
"praise": 2,
"presentation_and_reporting": 5,
"results_and_discussion": 3,
"suggestion_and_solution": 2,
"total": 18
} | 1.333333 | 0.938773 | 0.394561 | 1.405845 | 0.669785 | 0.072511 | 0.166667 | 0 | 0.111111 | 0.388889 | 0.111111 | 0.277778 | 0.166667 | 0.111111 | {
"criticism": 0.16666666666666666,
"example": 0,
"importance_and_relevance": 0.1111111111111111,
"materials_and_methods": 0.3888888888888889,
"praise": 0.1111111111111111,
"presentation_and_reporting": 0.2777777777777778,
"results_and_discussion": 0.16666666666666666,
"suggestion_and_solution": 0.11111... | 1.333333 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
rtGYtZ-ZKSMzk | Tree structured sparse coding on cubes | A brief description of tree structured sparse coding on the binary cube. | arXiv:1301.3590v1 [cs.IT] 16 Jan 2013
Tree structured sparse coding on cubes
Arthur Szlam
City College of New York
aszlam@ccny.cuny.edu
Several recent works have discussed tree structured sparsecoding [8, 10, 7, 3], where N data points
in Rd written as the d × N matrix X are approximately decomposed into the product ... | Arthur Szlam | Unknown | 2,013 | {"id": "rtGYtZ-ZKSMzk", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358405100000, "tmdate": 1358405100000, "ddate": null, "number": 7, "content": {"title": "Tree structured sparse coding on cubes", "decision": "conferencePoster-iclr2013-workshop", "abstract": "A brief descri... | [Review]:
summary:
This is a 3-page abstract only. It proposes a low-dimensional representation of data in order
to impose a tree structure. It relates to other mixed-norm approaches previously proposed in
the literature. Experiments on a binarized MNIST show how it becomes robust to added noise.
review:
I must ... | anonymous reviewer 2f02 | null | null | {"id": "7ESq7YWfqMhHk", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362001920000, "tmdate": 1362001920000, "ddate": null, "number": 2, "content": {"title": "review of Tree structured sparse coding on cubes", "review": "summary:\r\nThis is a 3-page abstract only. It proposes ... | {
"criticism": 4,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 7,
"praise": 0,
"presentation_and_reporting": 3,
"results_and_discussion": 0,
"suggestion_and_solution": 1,
"total": 9
} | 1.666667 | 1.414148 | 0.252519 | 1.699022 | 0.31385 | 0.032355 | 0.444444 | 0 | 0 | 0.777778 | 0 | 0.333333 | 0 | 0.111111 | {
"criticism": 0.4444444444444444,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0.7777777777777778,
"praise": 0,
"presentation_and_reporting": 0.3333333333333333,
"results_and_discussion": 0,
"suggestion_and_solution": 0.1111111111111111
} | 1.666667 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
rOvg47Txgprkn | Learnable Pooling Regions for Image Classification | From the early HMAX model to Spatial Pyramid Matching, pooling has played an important role in visual recognition pipelines. Spatial pooling, by grouping of local codes, equips these methods with a certain degree of robustness to translation and deformation yet preserving important spatial information. Despite the pred... | Learnable Pooling Regions for Image Classification
Mateusz Malinowski
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66123 Saarbr¨ucken, Germany
mmalinow at mpi-inf.mpg.de
Mario Fritz
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66... | Mateusz Malinowski, Mario Fritz | Unknown | 2,013 | {"id": "rOvg47Txgprkn", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358430300000, "tmdate": 1358430300000, "ddate": null, "number": 21, "content": {"title": "Learnable Pooling Regions for Image Classification", "decision": "conferencePoster-iclr2013-workshop", "abstract": "F... | [Review]:
Summary:
The paper proposes to replace the final stages of Coates and Ng's CIFAR-10 classification pipeline. In place of the hand-designed 3x3 mean pooling layer, the paper proposes to learn a pooling layer. In place of the SVM, the paper proposes to use softmax regression jointly trained with the pooling la... | anonymous reviewer 45d8 | null | null | {"id": "xEdmrekMJsvCj", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1361914920000, "tmdate": 1361914920000, "ddate": null, "number": 8, "content": {"title": "review of Learnable Pooling Regions for Image Classification", "review": "Summary:\r\nThe paper proposes to replace th... | {
"criticism": 5,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 17,
"praise": 1,
"presentation_and_reporting": 0,
"results_and_discussion": 2,
"suggestion_and_solution": 1,
"total": 18
} | 1.444444 | 1.049884 | 0.394561 | 1.479376 | 0.325636 | 0.034932 | 0.277778 | 0 | 0 | 0.944444 | 0.055556 | 0 | 0.111111 | 0.055556 | {
"criticism": 0.2777777777777778,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0.9444444444444444,
"praise": 0.05555555555555555,
"presentation_and_reporting": 0,
"results_and_discussion": 0.1111111111111111,
"suggestion_and_solution": 0.05555555555555555
} | 1.444444 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
rOvg47Txgprkn | Learnable Pooling Regions for Image Classification | From the early HMAX model to Spatial Pyramid Matching, pooling has played an important role in visual recognition pipelines. Spatial pooling, by grouping of local codes, equips these methods with a certain degree of robustness to translation and deformation yet preserving important spatial information. Despite the pred... | Learnable Pooling Regions for Image Classification
Mateusz Malinowski
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66123 Saarbr¨ucken, Germany
mmalinow at mpi-inf.mpg.de
Mario Fritz
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66... | Mateusz Malinowski, Mario Fritz | Unknown | 2,013 | {"id": "rOvg47Txgprkn", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358430300000, "tmdate": 1358430300000, "ddate": null, "number": 21, "content": {"title": "Learnable Pooling Regions for Image Classification", "decision": "conferencePoster-iclr2013-workshop", "abstract": "F... | [Review]:
PS. After reading some of the other comments, I see that I was wrong about the weights in the linear layer being possibly negative. I actually wasn't able to find the part of the paper that specifies this. I think in general the paper could be improved by being a little bit more straightforward. The method is... | anonymous reviewer 45d8 | null | null | {"id": "uEhruhQZrGeZw", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1361927280000, "tmdate": 1361927280000, "ddate": null, "number": 9, "content": {"title": "", "review": "PS. After reading some of the other comments, I see that I was wrong about the weights in the linear lay... | {
"criticism": 3,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 2,
"praise": 1,
"presentation_and_reporting": 4,
"results_and_discussion": 0,
"suggestion_and_solution": 2,
"total": 6
} | 2.166667 | 1.393328 | 0.773339 | 2.174394 | 0.155331 | 0.007728 | 0.5 | 0 | 0.166667 | 0.333333 | 0.166667 | 0.666667 | 0 | 0.333333 | {
"criticism": 0.5,
"example": 0,
"importance_and_relevance": 0.16666666666666666,
"materials_and_methods": 0.3333333333333333,
"praise": 0.16666666666666666,
"presentation_and_reporting": 0.6666666666666666,
"results_and_discussion": 0,
"suggestion_and_solution": 0.3333333333333333
} | 2.166667 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
rOvg47Txgprkn | Learnable Pooling Regions for Image Classification | From the early HMAX model to Spatial Pyramid Matching, pooling has played an important role in visual recognition pipelines. Spatial pooling, by grouping of local codes, equips these methods with a certain degree of robustness to translation and deformation yet preserving important spatial information. Despite the pred... | Learnable Pooling Regions for Image Classification
Mateusz Malinowski
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66123 Saarbr¨ucken, Germany
mmalinow at mpi-inf.mpg.de
Mario Fritz
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66... | Mateusz Malinowski, Mario Fritz | Unknown | 2,013 | {"id": "rOvg47Txgprkn", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358430300000, "tmdate": 1358430300000, "ddate": null, "number": 21, "content": {"title": "Learnable Pooling Regions for Image Classification", "decision": "conferencePoster-iclr2013-workshop", "abstract": "F... | [Review]:
As far as I can tell, the algorithm in section 2.2 (pooling + linear classifier) is essentially a 2-layer neural net trained with backprop, except that the hidden layer is linear with positive weights.
The only innovation seems to be the weight spatial smoothness regularizer of section 2.3. I think this shou... | Yann LeCun | null | null | {"id": "ttaRtzuy2NtjF", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1360139640000, "tmdate": 1360139640000, "ddate": null, "number": 2, "content": {"title": "", "review": "As far as I can tell, the algorithm in section 2.2 (pooling + linear classifier) is essentially a 2-laye... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 6,
"praise": 0,
"presentation_and_reporting": 1,
"results_and_discussion": 0,
"suggestion_and_solution": 3,
"total": 9
} | 1.222222 | 0.969703 | 0.252519 | 1.239295 | 0.221353 | 0.017073 | 0 | 0 | 0.111111 | 0.666667 | 0 | 0.111111 | 0 | 0.333333 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0.1111111111111111,
"materials_and_methods": 0.6666666666666666,
"praise": 0,
"presentation_and_reporting": 0.1111111111111111,
"results_and_discussion": 0,
"suggestion_and_solution": 0.3333333333333333
} | 1.222222 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
rOvg47Txgprkn | Learnable Pooling Regions for Image Classification | From the early HMAX model to Spatial Pyramid Matching, pooling has played an important role in visual recognition pipelines. Spatial pooling, by grouping of local codes, equips these methods with a certain degree of robustness to translation and deformation yet preserving important spatial information. Despite the pred... | Learnable Pooling Regions for Image Classification
Mateusz Malinowski
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66123 Saarbr¨ucken, Germany
mmalinow at mpi-inf.mpg.de
Mario Fritz
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66... | Mateusz Malinowski, Mario Fritz | Unknown | 2,013 | {"id": "rOvg47Txgprkn", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358430300000, "tmdate": 1358430300000, "ddate": null, "number": 21, "content": {"title": "Learnable Pooling Regions for Image Classification", "decision": "conferencePoster-iclr2013-workshop", "abstract": "F... | [Review]:
Our paper addresses the shortcomings of fixed and data-independent pooling regions in architectures such as Spatial Pyramid Matching [Lazebnik et. al., 'Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories', CVPR 2006], where dictionary-based features are pooled over larg... | Mateusz Malinowski | null | null | {"id": "mdD47o8J4hmr1", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1360973580000, "tmdate": 1360973580000, "ddate": null, "number": 10, "content": {"title": "", "review": "Our paper addresses the shortcomings of fixed and data-independent pooling regions in architectures suc... | {
"criticism": 1,
"example": 0,
"importance_and_relevance": 2,
"materials_and_methods": 14,
"praise": 0,
"presentation_and_reporting": 1,
"results_and_discussion": 2,
"suggestion_and_solution": 1,
"total": 17
} | 1.235294 | 0.982775 | 0.252519 | 1.268301 | 0.319113 | 0.033007 | 0.058824 | 0 | 0.117647 | 0.823529 | 0 | 0.058824 | 0.117647 | 0.058824 | {
"criticism": 0.058823529411764705,
"example": 0,
"importance_and_relevance": 0.11764705882352941,
"materials_and_methods": 0.8235294117647058,
"praise": 0,
"presentation_and_reporting": 0.058823529411764705,
"results_and_discussion": 0.11764705882352941,
"suggestion_and_solution": 0.058823529411764705... | 1.235294 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
rOvg47Txgprkn | Learnable Pooling Regions for Image Classification | From the early HMAX model to Spatial Pyramid Matching, pooling has played an important role in visual recognition pipelines. Spatial pooling, by grouping of local codes, equips these methods with a certain degree of robustness to translation and deformation yet preserving important spatial information. Despite the pred... | Learnable Pooling Regions for Image Classification
Mateusz Malinowski
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66123 Saarbr¨ucken, Germany
mmalinow at mpi-inf.mpg.de
Mario Fritz
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66... | Mateusz Malinowski, Mario Fritz | Unknown | 2,013 | {"id": "rOvg47Txgprkn", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358430300000, "tmdate": 1358430300000, "ddate": null, "number": 21, "content": {"title": "Learnable Pooling Regions for Image Classification", "decision": "conferencePoster-iclr2013-workshop", "abstract": "F... | [Review]:
This is a follow-up to Yoshua Bengio's comment. I'm lead author on the paper that he linked to.
One reason that Zeiler & Fergus got good results on CIFAR-100 with stochastic max pooling and my co-authors and I got good results on CIFAR-100 with maxout is that we were both using deep architectures. I think ... | Ian Goodfellow | null | null | {"id": "ddaBUNcnvHrLK", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1361922660000, "tmdate": 1361922660000, "ddate": null, "number": 6, "content": {"title": "", "review": "This is a follow-up to Yoshua Bengio's comment. I'm lead author on the paper that he linked to.\r\n\r\nO... | {
"criticism": 1,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 4,
"praise": 1,
"presentation_and_reporting": 0,
"results_and_discussion": 1,
"suggestion_and_solution": 2,
"total": 8
} | 1.25 | 0.855439 | 0.394561 | 1.265513 | 0.196999 | 0.015513 | 0.125 | 0 | 0.125 | 0.5 | 0.125 | 0 | 0.125 | 0.25 | {
"criticism": 0.125,
"example": 0,
"importance_and_relevance": 0.125,
"materials_and_methods": 0.5,
"praise": 0.125,
"presentation_and_reporting": 0,
"results_and_discussion": 0.125,
"suggestion_and_solution": 0.25
} | 1.25 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
rOvg47Txgprkn | Learnable Pooling Regions for Image Classification | From the early HMAX model to Spatial Pyramid Matching, pooling has played an important role in visual recognition pipelines. Spatial pooling, by grouping of local codes, equips these methods with a certain degree of robustness to translation and deformation yet preserving important spatial information. Despite the pred... | Learnable Pooling Regions for Image Classification
Mateusz Malinowski
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66123 Saarbr¨ucken, Germany
mmalinow at mpi-inf.mpg.de
Mario Fritz
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66... | Mateusz Malinowski, Mario Fritz | Unknown | 2,013 | {"id": "rOvg47Txgprkn", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358430300000, "tmdate": 1358430300000, "ddate": null, "number": 21, "content": {"title": "Learnable Pooling Regions for Image Classification", "decision": "conferencePoster-iclr2013-workshop", "abstract": "F... | [Review]:
We thank all the reviewers for their comments.
We will include suggested papers on related work and origins of pooling architectures as well as improvement on the state of the art that occurred in the meanwhile.
The reviewers acknowledge our analysis of regularization schemes to learn weighted pooling units... | Mateusz Malinowski, Mario Fritz | null | null | {"id": "bYfTY-ABwrbB2", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363737660000, "tmdate": 1363737660000, "ddate": null, "number": 7, "content": {"title": "", "review": "We thank all the reviewers for their comments.\r\nWe will include suggested papers on related work and o... | {
"criticism": 2,
"example": 4,
"importance_and_relevance": 2,
"materials_and_methods": 20,
"praise": 2,
"presentation_and_reporting": 1,
"results_and_discussion": 7,
"suggestion_and_solution": 3,
"total": 27
} | 1.518519 | -1.574838 | 3.093356 | 1.572411 | 0.506506 | 0.053892 | 0.074074 | 0.148148 | 0.074074 | 0.740741 | 0.074074 | 0.037037 | 0.259259 | 0.111111 | {
"criticism": 0.07407407407407407,
"example": 0.14814814814814814,
"importance_and_relevance": 0.07407407407407407,
"materials_and_methods": 0.7407407407407407,
"praise": 0.07407407407407407,
"presentation_and_reporting": 0.037037037037037035,
"results_and_discussion": 0.25925925925925924,
"suggestion_... | 1.518519 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
rOvg47Txgprkn | Learnable Pooling Regions for Image Classification | From the early HMAX model to Spatial Pyramid Matching, pooling has played an important role in visual recognition pipelines. Spatial pooling, by grouping of local codes, equips these methods with a certain degree of robustness to translation and deformation yet preserving important spatial information. Despite the pred... | Learnable Pooling Regions for Image Classification
Mateusz Malinowski
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66123 Saarbr¨ucken, Germany
mmalinow at mpi-inf.mpg.de
Mario Fritz
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66... | Mateusz Malinowski, Mario Fritz | Unknown | 2,013 | {"id": "rOvg47Txgprkn", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358430300000, "tmdate": 1358430300000, "ddate": null, "number": 21, "content": {"title": "Learnable Pooling Regions for Image Classification", "decision": "conferencePoster-iclr2013-workshop", "abstract": "F... | [Review]:
This is an interesting investigation and I only have remarks to make regarding the CIFAR-10 and CIFAR-100 results and the rapidly moving state-of-the-art (SOTA). In particular, on CIFAR-100, the 56.29% accuracy is not state-of-the-art anymore (thankfully, our field is moving fast!). There was first the resul... | Yoshua Bengio | null | null | {"id": "DtAvRX423kRIf", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1361903280000, "tmdate": 1361903280000, "ddate": null, "number": 1, "content": {"title": "", "review": "This is an interesting investigation and I only have remarks to make regarding the CIFAR-10 and CIFAR-10... | {
"criticism": 1,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 4,
"praise": 1,
"presentation_and_reporting": 0,
"results_and_discussion": 3,
"suggestion_and_solution": 2,
"total": 8
} | 1.5 | 1.105439 | 0.394561 | 1.514897 | 0.19416 | 0.014897 | 0.125 | 0 | 0.125 | 0.5 | 0.125 | 0 | 0.375 | 0.25 | {
"criticism": 0.125,
"example": 0,
"importance_and_relevance": 0.125,
"materials_and_methods": 0.5,
"praise": 0.125,
"presentation_and_reporting": 0,
"results_and_discussion": 0.375,
"suggestion_and_solution": 0.25
} | 1.5 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
rOvg47Txgprkn | Learnable Pooling Regions for Image Classification | From the early HMAX model to Spatial Pyramid Matching, pooling has played an important role in visual recognition pipelines. Spatial pooling, by grouping of local codes, equips these methods with a certain degree of robustness to translation and deformation yet preserving important spatial information. Despite the pred... | Learnable Pooling Regions for Image Classification
Mateusz Malinowski
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66123 Saarbr¨ucken, Germany
mmalinow at mpi-inf.mpg.de
Mario Fritz
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66... | Mateusz Malinowski, Mario Fritz | Unknown | 2,013 | {"id": "rOvg47Txgprkn", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358430300000, "tmdate": 1358430300000, "ddate": null, "number": 21, "content": {"title": "Learnable Pooling Regions for Image Classification", "decision": "conferencePoster-iclr2013-workshop", "abstract": "F... | [Review]:
I'm not sure why the authors are claiming state of the art on CIFAR-10 in their response, because the paper doesn't make this claim and I don't see any update to the paper. The method does not actually have state of the art on CIFAR-10 even under the constraint that it follow the architecture considered in th... | anonymous reviewer 45d8 | null | null | {"id": "6tLOt5yk_I6cd", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363741140000, "tmdate": 1363741140000, "ddate": null, "number": 4, "content": {"title": "", "review": "I'm not sure why the authors are claiming state of the art on CIFAR-10 in their response, because the pa... | {
"criticism": 6,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 11,
"praise": 1,
"presentation_and_reporting": 1,
"results_and_discussion": 2,
"suggestion_and_solution": 3,
"total": 12
} | 2 | 1.984218 | 0.015782 | 2.021859 | 0.247019 | 0.021859 | 0.5 | 0 | 0 | 0.916667 | 0.083333 | 0.083333 | 0.166667 | 0.25 | {
"criticism": 0.5,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0.9166666666666666,
"praise": 0.08333333333333333,
"presentation_and_reporting": 0.08333333333333333,
"results_and_discussion": 0.16666666666666666,
"suggestion_and_solution": 0.25
} | 2 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
rOvg47Txgprkn | Learnable Pooling Regions for Image Classification | From the early HMAX model to Spatial Pyramid Matching, pooling has played an important role in visual recognition pipelines. Spatial pooling, by grouping of local codes, equips these methods with a certain degree of robustness to translation and deformation yet preserving important spatial information. Despite the pred... | Learnable Pooling Regions for Image Classification
Mateusz Malinowski
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66123 Saarbr¨ucken, Germany
mmalinow at mpi-inf.mpg.de
Mario Fritz
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66... | Mateusz Malinowski, Mario Fritz | Unknown | 2,013 | {"id": "rOvg47Txgprkn", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358430300000, "tmdate": 1358430300000, "ddate": null, "number": 21, "content": {"title": "Learnable Pooling Regions for Image Classification", "decision": "conferencePoster-iclr2013-workshop", "abstract": "F... | [Review]:
This paper proposes a method to jointly train a pooling layer and a classifier in a supervised way.
The idea is to first extract some features and then train a 2 layer neural net by backpropagation (although in practice they use l-bfgs). The first layer is linear and the parameters are box constrained and r... | anonymous reviewer 2426 | null | null | {"id": "4w1kwHXszr4D8", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362138060000, "tmdate": 1362138060000, "ddate": null, "number": 5, "content": {"title": "review of Learnable Pooling Regions for Image Classification", "review": "This paper proposes a method to jointly trai... | {
"criticism": 10,
"example": 0,
"importance_and_relevance": 2,
"materials_and_methods": 20,
"praise": 1,
"presentation_and_reporting": 6,
"results_and_discussion": 1,
"suggestion_and_solution": 5,
"total": 29
} | 1.551724 | -2.488578 | 4.040302 | 1.596763 | 0.438764 | 0.045039 | 0.344828 | 0 | 0.068966 | 0.689655 | 0.034483 | 0.206897 | 0.034483 | 0.172414 | {
"criticism": 0.3448275862068966,
"example": 0,
"importance_and_relevance": 0.06896551724137931,
"materials_and_methods": 0.6896551724137931,
"praise": 0.034482758620689655,
"presentation_and_reporting": 0.20689655172413793,
"results_and_discussion": 0.034482758620689655,
"suggestion_and_solution": 0.1... | 1.551724 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
rOvg47Txgprkn | Learnable Pooling Regions for Image Classification | From the early HMAX model to Spatial Pyramid Matching, pooling has played an important role in visual recognition pipelines. Spatial pooling, by grouping of local codes, equips these methods with a certain degree of robustness to translation and deformation yet preserving important spatial information. Despite the pred... | Learnable Pooling Regions for Image Classification
Mateusz Malinowski
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66123 Saarbr¨ucken, Germany
mmalinow at mpi-inf.mpg.de
Mario Fritz
Computer Vision and Multimodal Computing
Max Planck Institute for Informatics
Campus E1 4, 66... | Mateusz Malinowski, Mario Fritz | Unknown | 2,013 | {"id": "rOvg47Txgprkn", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358430300000, "tmdate": 1358430300000, "ddate": null, "number": 21, "content": {"title": "Learnable Pooling Regions for Image Classification", "decision": "conferencePoster-iclr2013-workshop", "abstract": "F... | [Review]:
The paper presents a method for training pooling regions in image classification pipelines (similar to those that employ bag-of-words or spatial pyramid models). The system uses a linear pooling matrix to parametrize the pooling units and follows them with a linear classifier. The pooling units are then tra... | anonymous reviewer c1a0 | null | null | {"id": "0IOVI1hnXH0m-", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362196620000, "tmdate": 1362196620000, "ddate": null, "number": 3, "content": {"title": "review of Learnable Pooling Regions for Image Classification", "review": "The paper presents a method for training poo... | {
"criticism": 3,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 16,
"praise": 2,
"presentation_and_reporting": 4,
"results_and_discussion": 8,
"suggestion_and_solution": 1,
"total": 17
} | 2.058824 | 1.806305 | 0.252519 | 2.092186 | 0.32455 | 0.033363 | 0.176471 | 0 | 0.058824 | 0.941176 | 0.117647 | 0.235294 | 0.470588 | 0.058824 | {
"criticism": 0.17647058823529413,
"example": 0,
"importance_and_relevance": 0.058823529411764705,
"materials_and_methods": 0.9411764705882353,
"praise": 0.11764705882352941,
"presentation_and_reporting": 0.23529411764705882,
"results_and_discussion": 0.47058823529411764,
"suggestion_and_solution": 0.0... | 2.058824 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
qEV_E7oCrKqWT | Zero-Shot Learning Through Cross-Modal Transfer | This work introduces a model that can recognize objects in images even if no training data is available for the objects. The only necessary knowledge about the unseen categories comes from unsupervised large text corpora. In our zero-shot framework distributional information in language can be seen as spanning a semant... | Zero-Shot Learning Through Cross-Modal Transfer
Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng
Computer Science Department, Stanford University, Stanford, CA 94305, USA
richard@socher.org, {mganjoo, hsridhar, obastani, manning, ang}@stanford.edu
Abstract
This work int... | Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher Manning, Andrew Y. Ng | Unknown | 2,013 | {"id": "qEV_E7oCrKqWT", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358436600000, "tmdate": 1358436600000, "ddate": null, "number": 9, "content": {"title": "Zero-Shot Learning Through Cross-Modal Transfer", "decision": "conferenceOral-iclr2013-workshop", "abstract": "This wo... | [Review]:
We thank the reviewers for their feedback.
I have not seen references to similarity learning, which can be used to say if two images are of the same class. These can obviously be used to determine if an image is of a known class or not, without having seen any image of the class.
- Thanks for the referen... | Richard Socher | null | null | {"id": "ddIxYp60xFd0m", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363754820000, "tmdate": 1363754820000, "ddate": null, "number": 1, "content": {"title": "", "review": "We thank the reviewers for their feedback.\r\n\r\nI have not seen references to similarity learning, whi... | {
"criticism": 3,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 12,
"praise": 1,
"presentation_and_reporting": 7,
"results_and_discussion": 3,
"suggestion_and_solution": 6,
"total": 24
} | 1.333333 | -0.576341 | 1.909674 | 1.377911 | 0.437321 | 0.044578 | 0.125 | 0 | 0 | 0.5 | 0.041667 | 0.291667 | 0.125 | 0.25 | {
"criticism": 0.125,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0.5,
"praise": 0.041666666666666664,
"presentation_and_reporting": 0.2916666666666667,
"results_and_discussion": 0.125,
"suggestion_and_solution": 0.25
} | 1.333333 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
qEV_E7oCrKqWT | Zero-Shot Learning Through Cross-Modal Transfer | This work introduces a model that can recognize objects in images even if no training data is available for the objects. The only necessary knowledge about the unseen categories comes from unsupervised large text corpora. In our zero-shot framework distributional information in language can be seen as spanning a semant... | Zero-Shot Learning Through Cross-Modal Transfer
Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng
Computer Science Department, Stanford University, Stanford, CA 94305, USA
richard@socher.org, {mganjoo, hsridhar, obastani, manning, ang}@stanford.edu
Abstract
This work int... | Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher Manning, Andrew Y. Ng | Unknown | 2,013 | {"id": "qEV_E7oCrKqWT", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358436600000, "tmdate": 1358436600000, "ddate": null, "number": 9, "content": {"title": "Zero-Shot Learning Through Cross-Modal Transfer", "decision": "conferenceOral-iclr2013-workshop", "abstract": "This wo... | [Review]:
*A brief summary of the paper's contributions, in the context of prior work*
This paper introduces a zero-shot learning approach to image classification. The model first tries to detect whether an image contains an object from a so-far unseen category. If not, the model relies on a regular, state-of-the art... | anonymous reviewer cfb0 | null | null | {"id": "UgMKgxnHDugHr", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362080640000, "tmdate": 1362080640000, "ddate": null, "number": 3, "content": {"title": "review of Zero-Shot Learning Through Cross-Modal Transfer", "review": "*A brief summary of the paper's contributions, ... | {
"criticism": 5,
"example": 0,
"importance_and_relevance": 6,
"materials_and_methods": 12,
"praise": 5,
"presentation_and_reporting": 6,
"results_and_discussion": 2,
"suggestion_and_solution": 2,
"total": 21
} | 1.809524 | 0.799448 | 1.010076 | 1.861526 | 0.51181 | 0.052002 | 0.238095 | 0 | 0.285714 | 0.571429 | 0.238095 | 0.285714 | 0.095238 | 0.095238 | {
"criticism": 0.23809523809523808,
"example": 0,
"importance_and_relevance": 0.2857142857142857,
"materials_and_methods": 0.5714285714285714,
"praise": 0.23809523809523808,
"presentation_and_reporting": 0.2857142857142857,
"results_and_discussion": 0.09523809523809523,
"suggestion_and_solution": 0.0952... | 1.809524 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
qEV_E7oCrKqWT | Zero-Shot Learning Through Cross-Modal Transfer | This work introduces a model that can recognize objects in images even if no training data is available for the objects. The only necessary knowledge about the unseen categories comes from unsupervised large text corpora. In our zero-shot framework distributional information in language can be seen as spanning a semant... | Zero-Shot Learning Through Cross-Modal Transfer
Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng
Computer Science Department, Stanford University, Stanford, CA 94305, USA
richard@socher.org, {mganjoo, hsridhar, obastani, manning, ang}@stanford.edu
Abstract
This work int... | Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher Manning, Andrew Y. Ng | Unknown | 2,013 | {"id": "qEV_E7oCrKqWT", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358436600000, "tmdate": 1358436600000, "ddate": null, "number": 9, "content": {"title": "Zero-Shot Learning Through Cross-Modal Transfer", "decision": "conferenceOral-iclr2013-workshop", "abstract": "This wo... | [Review]:
We thank the reviewers for their feedback.
I have not seen references to similarity learning, which can be used to say if two images are of the same class. These can obviously be used to determine if an image is of a known class or not, without having seen any image of the class.
- Thanks for the referen... | Richard Socher | null | null | {"id": "SSiPd5Rr9bdXm", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363754760000, "tmdate": 1363754760000, "ddate": null, "number": 4, "content": {"title": "", "review": "We thank the reviewers for their feedback.\r\n\r\nI have not seen references to similarity learning, whi... | {
"criticism": 3,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 12,
"praise": 1,
"presentation_and_reporting": 7,
"results_and_discussion": 3,
"suggestion_and_solution": 6,
"total": 24
} | 1.333333 | -0.576341 | 1.909674 | 1.377911 | 0.437321 | 0.044578 | 0.125 | 0 | 0 | 0.5 | 0.041667 | 0.291667 | 0.125 | 0.25 | {
"criticism": 0.125,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0.5,
"praise": 0.041666666666666664,
"presentation_and_reporting": 0.2916666666666667,
"results_and_discussion": 0.125,
"suggestion_and_solution": 0.25
} | 1.333333 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
qEV_E7oCrKqWT | Zero-Shot Learning Through Cross-Modal Transfer | This work introduces a model that can recognize objects in images even if no training data is available for the objects. The only necessary knowledge about the unseen categories comes from unsupervised large text corpora. In our zero-shot framework distributional information in language can be seen as spanning a semant... | Zero-Shot Learning Through Cross-Modal Transfer
Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher D. Manning, Andrew Y. Ng
Computer Science Department, Stanford University, Stanford, CA 94305, USA
richard@socher.org, {mganjoo, hsridhar, obastani, manning, ang}@stanford.edu
Abstract
This work int... | Richard Socher, Milind Ganjoo, Hamsa Sridhar, Osbert Bastani, Christopher Manning, Andrew Y. Ng | Unknown | 2,013 | {"id": "qEV_E7oCrKqWT", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358436600000, "tmdate": 1358436600000, "ddate": null, "number": 9, "content": {"title": "Zero-Shot Learning Through Cross-Modal Transfer", "decision": "conferenceOral-iclr2013-workshop", "abstract": "This wo... | [Review]:
summary:
the paper presents a framework to learn to classify images that can come either from known
or unknown classes. This is done by first mapping both images and classes into a joint embedding
space. Furthermore, the probability of an image being of an unknown class is estimated using
a mixture of Gau... | anonymous reviewer 310e | null | null | {"id": "88s34zXWw20My", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362001800000, "tmdate": 1362001800000, "ddate": null, "number": 2, "content": {"title": "review of Zero-Shot Learning Through Cross-Modal Transfer", "review": "summary:\r\nthe paper presents a framework to l... | {
"criticism": 5,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 9,
"praise": 0,
"presentation_and_reporting": 5,
"results_and_discussion": 4,
"suggestion_and_solution": 2,
"total": 17
} | 1.529412 | 1.276893 | 0.252519 | 1.578995 | 0.468711 | 0.049583 | 0.294118 | 0 | 0.058824 | 0.529412 | 0 | 0.294118 | 0.235294 | 0.117647 | {
"criticism": 0.29411764705882354,
"example": 0,
"importance_and_relevance": 0.058823529411764705,
"materials_and_methods": 0.5294117647058824,
"praise": 0,
"presentation_and_reporting": 0.29411764705882354,
"results_and_discussion": 0.23529411764705882,
"suggestion_and_solution": 0.11764705882352941
} | 1.529412 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
msGKsXQXNiCBk | Learning New Facts From Knowledge Bases With Neural Tensor Networks and
Semantic Word Vectors | Knowledge bases provide applications with the benefit of easily accessible, systematic relational knowledge but often suffer in practice from their incompleteness and lack of knowledge of new entities and relations. Much work has focused on building or extending them by finding patterns in large unannotated text corpor... | arXiv:1301.3618v2 [cs.CL] 16 Mar 2013
Learning New Facts From Knowledge Bases With
Neural T ensor Networks and Semantic W ord V ectors
Danqi Chen, Richard Socher , Christopher D. Manning, Andrew Y . Ng
Computer Science Department, Stanford University, Stanford, CA 94305, USA
{danqi,manning,ang}@stanford.edu, richard@... | Danqi Chen, Richard Socher, Christopher Manning, Andrew Y. Ng | Unknown | 2,013 | {"id": "msGKsXQXNiCBk", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358425800000, "tmdate": 1358425800000, "ddate": null, "number": 67, "content": {"title": "Learning New Facts From Knowledge Bases With Neural Tensor Networks and\r\n Semantic Word Vectors", "decision": ... | [Review]:
This paper proposes a new model for modeling data of multi-relational knowledge bases such as Wordnet or YAGO. Inspired by the work of (Bordes et al., AAAI11), they propose a neural network-based scoring function, which is trained to assign high score to plausible relations. Evaluation is performed on Wordnet... | anonymous reviewer 7e51 | null | null | {"id": "yA-tyFEFr2A5u", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362246000000, "tmdate": 1362246000000, "ddate": null, "number": 2, "content": {"title": "review of Learning New Facts From Knowledge Bases With Neural Tensor Networks and\r\n Semantic Word Vectors", "re... | {
"criticism": 4,
"example": 2,
"importance_and_relevance": 1,
"materials_and_methods": 14,
"praise": 0,
"presentation_and_reporting": 8,
"results_and_discussion": 5,
"suggestion_and_solution": 4,
"total": 18
} | 2.111111 | 1.71655 | 0.394561 | 2.161251 | 0.494712 | 0.05014 | 0.222222 | 0.111111 | 0.055556 | 0.777778 | 0 | 0.444444 | 0.277778 | 0.222222 | {
"criticism": 0.2222222222222222,
"example": 0.1111111111111111,
"importance_and_relevance": 0.05555555555555555,
"materials_and_methods": 0.7777777777777778,
"praise": 0,
"presentation_and_reporting": 0.4444444444444444,
"results_and_discussion": 0.2777777777777778,
"suggestion_and_solution": 0.222222... | 2.111111 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
msGKsXQXNiCBk | Learning New Facts From Knowledge Bases With Neural Tensor Networks and
Semantic Word Vectors | Knowledge bases provide applications with the benefit of easily accessible, systematic relational knowledge but often suffer in practice from their incompleteness and lack of knowledge of new entities and relations. Much work has focused on building or extending them by finding patterns in large unannotated text corpor... | arXiv:1301.3618v2 [cs.CL] 16 Mar 2013
Learning New Facts From Knowledge Bases With
Neural T ensor Networks and Semantic W ord V ectors
Danqi Chen, Richard Socher , Christopher D. Manning, Andrew Y . Ng
Computer Science Department, Stanford University, Stanford, CA 94305, USA
{danqi,manning,ang}@stanford.edu, richard@... | Danqi Chen, Richard Socher, Christopher Manning, Andrew Y. Ng | Unknown | 2,013 | {"id": "msGKsXQXNiCBk", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358425800000, "tmdate": 1358425800000, "ddate": null, "number": 67, "content": {"title": "Learning New Facts From Knowledge Bases With Neural Tensor Networks and\r\n Semantic Word Vectors", "decision": ... | [Review]:
- A brief summary of the paper's contributions, in the context of prior work.
This paper proposes a new energy function (or scoring function) for ranking pairs of entities and their relationship type. The energy function is based on a so-called Neural Tensor Network, which essentially introduces a bilinear... | anonymous reviewer 75b8 | null | null | {"id": "PnfD3BSBKbnZh", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362079260000, "tmdate": 1362079260000, "ddate": null, "number": 1, "content": {"title": "review of Learning New Facts From Knowledge Bases With Neural Tensor Networks and\r\n Semantic Word Vectors", "re... | {
"criticism": 1,
"example": 0,
"importance_and_relevance": 2,
"materials_and_methods": 11,
"praise": 2,
"presentation_and_reporting": 1,
"results_and_discussion": 2,
"suggestion_and_solution": 6,
"total": 25
} | 1 | -1.27267 | 2.27267 | 1.071736 | 0.675947 | 0.071736 | 0.04 | 0 | 0.08 | 0.44 | 0.08 | 0.04 | 0.08 | 0.24 | {
"criticism": 0.04,
"example": 0,
"importance_and_relevance": 0.08,
"materials_and_methods": 0.44,
"praise": 0.08,
"presentation_and_reporting": 0.04,
"results_and_discussion": 0.08,
"suggestion_and_solution": 0.24
} | 1 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
msGKsXQXNiCBk | Learning New Facts From Knowledge Bases With Neural Tensor Networks and
Semantic Word Vectors | Knowledge bases provide applications with the benefit of easily accessible, systematic relational knowledge but often suffer in practice from their incompleteness and lack of knowledge of new entities and relations. Much work has focused on building or extending them by finding patterns in large unannotated text corpor... | arXiv:1301.3618v2 [cs.CL] 16 Mar 2013
Learning New Facts From Knowledge Bases With
Neural T ensor Networks and Semantic W ord V ectors
Danqi Chen, Richard Socher , Christopher D. Manning, Andrew Y . Ng
Computer Science Department, Stanford University, Stanford, CA 94305, USA
{danqi,manning,ang}@stanford.edu, richard@... | Danqi Chen, Richard Socher, Christopher Manning, Andrew Y. Ng | Unknown | 2,013 | {"id": "msGKsXQXNiCBk", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358425800000, "tmdate": 1358425800000, "ddate": null, "number": 67, "content": {"title": "Learning New Facts From Knowledge Bases With Neural Tensor Networks and\r\n Semantic Word Vectors", "decision": ... | [Review]:
We thank the reviewers for their comments and agree with most of them.
- We've updated our paper on arxiv, and added the important experimental comparison to the model in 'Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing' (AISTATS 2012).
Experimental results show that ... | Danqi Chen, Richard Socher, Christopher D. Manning, Andrew Y. Ng | null | null | {"id": "OgesTW8qZ5TWn", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363419120000, "tmdate": 1363419120000, "ddate": null, "number": 3, "content": {"title": "", "review": "We thank the reviewers for their comments and agree with most of them.\r\n\r\n- We've updated our paper ... | {
"criticism": 1,
"example": 1,
"importance_and_relevance": 1,
"materials_and_methods": 8,
"praise": 2,
"presentation_and_reporting": 4,
"results_and_discussion": 3,
"suggestion_and_solution": 0,
"total": 12
} | 1.666667 | 1.650884 | 0.015782 | 1.703793 | 0.346886 | 0.037126 | 0.083333 | 0.083333 | 0.083333 | 0.666667 | 0.166667 | 0.333333 | 0.25 | 0 | {
"criticism": 0.08333333333333333,
"example": 0.08333333333333333,
"importance_and_relevance": 0.08333333333333333,
"materials_and_methods": 0.6666666666666666,
"praise": 0.16666666666666666,
"presentation_and_reporting": 0.3333333333333333,
"results_and_discussion": 0.25,
"suggestion_and_solution": 0
... | 1.666667 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
msGKsXQXNiCBk | Learning New Facts From Knowledge Bases With Neural Tensor Networks and
Semantic Word Vectors | Knowledge bases provide applications with the benefit of easily accessible, systematic relational knowledge but often suffer in practice from their incompleteness and lack of knowledge of new entities and relations. Much work has focused on building or extending them by finding patterns in large unannotated text corpor... | arXiv:1301.3618v2 [cs.CL] 16 Mar 2013
Learning New Facts From Knowledge Bases With
Neural T ensor Networks and Semantic W ord V ectors
Danqi Chen, Richard Socher , Christopher D. Manning, Andrew Y . Ng
Computer Science Department, Stanford University, Stanford, CA 94305, USA
{danqi,manning,ang}@stanford.edu, richard@... | Danqi Chen, Richard Socher, Christopher Manning, Andrew Y. Ng | Unknown | 2,013 | {"id": "msGKsXQXNiCBk", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358425800000, "tmdate": 1358425800000, "ddate": null, "number": 67, "content": {"title": "Learning New Facts From Knowledge Bases With Neural Tensor Networks and\r\n Semantic Word Vectors", "decision": ... | [Review]:
We thank the reviewers for their comments and agree with most of them.
- We've updated our paper on arxiv, and added the important experimental comparison to the model in 'Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing' (AISTATS 2012).
Experimental results show that ... | Danqi Chen, Richard Socher, Christopher D. Manning, Andrew Y. Ng | null | null | {"id": "7jyp7wrwSzagb", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363419120000, "tmdate": 1363419120000, "ddate": null, "number": 4, "content": {"title": "", "review": "We thank the reviewers for their comments and agree with most of them.\r\n\r\n- We've updated our paper ... | {
"criticism": 1,
"example": 1,
"importance_and_relevance": 1,
"materials_and_methods": 8,
"praise": 2,
"presentation_and_reporting": 4,
"results_and_discussion": 3,
"suggestion_and_solution": 0,
"total": 12
} | 1.666667 | 1.650884 | 0.015782 | 1.703793 | 0.346886 | 0.037126 | 0.083333 | 0.083333 | 0.083333 | 0.666667 | 0.166667 | 0.333333 | 0.25 | 0 | {
"criticism": 0.08333333333333333,
"example": 0.08333333333333333,
"importance_and_relevance": 0.08333333333333333,
"materials_and_methods": 0.6666666666666666,
"praise": 0.16666666666666666,
"presentation_and_reporting": 0.3333333333333333,
"results_and_discussion": 0.25,
"suggestion_and_solution": 0
... | 1.666667 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
mLr3In-nbamNu | Local Component Analysis | Kernel density estimation, a.k.a. Parzen windows, is a popular density estimation method, which can be used for outlier detection or clustering. With multivariate data, its performance is heavily reliant on the metric used within the kernel. Most earlier work has focused on learning only the bandwidth of the kernel (i.... | arXiv:1109.0093v4 [cs.LG] 10 Dec 2012
Local Component Analysis
Nicolas Le Roux
nicolas@le-roux.name
Francis Bach
francis.bach@ens.fr
INRIA - SIERRA Project - Team
Laboratoire d’Informatique de l’ ´Ecole Normale Sup´ erieure
Paris, France
Abstract
Kernel density estimation, a.k.a. Parzen windows, is
a popular density ... | Nicolas Le Roux, Francis Bach | Unknown | 2,013 | {"id": "mLr3In-nbamNu", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1357918200000, "tmdate": 1357918200000, "ddate": null, "number": 64, "content": {"title": "Local Component Analysis", "decision": "conferencePoster-iclr2013-conference", "abstract": "Kernel density estimation... | [Review]:
Summary of contributions:
The paper presents a robust algorithm for density estimation. The main idea is to model the density into a product of two independent distributions: one from a Parzen windows estimation (for modeling a low dimensional manifold) and the other from a Gaussian distribution (for modelin... | anonymous reviewer 61c0 | null | null | {"id": "pRFvp6BDvn46c", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362491220000, "tmdate": 1362491220000, "ddate": null, "number": 4, "content": {"title": "review of Local Component Analysis", "review": "Summary of contributions:\r\nThe paper presents a robust algorithm for... | {
"criticism": 6,
"example": 1,
"importance_and_relevance": 5,
"materials_and_methods": 22,
"praise": 10,
"presentation_and_reporting": 2,
"results_and_discussion": 6,
"suggestion_and_solution": 2,
"total": 25
} | 2.16 | -0.11267 | 2.27267 | 2.189337 | 0.307952 | 0.029337 | 0.24 | 0.04 | 0.2 | 0.88 | 0.4 | 0.08 | 0.24 | 0.08 | {
"criticism": 0.24,
"example": 0.04,
"importance_and_relevance": 0.2,
"materials_and_methods": 0.88,
"praise": 0.4,
"presentation_and_reporting": 0.08,
"results_and_discussion": 0.24,
"suggestion_and_solution": 0.08
} | 2.16 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
mLr3In-nbamNu | Local Component Analysis | Kernel density estimation, a.k.a. Parzen windows, is a popular density estimation method, which can be used for outlier detection or clustering. With multivariate data, its performance is heavily reliant on the metric used within the kernel. Most earlier work has focused on learning only the bandwidth of the kernel (i.... | arXiv:1109.0093v4 [cs.LG] 10 Dec 2012
Local Component Analysis
Nicolas Le Roux
nicolas@le-roux.name
Francis Bach
francis.bach@ens.fr
INRIA - SIERRA Project - Team
Laboratoire d’Informatique de l’ ´Ecole Normale Sup´ erieure
Paris, France
Abstract
Kernel density estimation, a.k.a. Parzen windows, is
a popular density ... | Nicolas Le Roux, Francis Bach | Unknown | 2,013 | {"id": "mLr3In-nbamNu", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1357918200000, "tmdate": 1357918200000, "ddate": null, "number": 64, "content": {"title": "Local Component Analysis", "decision": "conferencePoster-iclr2013-conference", "abstract": "Kernel density estimation... | [Review]:
Summary of contributions:
1. The paper proposed an unsupervised local component analysis (LCA) framework that estimates the Parzen window covariance via maximizing the leave-one-out density. The basic algorithm is an EM procedure with closed form updates.
2. One further extension of LCA was introduced, w... | anonymous reviewer 18ca | null | null | {"id": "iGfW_jMjFAoZQ", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362428640000, "tmdate": 1362428640000, "ddate": null, "number": 3, "content": {"title": "review of Local Component Analysis", "review": "Summary of contributions:\r\n1. The paper proposed an unsupervised loc... | {
"criticism": 4,
"example": 0,
"importance_and_relevance": 3,
"materials_and_methods": 7,
"praise": 4,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 1,
"total": 15
} | 1.266667 | 1.203537 | 0.06313 | 1.280802 | 0.165221 | 0.014135 | 0.266667 | 0 | 0.2 | 0.466667 | 0.266667 | 0 | 0 | 0.066667 | {
"criticism": 0.26666666666666666,
"example": 0,
"importance_and_relevance": 0.2,
"materials_and_methods": 0.4666666666666667,
"praise": 0.26666666666666666,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0.06666666666666667
} | 1.266667 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
mLr3In-nbamNu | Local Component Analysis | Kernel density estimation, a.k.a. Parzen windows, is a popular density estimation method, which can be used for outlier detection or clustering. With multivariate data, its performance is heavily reliant on the metric used within the kernel. Most earlier work has focused on learning only the bandwidth of the kernel (i.... | arXiv:1109.0093v4 [cs.LG] 10 Dec 2012
Local Component Analysis
Nicolas Le Roux
nicolas@le-roux.name
Francis Bach
francis.bach@ens.fr
INRIA - SIERRA Project - Team
Laboratoire d’Informatique de l’ ´Ecole Normale Sup´ erieure
Paris, France
Abstract
Kernel density estimation, a.k.a. Parzen windows, is
a popular density ... | Nicolas Le Roux, Francis Bach | Unknown | 2,013 | {"id": "mLr3In-nbamNu", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1357918200000, "tmdate": 1357918200000, "ddate": null, "number": 64, "content": {"title": "Local Component Analysis", "decision": "conferencePoster-iclr2013-conference", "abstract": "Kernel density estimation... | [Review]:
First, we would like to thank the reviewers for their comments.
The main complaint was that the experiments were limited to toy problems. Since it is always hard to evaluate unsupervised learning algorithms (what is the metric of performance), the experiments were designed as a proof of concept. Hence, we ... | Nicolas Le Roux, Francis Bach | null | null | {"id": "c2pVc0PtwzcEK", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1364253000000, "tmdate": 1364253000000, "ddate": null, "number": 1, "content": {"title": "", "review": "First, we would like to thank the reviewers for their comments.\r\n\r\nThe main complaint was that the e... | {
"criticism": 1,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 4,
"praise": 1,
"presentation_and_reporting": 1,
"results_and_discussion": 0,
"suggestion_and_solution": 2,
"total": 6
} | 1.5 | 0.726661 | 0.773339 | 1.506837 | 0.124281 | 0.006837 | 0.166667 | 0 | 0 | 0.666667 | 0.166667 | 0.166667 | 0 | 0.333333 | {
"criticism": 0.16666666666666666,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0.6666666666666666,
"praise": 0.16666666666666666,
"presentation_and_reporting": 0.16666666666666666,
"results_and_discussion": 0,
"suggestion_and_solution": 0.3333333333333333
} | 1.5 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
mLr3In-nbamNu | Local Component Analysis | Kernel density estimation, a.k.a. Parzen windows, is a popular density estimation method, which can be used for outlier detection or clustering. With multivariate data, its performance is heavily reliant on the metric used within the kernel. Most earlier work has focused on learning only the bandwidth of the kernel (i.... | arXiv:1109.0093v4 [cs.LG] 10 Dec 2012
Local Component Analysis
Nicolas Le Roux
nicolas@le-roux.name
Francis Bach
francis.bach@ens.fr
INRIA - SIERRA Project - Team
Laboratoire d’Informatique de l’ ´Ecole Normale Sup´ erieure
Paris, France
Abstract
Kernel density estimation, a.k.a. Parzen windows, is
a popular density ... | Nicolas Le Roux, Francis Bach | Unknown | 2,013 | {"id": "mLr3In-nbamNu", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1357918200000, "tmdate": 1357918200000, "ddate": null, "number": 64, "content": {"title": "Local Component Analysis", "decision": "conferencePoster-iclr2013-conference", "abstract": "Kernel density estimation... | [Review]:
In this paper, the authors consider unsupervised metric learning as a
density estimation problem with a Parzen windows estimator based on
Euclidean metric. They use maximum likelihood method and EM algorithm
for deriving a method that may be considered as an unsupervised counterpart to neighbourhood compo... | anonymous reviewer 71f4 | null | null | {"id": "D1cO7TgVjPGT9", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1361300640000, "tmdate": 1361300640000, "ddate": null, "number": 2, "content": {"title": "review of Local Component Analysis", "review": "In this paper, the authors consider unsupervised metric learning as a\... | {
"criticism": 1,
"example": 1,
"importance_and_relevance": 1,
"materials_and_methods": 12,
"praise": 2,
"presentation_and_reporting": 1,
"results_and_discussion": 3,
"suggestion_and_solution": 4,
"total": 19
} | 1.315789 | 0.747622 | 0.568167 | 1.337187 | 0.235513 | 0.021397 | 0.052632 | 0.052632 | 0.052632 | 0.631579 | 0.105263 | 0.052632 | 0.157895 | 0.210526 | {
"criticism": 0.05263157894736842,
"example": 0.05263157894736842,
"importance_and_relevance": 0.05263157894736842,
"materials_and_methods": 0.631578947368421,
"praise": 0.10526315789473684,
"presentation_and_reporting": 0.05263157894736842,
"results_and_discussion": 0.15789473684210525,
"suggestion_an... | 1.315789 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
l_PClqDdLb5Bp | Stochastic Pooling for Regularization of Deep Convolutional Neural
Networks | We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within th... | Stochastic Pooling for Regularization of
Deep Convolutional Neural Networks
Matthew D. Zeiler
Department of Computer Science
Courant Institute, New York University
zeiler@cs.nyu.edu
Rob Fergus
Department of Computer Science
Courant Institute, New York University
fergus@cs.nyu.edu
Abstract
We introduce a simple and effe... | Matthew Zeiler, Rob Fergus | Unknown | 2,013 | {"id": "l_PClqDdLb5Bp", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358408700000, "tmdate": 1358408700000, "ddate": null, "number": 14, "content": {"title": "Stochastic Pooling for Regularization of Deep Convolutional Neural\r\n Networks", "decision": "conferenceOral-iclr... | [Review]:
I apologize for the delay in my reply.
Verdict: weak accept. | anonymous reviewer f4a8 | null | null | {"id": "w0XswsNFad7Qu", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1394470920000, "tmdate": 1394470920000, "ddate": null, "number": 7, "content": {"title": "", "review": "I apologize for the delay in my reply.\r\nVerdict: weak accept."}, "forum": "l_PClqDdLb5Bp", "referent"... | {
"criticism": 1,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0,
"total": 2
} | 0.5 | -1.409674 | 1.909674 | 0.501183 | 0.015256 | 0.001183 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | {
"criticism": 0.5,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0
} | 0.5 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
l_PClqDdLb5Bp | Stochastic Pooling for Regularization of Deep Convolutional Neural
Networks | We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within th... | Stochastic Pooling for Regularization of
Deep Convolutional Neural Networks
Matthew D. Zeiler
Department of Computer Science
Courant Institute, New York University
zeiler@cs.nyu.edu
Rob Fergus
Department of Computer Science
Courant Institute, New York University
fergus@cs.nyu.edu
Abstract
We introduce a simple and effe... | Matthew Zeiler, Rob Fergus | Unknown | 2,013 | {"id": "l_PClqDdLb5Bp", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358408700000, "tmdate": 1358408700000, "ddate": null, "number": 14, "content": {"title": "Stochastic Pooling for Regularization of Deep Convolutional Neural\r\n Networks", "decision": "conferenceOral-iclr... | [Review]:
Another minor comment related to the visualization method: since there is no iterative 'inference' step typical of deconv. nets (the features are already given by a direct forward pass) then this method is perhaps more similar to this old paper of mine:
M. Ranzato, F.J. Huang, Y. Boureau, Y. LeCun, 'Unsuperv... | Marc'Aurelio Ranzato | null | null | {"id": "obPcCcSvhKovH", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362369360000, "tmdate": 1362369360000, "ddate": null, "number": 3, "content": {"title": "", "review": "Another minor comment related to the visualization method: since there is no iterative 'inference' step ... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 4,
"praise": 1,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0,
"total": 5
} | 1.2 | 0.189924 | 1.010076 | 1.212771 | 0.142729 | 0.012771 | 0 | 0 | 0.2 | 0.8 | 0.2 | 0 | 0 | 0 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0.2,
"materials_and_methods": 0.8,
"praise": 0.2,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0
} | 1.2 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
l_PClqDdLb5Bp | Stochastic Pooling for Regularization of Deep Convolutional Neural
Networks | We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within th... | Stochastic Pooling for Regularization of
Deep Convolutional Neural Networks
Matthew D. Zeiler
Department of Computer Science
Courant Institute, New York University
zeiler@cs.nyu.edu
Rob Fergus
Department of Computer Science
Courant Institute, New York University
fergus@cs.nyu.edu
Abstract
We introduce a simple and effe... | Matthew Zeiler, Rob Fergus | Unknown | 2,013 | {"id": "l_PClqDdLb5Bp", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358408700000, "tmdate": 1358408700000, "ddate": null, "number": 14, "content": {"title": "Stochastic Pooling for Regularization of Deep Convolutional Neural\r\n Networks", "decision": "conferenceOral-iclr... | [Review]:
The authors introduce a stochastic pooling method in the context of
convolutional neural networks, which replaces the traditionally used
average or max pooling operators. In the stochastic pooling a
multinomial distribution is created from input activations and used to
select the index of the activation t... | anonymous reviewer cd07 | null | null | {"id": "lWJdCuzGuRlGF", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362101820000, "tmdate": 1362101820000, "ddate": null, "number": 2, "content": {"title": "review of Stochastic Pooling for Regularization of Deep Convolutional Neural\r\n Networks", "review": "The authors ... | {
"criticism": 3,
"example": 4,
"importance_and_relevance": 0,
"materials_and_methods": 13,
"praise": 1,
"presentation_and_reporting": 3,
"results_and_discussion": 3,
"suggestion_and_solution": 5,
"total": 22
} | 1.454545 | 0.176169 | 1.278377 | 1.4945 | 0.395045 | 0.039955 | 0.136364 | 0.181818 | 0 | 0.590909 | 0.045455 | 0.136364 | 0.136364 | 0.227273 | {
"criticism": 0.13636363636363635,
"example": 0.18181818181818182,
"importance_and_relevance": 0,
"materials_and_methods": 0.5909090909090909,
"praise": 0.045454545454545456,
"presentation_and_reporting": 0.13636363636363635,
"results_and_discussion": 0.13636363636363635,
"suggestion_and_solution": 0.2... | 1.454545 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
l_PClqDdLb5Bp | Stochastic Pooling for Regularization of Deep Convolutional Neural
Networks | We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within th... | Stochastic Pooling for Regularization of
Deep Convolutional Neural Networks
Matthew D. Zeiler
Department of Computer Science
Courant Institute, New York University
zeiler@cs.nyu.edu
Rob Fergus
Department of Computer Science
Courant Institute, New York University
fergus@cs.nyu.edu
Abstract
We introduce a simple and effe... | Matthew Zeiler, Rob Fergus | Unknown | 2,013 | {"id": "l_PClqDdLb5Bp", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358408700000, "tmdate": 1358408700000, "ddate": null, "number": 14, "content": {"title": "Stochastic Pooling for Regularization of Deep Convolutional Neural\r\n Networks", "decision": "conferenceOral-iclr... | [Review]:
Regularization methods are critical for the successful applications of
neural networks. This work introduces a new dropout-inspired
regularization method named stochastic pooling. The method is simple,
applicable applicable to convolutional neural networks with positive
nonlinearites, and achieves good ... | anonymous reviewer f4a8 | null | null | {"id": "ZVb9LYU20iZhX", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362379980000, "tmdate": 1362379980000, "ddate": null, "number": 1, "content": {"title": "review of Stochastic Pooling for Regularization of Deep Convolutional Neural\r\n Networks", "review": "Regularizati... | {
"criticism": 4,
"example": 0,
"importance_and_relevance": 2,
"materials_and_methods": 12,
"praise": 3,
"presentation_and_reporting": 0,
"results_and_discussion": 6,
"suggestion_and_solution": 1,
"total": 18
} | 1.555556 | 1.160995 | 0.394561 | 1.592791 | 0.357829 | 0.037235 | 0.222222 | 0 | 0.111111 | 0.666667 | 0.166667 | 0 | 0.333333 | 0.055556 | {
"criticism": 0.2222222222222222,
"example": 0,
"importance_and_relevance": 0.1111111111111111,
"materials_and_methods": 0.6666666666666666,
"praise": 0.16666666666666666,
"presentation_and_reporting": 0,
"results_and_discussion": 0.3333333333333333,
"suggestion_and_solution": 0.05555555555555555
} | 1.555556 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
l_PClqDdLb5Bp | Stochastic Pooling for Regularization of Deep Convolutional Neural
Networks | We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within th... | Stochastic Pooling for Regularization of
Deep Convolutional Neural Networks
Matthew D. Zeiler
Department of Computer Science
Courant Institute, New York University
zeiler@cs.nyu.edu
Rob Fergus
Department of Computer Science
Courant Institute, New York University
fergus@cs.nyu.edu
Abstract
We introduce a simple and effe... | Matthew Zeiler, Rob Fergus | Unknown | 2,013 | {"id": "l_PClqDdLb5Bp", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358408700000, "tmdate": 1358408700000, "ddate": null, "number": 14, "content": {"title": "Stochastic Pooling for Regularization of Deep Convolutional Neural\r\n Networks", "decision": "conferenceOral-iclr... | [Review]:
This paper introduces a new regularization technique based on inexpensive approximations to model averaging, similar to dropout. As with dropout, the training procedure involves stochasticity but the trained model uses a cheap approximation to the average over all possible models to make a prediction.
The ... | anonymous reviewer 2b4c | null | null | {"id": "WilRXfhv6jXxa", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1361845800000, "tmdate": 1361845800000, "ddate": null, "number": 4, "content": {"title": "review of Stochastic Pooling for Regularization of Deep Convolutional Neural\r\n Networks", "review": "This paper i... | {
"criticism": 3,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 9,
"praise": 3,
"presentation_and_reporting": 0,
"results_and_discussion": 4,
"suggestion_and_solution": 1,
"total": 10
} | 2.1 | 1.957958 | 0.142042 | 2.119427 | 0.214432 | 0.019427 | 0.3 | 0 | 0.1 | 0.9 | 0.3 | 0 | 0.4 | 0.1 | {
"criticism": 0.3,
"example": 0,
"importance_and_relevance": 0.1,
"materials_and_methods": 0.9,
"praise": 0.3,
"presentation_and_reporting": 0,
"results_and_discussion": 0.4,
"suggestion_and_solution": 0.1
} | 2.1 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
l_PClqDdLb5Bp | Stochastic Pooling for Regularization of Deep Convolutional Neural
Networks | We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within th... | Stochastic Pooling for Regularization of
Deep Convolutional Neural Networks
Matthew D. Zeiler
Department of Computer Science
Courant Institute, New York University
zeiler@cs.nyu.edu
Rob Fergus
Department of Computer Science
Courant Institute, New York University
fergus@cs.nyu.edu
Abstract
We introduce a simple and effe... | Matthew Zeiler, Rob Fergus | Unknown | 2,013 | {"id": "l_PClqDdLb5Bp", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358408700000, "tmdate": 1358408700000, "ddate": null, "number": 14, "content": {"title": "Stochastic Pooling for Regularization of Deep Convolutional Neural\r\n Networks", "decision": "conferenceOral-iclr... | [Review]:
I apologize for the delay in my reply.
Verdict: weak accept. | anonymous reviewer f4a8 | null | null | {"id": "SPk0N0RlUTrqv", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1394470920000, "tmdate": 1394470920000, "ddate": null, "number": 9, "content": {"title": "", "review": "I apologize for the delay in my reply.\r\nVerdict: weak accept."}, "forum": "l_PClqDdLb5Bp", "referent"... | {
"criticism": 1,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0,
"total": 2
} | 0.5 | -1.409674 | 1.909674 | 0.501183 | 0.015256 | 0.001183 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | {
"criticism": 0.5,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0
} | 0.5 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
l_PClqDdLb5Bp | Stochastic Pooling for Regularization of Deep Convolutional Neural
Networks | We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within th... | Stochastic Pooling for Regularization of
Deep Convolutional Neural Networks
Matthew D. Zeiler
Department of Computer Science
Courant Institute, New York University
zeiler@cs.nyu.edu
Rob Fergus
Department of Computer Science
Courant Institute, New York University
fergus@cs.nyu.edu
Abstract
We introduce a simple and effe... | Matthew Zeiler, Rob Fergus | Unknown | 2,013 | {"id": "l_PClqDdLb5Bp", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358408700000, "tmdate": 1358408700000, "ddate": null, "number": 14, "content": {"title": "Stochastic Pooling for Regularization of Deep Convolutional Neural\r\n Networks", "decision": "conferenceOral-iclr... | [Review]:
I'm excited about this paper because it introduces another trick for cheap model averaging like dropout. It will be interesting to see if this kind of fast model averaging turns into a whole subfield.
I recently got some very good results ( http://arxiv.org/abs/1302.4389 ) by using a model that works well ... | Ian Goodfellow | null | null | {"id": "OOBjrzG_LdOEf", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362085800000, "tmdate": 1362085800000, "ddate": null, "number": 5, "content": {"title": "", "review": "I'm excited about this paper because it introduces another trick for cheap model averaging like dropout.... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 5,
"praise": 2,
"presentation_and_reporting": 0,
"results_and_discussion": 1,
"suggestion_and_solution": 1,
"total": 5
} | 2 | 0.989924 | 1.010076 | 2.010319 | 0.159465 | 0.010319 | 0 | 0 | 0.2 | 1 | 0.4 | 0 | 0.2 | 0.2 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0.2,
"materials_and_methods": 1,
"praise": 0.4,
"presentation_and_reporting": 0,
"results_and_discussion": 0.2,
"suggestion_and_solution": 0.2
} | 2 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
l_PClqDdLb5Bp | Stochastic Pooling for Regularization of Deep Convolutional Neural
Networks | We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within th... | Stochastic Pooling for Regularization of
Deep Convolutional Neural Networks
Matthew D. Zeiler
Department of Computer Science
Courant Institute, New York University
zeiler@cs.nyu.edu
Rob Fergus
Department of Computer Science
Courant Institute, New York University
fergus@cs.nyu.edu
Abstract
We introduce a simple and effe... | Matthew Zeiler, Rob Fergus | Unknown | 2,013 | {"id": "l_PClqDdLb5Bp", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358408700000, "tmdate": 1358408700000, "ddate": null, "number": 14, "content": {"title": "Stochastic Pooling for Regularization of Deep Convolutional Neural\r\n Networks", "decision": "conferenceOral-iclr... | [Review]:
I really like this paper because:
- it is simple yet very effective and
- the empirical validation not only demonstrates the method but it also helps understanding where the gain comes from (tab. 5 was very useful to understand the regularization effect brought by the sampling noise).
I also found intrig... | Marc'Aurelio Ranzato | null | null | {"id": "BBmMrdZA5UBaz", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362349140000, "tmdate": 1362349140000, "ddate": null, "number": 6, "content": {"title": "", "review": "I really like this paper because:\r\n- it is simple yet very effective and\r\n- the empirical validation... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 4,
"praise": 2,
"presentation_and_reporting": 1,
"results_and_discussion": 0,
"suggestion_and_solution": 0,
"total": 8
} | 1 | 0.605439 | 0.394561 | 1.009049 | 0.100953 | 0.009049 | 0 | 0 | 0.125 | 0.5 | 0.25 | 0.125 | 0 | 0 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0.125,
"materials_and_methods": 0.5,
"praise": 0.25,
"presentation_and_reporting": 0.125,
"results_and_discussion": 0,
"suggestion_and_solution": 0
} | 1 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
l_PClqDdLb5Bp | Stochastic Pooling for Regularization of Deep Convolutional Neural
Networks | We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within th... | Stochastic Pooling for Regularization of
Deep Convolutional Neural Networks
Matthew D. Zeiler
Department of Computer Science
Courant Institute, New York University
zeiler@cs.nyu.edu
Rob Fergus
Department of Computer Science
Courant Institute, New York University
fergus@cs.nyu.edu
Abstract
We introduce a simple and effe... | Matthew Zeiler, Rob Fergus | Unknown | 2,013 | {"id": "l_PClqDdLb5Bp", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358408700000, "tmdate": 1358408700000, "ddate": null, "number": 14, "content": {"title": "Stochastic Pooling for Regularization of Deep Convolutional Neural\r\n Networks", "decision": "conferenceOral-iclr... | [Review]:
I apologize for the delay in my reply.
Verdict: weak accept. | anonymous reviewer f4a8 | null | null | {"id": "1toZvrIP-Xvme", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1394470860000, "tmdate": 1394470860000, "ddate": null, "number": 8, "content": {"title": "", "review": "I apologize for the delay in my reply.\r\nVerdict: weak accept."}, "forum": "l_PClqDdLb5Bp", "referent"... | {
"criticism": 1,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0,
"total": 2
} | 0.5 | -1.409674 | 1.909674 | 0.501183 | 0.015256 | 0.001183 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | {
"criticism": 0.5,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0
} | 0.5 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
kk_XkMO0-dP8W | Feature Learning in Deep Neural Networks - A Study on Speech Recognition
Tasks | Recent studies have shown that deep neural networks (DNNs) perform significantly better than shallow networks and Gaussian mixture models (GMMs) on large vocabulary speech recognition tasks. In this paper we argue that the difficulty in speech recognition is primarily caused by the high variability in speech signals. D... | Feature Learning in Deep Neural Networks – Studies
on Speech Recognition Tasks
Dong Yu, Michael L. Seltzer, Jinyu Li1, Jui-Ting Huang1, Frank Seide2
Microsoft Research, Redmond, W A 98052
1Microsoft Corporation, Redmond, W A 98052
2Microsoft Research Asia, Beijing, P.R.C.
{dongyu,mseltzer,jinyli,jthuang,fseide}@microso... | Dong Yu, Mike Seltzer, Jinyu Li, Jui-Ting Huang, Frank Seide | Unknown | 2,013 | {"id": "kk_XkMO0-dP8W", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358404200000, "tmdate": 1358404200000, "ddate": null, "number": 43, "content": {"title": "Feature Learning in Deep Neural Networks - A Study on Speech Recognition\r\n Tasks", "decision": "conferenceOral-i... | [Review]:
* Comments
** Summary
The paper uses examples from speech recognition to make the
following points about feature learning in deep neural networks:
1. Speech recognition performance improves with deeper networks,
but the gain per layer diminishes.
2. The internal representations in a tr... | anonymous reviewer 778f | null | null | {"id": "ySpzfXa4-ryCM", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362161880000, "tmdate": 1362161880000, "ddate": null, "number": 4, "content": {"title": "review of Feature Learning in Deep Neural Networks - A Study on Speech Recognition\r\n Tasks", "review": "* Comment... | {
"criticism": 3,
"example": 0,
"importance_and_relevance": 5,
"materials_and_methods": 8,
"praise": 4,
"presentation_and_reporting": 3,
"results_and_discussion": 2,
"suggestion_and_solution": 3,
"total": 21
} | 1.333333 | 0.323258 | 1.010076 | 1.384104 | 0.498757 | 0.050771 | 0.142857 | 0 | 0.238095 | 0.380952 | 0.190476 | 0.142857 | 0.095238 | 0.142857 | {
"criticism": 0.14285714285714285,
"example": 0,
"importance_and_relevance": 0.23809523809523808,
"materials_and_methods": 0.38095238095238093,
"praise": 0.19047619047619047,
"presentation_and_reporting": 0.14285714285714285,
"results_and_discussion": 0.09523809523809523,
"suggestion_and_solution": 0.1... | 1.333333 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
kk_XkMO0-dP8W | Feature Learning in Deep Neural Networks - A Study on Speech Recognition
Tasks | Recent studies have shown that deep neural networks (DNNs) perform significantly better than shallow networks and Gaussian mixture models (GMMs) on large vocabulary speech recognition tasks. In this paper we argue that the difficulty in speech recognition is primarily caused by the high variability in speech signals. D... | Feature Learning in Deep Neural Networks – Studies
on Speech Recognition Tasks
Dong Yu, Michael L. Seltzer, Jinyu Li1, Jui-Ting Huang1, Frank Seide2
Microsoft Research, Redmond, W A 98052
1Microsoft Corporation, Redmond, W A 98052
2Microsoft Research Asia, Beijing, P.R.C.
{dongyu,mseltzer,jinyli,jthuang,fseide}@microso... | Dong Yu, Mike Seltzer, Jinyu Li, Jui-Ting Huang, Frank Seide | Unknown | 2,013 | {"id": "kk_XkMO0-dP8W", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358404200000, "tmdate": 1358404200000, "ddate": null, "number": 43, "content": {"title": "Feature Learning in Deep Neural Networks - A Study on Speech Recognition\r\n Tasks", "decision": "conferenceOral-i... | [Review]:
The paper presents an analysis of performance of DNN acoustic models in tasks where there is a mis-match between training and test data. Most of the results do not seem to be novel, and were published in several papers already. The paper is well written and mostly easy to follow.
Pros:
Although there is n... | anonymous reviewer cf74 | null | null | {"id": "eMmX26-PXaMJN", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362128940000, "tmdate": 1362128940000, "ddate": null, "number": 1, "content": {"title": "review of Feature Learning in Deep Neural Networks - A Study on Speech Recognition\r\n Tasks", "review": "The paper... | {
"criticism": 2,
"example": 2,
"importance_and_relevance": 2,
"materials_and_methods": 10,
"praise": 3,
"presentation_and_reporting": 3,
"results_and_discussion": 4,
"suggestion_and_solution": 6,
"total": 15
} | 2.133333 | 2.070204 | 0.06313 | 2.179727 | 0.496897 | 0.046393 | 0.133333 | 0.133333 | 0.133333 | 0.666667 | 0.2 | 0.2 | 0.266667 | 0.4 | {
"criticism": 0.13333333333333333,
"example": 0.13333333333333333,
"importance_and_relevance": 0.13333333333333333,
"materials_and_methods": 0.6666666666666666,
"praise": 0.2,
"presentation_and_reporting": 0.2,
"results_and_discussion": 0.26666666666666666,
"suggestion_and_solution": 0.4
} | 2.133333 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
kk_XkMO0-dP8W | Feature Learning in Deep Neural Networks - A Study on Speech Recognition
Tasks | Recent studies have shown that deep neural networks (DNNs) perform significantly better than shallow networks and Gaussian mixture models (GMMs) on large vocabulary speech recognition tasks. In this paper we argue that the difficulty in speech recognition is primarily caused by the high variability in speech signals. D... | Feature Learning in Deep Neural Networks – Studies
on Speech Recognition Tasks
Dong Yu, Michael L. Seltzer, Jinyu Li1, Jui-Ting Huang1, Frank Seide2
Microsoft Research, Redmond, W A 98052
1Microsoft Corporation, Redmond, W A 98052
2Microsoft Research Asia, Beijing, P.R.C.
{dongyu,mseltzer,jinyli,jthuang,fseide}@microso... | Dong Yu, Mike Seltzer, Jinyu Li, Jui-Ting Huang, Frank Seide | Unknown | 2,013 | {"id": "kk_XkMO0-dP8W", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358404200000, "tmdate": 1358404200000, "ddate": null, "number": 43, "content": {"title": "Feature Learning in Deep Neural Networks - A Study on Speech Recognition\r\n Tasks", "decision": "conferenceOral-i... | [Review]:
We’d like to thank the reviewers for their comments.
We have uploaded a revised version of the paper which we believe addresses reviewers’ concerns as well as the grammatical issues and typos.
We have revised the abstract and introduction to better establish the purpose of the paper. Our goal is to d... | Mike Seltzer | null | null | {"id": "WWycbHg8XRWuv", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362989220000, "tmdate": 1362989220000, "ddate": null, "number": 3, "content": {"title": "", "review": "We\u2019d like to thank the reviewers for their comments. \r\n\r\nWe have uploaded a revised version of ... | {
"criticism": 1,
"example": 0,
"importance_and_relevance": 3,
"materials_and_methods": 7,
"praise": 1,
"presentation_and_reporting": 2,
"results_and_discussion": 4,
"suggestion_and_solution": 2,
"total": 12
} | 1.666667 | 1.650884 | 0.015782 | 1.69549 | 0.316569 | 0.028824 | 0.083333 | 0 | 0.25 | 0.583333 | 0.083333 | 0.166667 | 0.333333 | 0.166667 | {
"criticism": 0.08333333333333333,
"example": 0,
"importance_and_relevance": 0.25,
"materials_and_methods": 0.5833333333333334,
"praise": 0.08333333333333333,
"presentation_and_reporting": 0.16666666666666666,
"results_and_discussion": 0.3333333333333333,
"suggestion_and_solution": 0.16666666666666666
... | 1.666667 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
kk_XkMO0-dP8W | Feature Learning in Deep Neural Networks - A Study on Speech Recognition
Tasks | Recent studies have shown that deep neural networks (DNNs) perform significantly better than shallow networks and Gaussian mixture models (GMMs) on large vocabulary speech recognition tasks. In this paper we argue that the difficulty in speech recognition is primarily caused by the high variability in speech signals. D... | Feature Learning in Deep Neural Networks – Studies
on Speech Recognition Tasks
Dong Yu, Michael L. Seltzer, Jinyu Li1, Jui-Ting Huang1, Frank Seide2
Microsoft Research, Redmond, W A 98052
1Microsoft Corporation, Redmond, W A 98052
2Microsoft Research Asia, Beijing, P.R.C.
{dongyu,mseltzer,jinyli,jthuang,fseide}@microso... | Dong Yu, Mike Seltzer, Jinyu Li, Jui-Ting Huang, Frank Seide | Unknown | 2,013 | {"id": "kk_XkMO0-dP8W", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358404200000, "tmdate": 1358404200000, "ddate": null, "number": 43, "content": {"title": "Feature Learning in Deep Neural Networks - A Study on Speech Recognition\r\n Tasks", "decision": "conferenceOral-i... | [Review]:
This paper is by the group that did the first large-scale speech recognition experiments on deep neural nets, and popularized the technique. It contains various analysis and experiments relating to this setup.
Ultimately I was not really sure what was the main point of the paper. There is some analysis o... | anonymous reviewer 1860 | null | null | {"id": "NFxrNAiI-clI8", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1361169180000, "tmdate": 1361169180000, "ddate": null, "number": 2, "content": {"title": "review of Feature Learning in Deep Neural Networks - A Study on Speech Recognition\r\n Tasks", "review": "This pape... | {
"criticism": 2,
"example": 0,
"importance_and_relevance": 3,
"materials_and_methods": 5,
"praise": 2,
"presentation_and_reporting": 3,
"results_and_discussion": 1,
"suggestion_and_solution": 4,
"total": 13
} | 1.538462 | 1.538462 | 0 | 1.563406 | 0.300883 | 0.024944 | 0.153846 | 0 | 0.230769 | 0.384615 | 0.153846 | 0.230769 | 0.076923 | 0.307692 | {
"criticism": 0.15384615384615385,
"example": 0,
"importance_and_relevance": 0.23076923076923078,
"materials_and_methods": 0.38461538461538464,
"praise": 0.15384615384615385,
"presentation_and_reporting": 0.23076923076923078,
"results_and_discussion": 0.07692307692307693,
"suggestion_and_solution": 0.3... | 1.538462 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
jbLdjjxPd-b2l | Natural Gradient Revisited | The aim of this paper is two-folded. First we intend to show that Hessian-Free optimization (Martens, 2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described as implementations of Natural Gradient Descent due to their use of the extended Gauss-Newton approximation of the Hessian. Secondly we re-der... | Revisiting Natural Gradient for Deep Networks
Razvan Pascanu and Yoshua Bengio
Dept. IRO
University of Montreal
Montreal, QC
Abstract
The aim of this paper is three-fold. First we show that Hessian-Free (Martens,
2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described
as implementations of natural ... | Razvan Pascanu, Yoshua Bengio | Unknown | 2,013 | {"id": "jbLdjjxPd-b2l", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358454600000, "tmdate": 1358454600000, "ddate": null, "number": 54, "content": {"title": "Natural Gradient Revisited", "decision": "conferencePoster-iclr2013-workshop", "abstract": "The aim of this paper is ... | [Review]:
Thank you for your comments. We will soon push a revision to fix all the grammar and language mistakes you pointed out.
Regarding equation (1) and equation (7), mathbf{G} represents the Fisher Information Matrix form of the metric resulting when you consider respectively p(x) vs p(y|x). Equation (1) is int... | Razvan Pascanu, Yoshua Bengio | null | null | {"id": "wiYbiqRc-GqXO", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362084780000, "tmdate": 1362084780000, "ddate": null, "number": 9, "content": {"title": "", "review": "Thank you for your comments. We will soon push a revision to fix all the grammar and language mistakes y... | {
"criticism": 2,
"example": 0,
"importance_and_relevance": 2,
"materials_and_methods": 12,
"praise": 0,
"presentation_and_reporting": 5,
"results_and_discussion": 11,
"suggestion_and_solution": 3,
"total": 25
} | 1.4 | -0.87267 | 2.27267 | 1.432551 | 0.322213 | 0.032551 | 0.08 | 0 | 0.08 | 0.48 | 0 | 0.2 | 0.44 | 0.12 | {
"criticism": 0.08,
"example": 0,
"importance_and_relevance": 0.08,
"materials_and_methods": 0.48,
"praise": 0,
"presentation_and_reporting": 0.2,
"results_and_discussion": 0.44,
"suggestion_and_solution": 0.12
} | 1.4 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
jbLdjjxPd-b2l | Natural Gradient Revisited | The aim of this paper is two-folded. First we intend to show that Hessian-Free optimization (Martens, 2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described as implementations of Natural Gradient Descent due to their use of the extended Gauss-Newton approximation of the Hessian. Secondly we re-der... | Revisiting Natural Gradient for Deep Networks
Razvan Pascanu and Yoshua Bengio
Dept. IRO
University of Montreal
Montreal, QC
Abstract
The aim of this paper is three-fold. First we show that Hessian-Free (Martens,
2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described
as implementations of natural ... | Razvan Pascanu, Yoshua Bengio | Unknown | 2,013 | {"id": "jbLdjjxPd-b2l", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358454600000, "tmdate": 1358454600000, "ddate": null, "number": 54, "content": {"title": "Natural Gradient Revisited", "decision": "conferencePoster-iclr2013-workshop", "abstract": "The aim of this paper is ... | [Review]:
Summary
The paper reviews the concept of natural gradient, re-derives it in the context of neural network training, compares a number of natural gradient-based algorithms and discusses their differences. The paper's aims are highly relevant to the state of the field, and it contains numerous valuable insig... | anonymous reviewer 6f71 | null | null | {"id": "uEQsuu1xiBueM", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362372600000, "tmdate": 1362372600000, "ddate": null, "number": 3, "content": {"title": "review of Natural Gradient Revisited", "review": "Summary\r\n\r\nThe paper reviews the concept of natural gradient, re... | {
"criticism": 9,
"example": 2,
"importance_and_relevance": 7,
"materials_and_methods": 14,
"praise": 5,
"presentation_and_reporting": 7,
"results_and_discussion": 9,
"suggestion_and_solution": 9,
"total": 28
} | 2.214286 | -1.336761 | 3.551047 | 2.245884 | 0.369311 | 0.031598 | 0.321429 | 0.071429 | 0.25 | 0.5 | 0.178571 | 0.25 | 0.321429 | 0.321429 | {
"criticism": 0.32142857142857145,
"example": 0.07142857142857142,
"importance_and_relevance": 0.25,
"materials_and_methods": 0.5,
"praise": 0.17857142857142858,
"presentation_and_reporting": 0.25,
"results_and_discussion": 0.32142857142857145,
"suggestion_and_solution": 0.32142857142857145
} | 2.214286 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
jbLdjjxPd-b2l | Natural Gradient Revisited | The aim of this paper is two-folded. First we intend to show that Hessian-Free optimization (Martens, 2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described as implementations of Natural Gradient Descent due to their use of the extended Gauss-Newton approximation of the Hessian. Secondly we re-der... | Revisiting Natural Gradient for Deep Networks
Razvan Pascanu and Yoshua Bengio
Dept. IRO
University of Montreal
Montreal, QC
Abstract
The aim of this paper is three-fold. First we show that Hessian-Free (Martens,
2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described
as implementations of natural ... | Razvan Pascanu, Yoshua Bengio | Unknown | 2,013 | {"id": "jbLdjjxPd-b2l", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358454600000, "tmdate": 1358454600000, "ddate": null, "number": 54, "content": {"title": "Natural Gradient Revisited", "decision": "conferencePoster-iclr2013-workshop", "abstract": "The aim of this paper is ... | [Review]:
GENERAL COMMENTS
The paper promises to establish the relation between Amari's natural gradient and many methods that are called Natural Gradient or can be related to Natural Gradient because they use Gauss-Newton approximations of the Hessian. The problem is that I find the paper misleading. In particular ... | anonymous reviewer 6a77 | null | null | {"id": "ttBP0QO8pKtvq", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1361998920000, "tmdate": 1361998920000, "ddate": null, "number": 7, "content": {"title": "review of Natural Gradient Revisited", "review": "GENERAL COMMENTS\r\nThe paper promises to establish the relation bet... | {
"criticism": 2,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 2,
"praise": 0,
"presentation_and_reporting": 3,
"results_and_discussion": 1,
"suggestion_and_solution": 3,
"total": 10
} | 1.1 | 0.957958 | 0.142042 | 1.111044 | 0.151747 | 0.011044 | 0.2 | 0 | 0 | 0.2 | 0 | 0.3 | 0.1 | 0.3 | {
"criticism": 0.2,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0.2,
"praise": 0,
"presentation_and_reporting": 0.3,
"results_and_discussion": 0.1,
"suggestion_and_solution": 0.3
} | 1.1 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
jbLdjjxPd-b2l | Natural Gradient Revisited | The aim of this paper is two-folded. First we intend to show that Hessian-Free optimization (Martens, 2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described as implementations of Natural Gradient Descent due to their use of the extended Gauss-Newton approximation of the Hessian. Secondly we re-der... | Revisiting Natural Gradient for Deep Networks
Razvan Pascanu and Yoshua Bengio
Dept. IRO
University of Montreal
Montreal, QC
Abstract
The aim of this paper is three-fold. First we show that Hessian-Free (Martens,
2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described
as implementations of natural ... | Razvan Pascanu, Yoshua Bengio | Unknown | 2,013 | {"id": "jbLdjjxPd-b2l", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358454600000, "tmdate": 1358454600000, "ddate": null, "number": 54, "content": {"title": "Natural Gradient Revisited", "decision": "conferencePoster-iclr2013-workshop", "abstract": "The aim of this paper is ... | [Review]:
I read the updated version of the paper. I has indeed been improved substantially, and my concerns were addressed. It should clearly be accepted in its current form. | anonymous reviewer 6f71 | null | null | {"id": "iPpSPn9bTwn4Y", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363291260000, "tmdate": 1363291260000, "ddate": null, "number": 5, "content": {"title": "", "review": "I read the updated version of the paper. I has indeed been improved substantially, and my concerns were ... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 1,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 1,
"total": 3
} | 0.666667 | -0.911576 | 1.578243 | 0.66835 | 0.071263 | 0.001684 | 0 | 0 | 0 | 0 | 0.333333 | 0 | 0 | 0.333333 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0.3333333333333333,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0.3333333333333333
} | 0.666667 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
jbLdjjxPd-b2l | Natural Gradient Revisited | The aim of this paper is two-folded. First we intend to show that Hessian-Free optimization (Martens, 2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described as implementations of Natural Gradient Descent due to their use of the extended Gauss-Newton approximation of the Hessian. Secondly we re-der... | Revisiting Natural Gradient for Deep Networks
Razvan Pascanu and Yoshua Bengio
Dept. IRO
University of Montreal
Montreal, QC
Abstract
The aim of this paper is three-fold. First we show that Hessian-Free (Martens,
2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described
as implementations of natural ... | Razvan Pascanu, Yoshua Bengio | Unknown | 2,013 | {"id": "jbLdjjxPd-b2l", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358454600000, "tmdate": 1358454600000, "ddate": null, "number": 54, "content": {"title": "Natural Gradient Revisited", "decision": "conferencePoster-iclr2013-workshop", "abstract": "The aim of this paper is ... | [Review]:
We would like to thank all the reviewers for their feedback and insights. We had submitted a new version of the paper (it should appear on arxiv on Thu, 14 Mar 2013 00:00:00 GMT, though it can be retrieved now from
http://www-etud.iro.umontreal.ca/~pascanur/papers/ICLR_natural_gradient.pdf
We kindly a... | Razvan Pascanu | null | null | {"id": "aaN5bD_cRqbLk", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363216740000, "tmdate": 1363216740000, "ddate": null, "number": 2, "content": {"title": "", "review": "We would like to thank all the reviewers for their feedback and insights. We had submitted a new version... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 1,
"praise": 1,
"presentation_and_reporting": 1,
"results_and_discussion": 1,
"suggestion_and_solution": 3,
"total": 4
} | 1.75 | 0.471623 | 1.278377 | 1.757797 | 0.197601 | 0.007797 | 0 | 0 | 0 | 0.25 | 0.25 | 0.25 | 0.25 | 0.75 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0.25,
"praise": 0.25,
"presentation_and_reporting": 0.25,
"results_and_discussion": 0.25,
"suggestion_and_solution": 0.75
} | 1.75 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
jbLdjjxPd-b2l | Natural Gradient Revisited | The aim of this paper is two-folded. First we intend to show that Hessian-Free optimization (Martens, 2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described as implementations of Natural Gradient Descent due to their use of the extended Gauss-Newton approximation of the Hessian. Secondly we re-der... | Revisiting Natural Gradient for Deep Networks
Razvan Pascanu and Yoshua Bengio
Dept. IRO
University of Montreal
Montreal, QC
Abstract
The aim of this paper is three-fold. First we show that Hessian-Free (Martens,
2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described
as implementations of natural ... | Razvan Pascanu, Yoshua Bengio | Unknown | 2,013 | {"id": "jbLdjjxPd-b2l", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358454600000, "tmdate": 1358454600000, "ddate": null, "number": 54, "content": {"title": "Natural Gradient Revisited", "decision": "conferencePoster-iclr2013-workshop", "abstract": "The aim of this paper is ... | [Review]:
Clearly, the revised paper is much better than the initial paper to the extent that it should be considered a different paper that shares its title with the initial paper. The ICLR committee will have to make a policy decision about this.
The revised paper is poorly summarized by it abstract because it doe... | anonymous reviewer 6a77 | null | null | {"id": "_MfuTMZ4u7mWN", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1364251020000, "tmdate": 1364251020000, "ddate": null, "number": 4, "content": {"title": "", "review": "Clearly, the revised paper is much better than the initial paper to the extent that it should be conside... | {
"criticism": 6,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 5,
"praise": 2,
"presentation_and_reporting": 7,
"results_and_discussion": 7,
"suggestion_and_solution": 5,
"total": 15
} | 2.2 | 2.13687 | 0.06313 | 2.213608 | 0.196648 | 0.013608 | 0.4 | 0 | 0.066667 | 0.333333 | 0.133333 | 0.466667 | 0.466667 | 0.333333 | {
"criticism": 0.4,
"example": 0,
"importance_and_relevance": 0.06666666666666667,
"materials_and_methods": 0.3333333333333333,
"praise": 0.13333333333333333,
"presentation_and_reporting": 0.4666666666666667,
"results_and_discussion": 0.4666666666666667,
"suggestion_and_solution": 0.3333333333333333
} | 2.2 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
jbLdjjxPd-b2l | Natural Gradient Revisited | The aim of this paper is two-folded. First we intend to show that Hessian-Free optimization (Martens, 2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described as implementations of Natural Gradient Descent due to their use of the extended Gauss-Newton approximation of the Hessian. Secondly we re-der... | Revisiting Natural Gradient for Deep Networks
Razvan Pascanu and Yoshua Bengio
Dept. IRO
University of Montreal
Montreal, QC
Abstract
The aim of this paper is three-fold. First we show that Hessian-Free (Martens,
2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described
as implementations of natural ... | Razvan Pascanu, Yoshua Bengio | Unknown | 2,013 | {"id": "jbLdjjxPd-b2l", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358454600000, "tmdate": 1358454600000, "ddate": null, "number": 54, "content": {"title": "Natural Gradient Revisited", "decision": "conferencePoster-iclr2013-workshop", "abstract": "The aim of this paper is ... | [Review]:
The revised arxiv paper is available now, and we replied to the reviewers comments. | Razvan Pascanu, Yoshua Bengio | null | null | {"id": "XXo-vXWa-ZvQL", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363288920000, "tmdate": 1363288920000, "ddate": null, "number": 1, "content": {"title": "", "review": "The revised arxiv paper is available now, and we replied to the reviewers comments."}, "forum": "jbLdjjx... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0,
"total": 1
} | 0 | -2.27267 | 2.27267 | 0.000842 | 0.007299 | 0.000842 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0
} | 0 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
jbLdjjxPd-b2l | Natural Gradient Revisited | The aim of this paper is two-folded. First we intend to show that Hessian-Free optimization (Martens, 2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described as implementations of Natural Gradient Descent due to their use of the extended Gauss-Newton approximation of the Hessian. Secondly we re-der... | Revisiting Natural Gradient for Deep Networks
Razvan Pascanu and Yoshua Bengio
Dept. IRO
University of Montreal
Montreal, QC
Abstract
The aim of this paper is three-fold. First we show that Hessian-Free (Martens,
2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described
as implementations of natural ... | Razvan Pascanu, Yoshua Bengio | Unknown | 2,013 | {"id": "jbLdjjxPd-b2l", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358454600000, "tmdate": 1358454600000, "ddate": null, "number": 54, "content": {"title": "Natural Gradient Revisited", "decision": "conferencePoster-iclr2013-workshop", "abstract": "The aim of this paper is ... | [Review]:
This paper attempts to reconcile several definitions of the natural gradient, and to connect the Gauss-Newton approximation of the Hessian used in Hessian free optimization to the metric used in natural gradient descent. Understanding the geometry of objective functions, and the geometry of the space they li... | anonymous reviewer 1939 | null | null | {"id": "26sD6qgwF8Vob", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362404760000, "tmdate": 1362404760000, "ddate": null, "number": 8, "content": {"title": "review of Natural Gradient Revisited", "review": "This paper attempts to reconcile several definitions of the natural ... | {
"criticism": 6,
"example": 3,
"importance_and_relevance": 1,
"materials_and_methods": 5,
"praise": 0,
"presentation_and_reporting": 5,
"results_and_discussion": 1,
"suggestion_and_solution": 5,
"total": 18
} | 1.444444 | 1.049884 | 0.394561 | 1.462483 | 0.218059 | 0.018038 | 0.333333 | 0.166667 | 0.055556 | 0.277778 | 0 | 0.277778 | 0.055556 | 0.277778 | {
"criticism": 0.3333333333333333,
"example": 0.16666666666666666,
"importance_and_relevance": 0.05555555555555555,
"materials_and_methods": 0.2777777777777778,
"praise": 0,
"presentation_and_reporting": 0.2777777777777778,
"results_and_discussion": 0.05555555555555555,
"suggestion_and_solution": 0.2777... | 1.444444 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
jbLdjjxPd-b2l | Natural Gradient Revisited | The aim of this paper is two-folded. First we intend to show that Hessian-Free optimization (Martens, 2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described as implementations of Natural Gradient Descent due to their use of the extended Gauss-Newton approximation of the Hessian. Secondly we re-der... | Revisiting Natural Gradient for Deep Networks
Razvan Pascanu and Yoshua Bengio
Dept. IRO
University of Montreal
Montreal, QC
Abstract
The aim of this paper is three-fold. First we show that Hessian-Free (Martens,
2010) and Krylov Subspace Descent (Vinyals and Povey, 2012) can be described
as implementations of natural ... | Razvan Pascanu, Yoshua Bengio | Unknown | 2,013 | {"id": "jbLdjjxPd-b2l", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358454600000, "tmdate": 1358454600000, "ddate": null, "number": 54, "content": {"title": "Natural Gradient Revisited", "decision": "conferencePoster-iclr2013-workshop", "abstract": "The aim of this paper is ... | [Review]:
As the previous reviewer states, there are very large improvements in the paper. Clarity and mathematical precision are both greatly increased, and reading it now gives useful insight into the relationship between different perspectives and definitions of the natural gradient, and Hessian based methods. Not... | anonymous reviewer 1939 | null | null | {"id": "0mPCmj67CX0Ti", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1364262660000, "tmdate": 1364262660000, "ddate": null, "number": 6, "content": {"title": "", "review": "As the previous reviewer states, there are very large improvements in the paper. Clarity and mathematic... | {
"criticism": 4,
"example": 3,
"importance_and_relevance": 2,
"materials_and_methods": 2,
"praise": 3,
"presentation_and_reporting": 9,
"results_and_discussion": 1,
"suggestion_and_solution": 7,
"total": 22
} | 1.409091 | 0.130714 | 1.278377 | 1.425665 | 0.214604 | 0.016574 | 0.181818 | 0.136364 | 0.090909 | 0.090909 | 0.136364 | 0.409091 | 0.045455 | 0.318182 | {
"criticism": 0.18181818181818182,
"example": 0.13636363636363635,
"importance_and_relevance": 0.09090909090909091,
"materials_and_methods": 0.09090909090909091,
"praise": 0.13636363636363635,
"presentation_and_reporting": 0.4090909090909091,
"results_and_discussion": 0.045454545454545456,
"suggestion_... | 1.409091 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
idpCdOWtqXd60 | Efficient Estimation of Word Representations in Vector Space | We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. ... | Efficient Estimation of Word Representations in
Vector Space
Tomas Mikolov
Google Inc., Mountain View, CA
tmikolov@google.com
Kai Chen
Google Inc., Mountain View, CA
kaichen@google.com
Greg Corrado
Google Inc., Mountain View, CA
gcorrado@google.com
Jeffrey Dean
Google Inc., Mountain View, CA
jeff@google.com
Abstract
We ... | Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean | Unknown | 2,013 | {"id": "idpCdOWtqXd60", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358403300000, "tmdate": 1358403300000, "ddate": null, "number": 58, "content": {"title": "Efficient Estimation of Word Representations in Vector Space", "decision": "conferencePoster-iclr2013-workshop", "abs... | [Review]:
The revision and rebuttal failed to address the issues raised by the reviewers. I do not think the paper should be accepted in its current form.
Quality rating: Strong reject
Confidence: Reviewer is knowledgeable | anonymous reviewer f5bf | null | null | {"id": "sJxHJpdSKIJNL", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363326840000, "tmdate": 1363326840000, "ddate": null, "number": 4, "content": {"title": "", "review": "The revision and rebuttal failed to address the issues raised by the reviewers. I do not think the paper... | {
"criticism": 2,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 1,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0,
"total": 3
} | 1 | -0.578243 | 1.578243 | 1.002566 | 0.032246 | 0.002566 | 0.666667 | 0 | 0 | 0 | 0.333333 | 0 | 0 | 0 | {
"criticism": 0.6666666666666666,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0.3333333333333333,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0
} | 1 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
idpCdOWtqXd60 | Efficient Estimation of Word Representations in Vector Space | We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. ... | Efficient Estimation of Word Representations in
Vector Space
Tomas Mikolov
Google Inc., Mountain View, CA
tmikolov@google.com
Kai Chen
Google Inc., Mountain View, CA
kaichen@google.com
Greg Corrado
Google Inc., Mountain View, CA
gcorrado@google.com
Jeffrey Dean
Google Inc., Mountain View, CA
jeff@google.com
Abstract
We ... | Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean | Unknown | 2,013 | {"id": "idpCdOWtqXd60", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358403300000, "tmdate": 1358403300000, "ddate": null, "number": 58, "content": {"title": "Efficient Estimation of Word Representations in Vector Space", "decision": "conferencePoster-iclr2013-workshop", "abs... | [Review]:
The revision and rebuttal failed to address the issues raised by the reviewers. I do not think the paper should be accepted in its current form.
Quality rating: Strong reject
Confidence: Reviewer is knowledgeable | anonymous reviewer f5bf | null | null | {"id": "qX8Cq3hI2EXpf", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363279380000, "tmdate": 1363279380000, "ddate": null, "number": 10, "content": {"title": "", "review": "The revision and rebuttal failed to address the issues raised by the reviewers. I do not think the pape... | {
"criticism": 2,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 1,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0,
"total": 3
} | 1 | -0.578243 | 1.578243 | 1.002566 | 0.032246 | 0.002566 | 0.666667 | 0 | 0 | 0 | 0.333333 | 0 | 0 | 0 | {
"criticism": 0.6666666666666666,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0.3333333333333333,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0
} | 1 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
idpCdOWtqXd60 | Efficient Estimation of Word Representations in Vector Space | We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. ... | Efficient Estimation of Word Representations in
Vector Space
Tomas Mikolov
Google Inc., Mountain View, CA
tmikolov@google.com
Kai Chen
Google Inc., Mountain View, CA
kaichen@google.com
Greg Corrado
Google Inc., Mountain View, CA
gcorrado@google.com
Jeffrey Dean
Google Inc., Mountain View, CA
jeff@google.com
Abstract
We ... | Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean | Unknown | 2,013 | {"id": "idpCdOWtqXd60", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358403300000, "tmdate": 1358403300000, "ddate": null, "number": 58, "content": {"title": "Efficient Estimation of Word Representations in Vector Space", "decision": "conferencePoster-iclr2013-workshop", "abs... | [Review]:
The revision and rebuttal failed to address the issues raised by the reviewers. I do not think the paper should be accepted in its current form.
Quality rating: Strong reject
Confidence: Reviewer is knowledgeable | anonymous reviewer f5bf | null | null | {"id": "mmlAm0ZawBraS", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363279380000, "tmdate": 1363279380000, "ddate": null, "number": 7, "content": {"title": "", "review": "The revision and rebuttal failed to address the issues raised by the reviewers. I do not think the paper... | {
"criticism": 2,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 1,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0,
"total": 3
} | 1 | -0.578243 | 1.578243 | 1.002566 | 0.032246 | 0.002566 | 0.666667 | 0 | 0 | 0 | 0.333333 | 0 | 0 | 0 | {
"criticism": 0.6666666666666666,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0.3333333333333333,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0
} | 1 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
idpCdOWtqXd60 | Efficient Estimation of Word Representations in Vector Space | We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. ... | Efficient Estimation of Word Representations in
Vector Space
Tomas Mikolov
Google Inc., Mountain View, CA
tmikolov@google.com
Kai Chen
Google Inc., Mountain View, CA
kaichen@google.com
Greg Corrado
Google Inc., Mountain View, CA
gcorrado@google.com
Jeffrey Dean
Google Inc., Mountain View, CA
jeff@google.com
Abstract
We ... | Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean | Unknown | 2,013 | {"id": "idpCdOWtqXd60", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358403300000, "tmdate": 1358403300000, "ddate": null, "number": 58, "content": {"title": "Efficient Estimation of Word Representations in Vector Space", "decision": "conferencePoster-iclr2013-workshop", "abs... | [Review]:
The revision and rebuttal failed to address the issues raised by the reviewers. I do not think the paper should be accepted in its current form.
Quality rating: Strong reject
Confidence: Reviewer is knowledgeable | anonymous reviewer f5bf | null | null | {"id": "ddu0ScgIDPSxi", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363279380000, "tmdate": 1363279380000, "ddate": null, "number": 5, "content": {"title": "", "review": "The revision and rebuttal failed to address the issues raised by the reviewers. I do not think the paper... | {
"criticism": 2,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 1,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0,
"total": 3
} | 1 | -0.578243 | 1.578243 | 1.002566 | 0.032246 | 0.002566 | 0.666667 | 0 | 0 | 0 | 0.333333 | 0 | 0 | 0 | {
"criticism": 0.6666666666666666,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0.3333333333333333,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0
} | 1 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
idpCdOWtqXd60 | Efficient Estimation of Word Representations in Vector Space | We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. ... | Efficient Estimation of Word Representations in
Vector Space
Tomas Mikolov
Google Inc., Mountain View, CA
tmikolov@google.com
Kai Chen
Google Inc., Mountain View, CA
kaichen@google.com
Greg Corrado
Google Inc., Mountain View, CA
gcorrado@google.com
Jeffrey Dean
Google Inc., Mountain View, CA
jeff@google.com
Abstract
We ... | Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean | Unknown | 2,013 | {"id": "idpCdOWtqXd60", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358403300000, "tmdate": 1358403300000, "ddate": null, "number": 58, "content": {"title": "Efficient Estimation of Word Representations in Vector Space", "decision": "conferencePoster-iclr2013-workshop", "abs... | [Review]:
The paper studies the problem of learning vector representations for words based on large text corpora using 'neural language models' (NLMs). These models learn a feature vector for each word in such a way, that the feature vector of the current word in a document can be predicted from the feature vectors of ... | anonymous reviewer f5bf | null | null | {"id": "bf2Dnm5t9Ubqe", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1360865940000, "tmdate": 1360865940000, "ddate": null, "number": 8, "content": {"title": "review of Efficient Estimation of Word Representations in Vector Space", "review": "The paper studies the problem of l... | {
"criticism": 13,
"example": 4,
"importance_and_relevance": 4,
"materials_and_methods": 37,
"praise": 3,
"presentation_and_reporting": 12,
"results_and_discussion": 6,
"suggestion_and_solution": 9,
"total": 52
} | 1.692308 | -22.312768 | 24.005076 | 1.762329 | 0.657661 | 0.070021 | 0.25 | 0.076923 | 0.076923 | 0.711538 | 0.057692 | 0.230769 | 0.115385 | 0.173077 | {
"criticism": 0.25,
"example": 0.07692307692307693,
"importance_and_relevance": 0.07692307692307693,
"materials_and_methods": 0.7115384615384616,
"praise": 0.057692307692307696,
"presentation_and_reporting": 0.23076923076923078,
"results_and_discussion": 0.11538461538461539,
"suggestion_and_solution": ... | 1.692308 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
idpCdOWtqXd60 | Efficient Estimation of Word Representations in Vector Space | We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. ... | Efficient Estimation of Word Representations in
Vector Space
Tomas Mikolov
Google Inc., Mountain View, CA
tmikolov@google.com
Kai Chen
Google Inc., Mountain View, CA
kaichen@google.com
Greg Corrado
Google Inc., Mountain View, CA
gcorrado@google.com
Jeffrey Dean
Google Inc., Mountain View, CA
jeff@google.com
Abstract
We ... | Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean | Unknown | 2,013 | {"id": "idpCdOWtqXd60", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358403300000, "tmdate": 1358403300000, "ddate": null, "number": 58, "content": {"title": "Efficient Estimation of Word Representations in Vector Space", "decision": "conferencePoster-iclr2013-workshop", "abs... | [Review]:
The authors propose two log-linear language models for learning real-valued vector representations of words. The models are designed to be simple and fast and are shown to be scalable to very large datasets. The resulting word embeddings are evaluated on a number of novel word similarity tasks, on which they ... | anonymous reviewer 13e8 | null | null | {"id": "QDmFD7aPnX1h7", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1360857420000, "tmdate": 1360857420000, "ddate": null, "number": 9, "content": {"title": "review of Efficient Estimation of Word Representations in Vector Space", "review": "The authors propose two log-linear... | {
"criticism": 4,
"example": 3,
"importance_and_relevance": 1,
"materials_and_methods": 21,
"praise": 4,
"presentation_and_reporting": 11,
"results_and_discussion": 4,
"suggestion_and_solution": 4,
"total": 30
} | 1.733333 | -2.827789 | 4.561122 | 1.770894 | 0.366316 | 0.03756 | 0.133333 | 0.1 | 0.033333 | 0.7 | 0.133333 | 0.366667 | 0.133333 | 0.133333 | {
"criticism": 0.13333333333333333,
"example": 0.1,
"importance_and_relevance": 0.03333333333333333,
"materials_and_methods": 0.7,
"praise": 0.13333333333333333,
"presentation_and_reporting": 0.36666666666666664,
"results_and_discussion": 0.13333333333333333,
"suggestion_and_solution": 0.133333333333333... | 1.733333 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
idpCdOWtqXd60 | Efficient Estimation of Word Representations in Vector Space | We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. ... | Efficient Estimation of Word Representations in
Vector Space
Tomas Mikolov
Google Inc., Mountain View, CA
tmikolov@google.com
Kai Chen
Google Inc., Mountain View, CA
kaichen@google.com
Greg Corrado
Google Inc., Mountain View, CA
gcorrado@google.com
Jeffrey Dean
Google Inc., Mountain View, CA
jeff@google.com
Abstract
We ... | Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean | Unknown | 2,013 | {"id": "idpCdOWtqXd60", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358403300000, "tmdate": 1358403300000, "ddate": null, "number": 58, "content": {"title": "Efficient Estimation of Word Representations in Vector Space", "decision": "conferencePoster-iclr2013-workshop", "abs... | [Review]:
In light of the authors' response I'm changing my score for the paper to Weak Reject. | anonymous reviewer 13e8 | null | null | {"id": "OOksUbLar_UGE", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363350360000, "tmdate": 1363350360000, "ddate": null, "number": 11, "content": {"title": "", "review": "In light of the authors' response I'm changing my score for the paper to Weak Reject."}, "forum": "idpC... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0,
"total": 1
} | 0 | -2.27267 | 2.27267 | 0.001132 | 0.009817 | 0.001132 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 0,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0
} | 0 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
idpCdOWtqXd60 | Efficient Estimation of Word Representations in Vector Space | We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. ... | Efficient Estimation of Word Representations in
Vector Space
Tomas Mikolov
Google Inc., Mountain View, CA
tmikolov@google.com
Kai Chen
Google Inc., Mountain View, CA
kaichen@google.com
Greg Corrado
Google Inc., Mountain View, CA
gcorrado@google.com
Jeffrey Dean
Google Inc., Mountain View, CA
jeff@google.com
Abstract
We ... | Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean | Unknown | 2,013 | {"id": "idpCdOWtqXd60", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358403300000, "tmdate": 1358403300000, "ddate": null, "number": 58, "content": {"title": "Efficient Estimation of Word Representations in Vector Space", "decision": "conferencePoster-iclr2013-workshop", "abs... | [Review]:
This paper introduces a linear word vector learning model and shows that it performs better on a linear evaluation task than nonlinear models. While the new evaluation experiment is interesting the paper has too many issues in its current form.
One problem that has already been pointed out by the other rev... | anonymous reviewer 3c5e | null | null | {"id": "ELp1azAY4uaYz", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362415140000, "tmdate": 1362415140000, "ddate": null, "number": 3, "content": {"title": "review of Efficient Estimation of Word Representations in Vector Space", "review": "This paper introduces a linear wor... | {
"criticism": 14,
"example": 0,
"importance_and_relevance": 4,
"materials_and_methods": 24,
"praise": 4,
"presentation_and_reporting": 12,
"results_and_discussion": 9,
"suggestion_and_solution": 8,
"total": 36
} | 2.083333 | -6.265572 | 8.348905 | 2.126464 | 0.439222 | 0.043131 | 0.388889 | 0 | 0.111111 | 0.666667 | 0.111111 | 0.333333 | 0.25 | 0.222222 | {
"criticism": 0.3888888888888889,
"example": 0,
"importance_and_relevance": 0.1111111111111111,
"materials_and_methods": 0.6666666666666666,
"praise": 0.1111111111111111,
"presentation_and_reporting": 0.3333333333333333,
"results_and_discussion": 0.25,
"suggestion_and_solution": 0.2222222222222222
} | 2.083333 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
idpCdOWtqXd60 | Efficient Estimation of Word Representations in Vector Space | We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. ... | Efficient Estimation of Word Representations in
Vector Space
Tomas Mikolov
Google Inc., Mountain View, CA
tmikolov@google.com
Kai Chen
Google Inc., Mountain View, CA
kaichen@google.com
Greg Corrado
Google Inc., Mountain View, CA
gcorrado@google.com
Jeffrey Dean
Google Inc., Mountain View, CA
jeff@google.com
Abstract
We ... | Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean | Unknown | 2,013 | {"id": "idpCdOWtqXd60", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358403300000, "tmdate": 1358403300000, "ddate": null, "number": 58, "content": {"title": "Efficient Estimation of Word Representations in Vector Space", "decision": "conferencePoster-iclr2013-workshop", "abs... | [Review]:
We have updated the paper (new version will be visible on Monday):
- added new results with comparison of models trained on the same data with the same dimensionality of the word vectors
- additional comparison on a task that was used previously for comparison of word vectors
- added citations, more ... | Tomas Mikolov | null | null | {"id": "C8Vn84fqSG8qa", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362716940000, "tmdate": 1362716940000, "ddate": null, "number": 2, "content": {"title": "", "review": "We have updated the paper (new version will be visible on Monday):\r\n\r\n- added new results with compa... | {
"criticism": 43,
"example": 8,
"importance_and_relevance": 15,
"materials_and_methods": 82,
"praise": 10,
"presentation_and_reporting": 59,
"results_and_discussion": 29,
"suggestion_and_solution": 29,
"total": 152
} | 1.809211 | -303.123114 | 304.932325 | 1.990204 | 1.625794 | 0.180994 | 0.282895 | 0.052632 | 0.098684 | 0.539474 | 0.065789 | 0.388158 | 0.190789 | 0.190789 | {
"criticism": 0.28289473684210525,
"example": 0.05263157894736842,
"importance_and_relevance": 0.09868421052631579,
"materials_and_methods": 0.5394736842105263,
"praise": 0.06578947368421052,
"presentation_and_reporting": 0.3881578947368421,
"results_and_discussion": 0.19078947368421054,
"suggestion_an... | 1.809211 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
idpCdOWtqXd60 | Efficient Estimation of Word Representations in Vector Space | We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. ... | Efficient Estimation of Word Representations in
Vector Space
Tomas Mikolov
Google Inc., Mountain View, CA
tmikolov@google.com
Kai Chen
Google Inc., Mountain View, CA
kaichen@google.com
Greg Corrado
Google Inc., Mountain View, CA
gcorrado@google.com
Jeffrey Dean
Google Inc., Mountain View, CA
jeff@google.com
Abstract
We ... | Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean | Unknown | 2,013 | {"id": "idpCdOWtqXd60", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358403300000, "tmdate": 1358403300000, "ddate": null, "number": 58, "content": {"title": "Efficient Estimation of Word Representations in Vector Space", "decision": "conferencePoster-iclr2013-workshop", "abs... | [Review]:
It is really unfortunate that the responding author seems to care
solely about every possible tweak to his model and combinations of his
models but shows a strong disregard for a proper scientific comparison
that would show what's really the underlying reason for the increase
in accuracy on (again) his ow... | anonymous reviewer 3c5e | null | null | {"id": "6NMO6i-9pXN8q", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363602720000, "tmdate": 1363602720000, "ddate": null, "number": 6, "content": {"title": "", "review": "It is really unfortunate that the responding author seems to care\r\nsolely about every possible tweak t... | {
"criticism": 11,
"example": 1,
"importance_and_relevance": 2,
"materials_and_methods": 19,
"praise": 1,
"presentation_and_reporting": 9,
"results_and_discussion": 4,
"suggestion_and_solution": 2,
"total": 22
} | 2.227273 | 0.948896 | 1.278377 | 2.268101 | 0.398978 | 0.040828 | 0.5 | 0.045455 | 0.090909 | 0.863636 | 0.045455 | 0.409091 | 0.181818 | 0.090909 | {
"criticism": 0.5,
"example": 0.045454545454545456,
"importance_and_relevance": 0.09090909090909091,
"materials_and_methods": 0.8636363636363636,
"praise": 0.045454545454545456,
"presentation_and_reporting": 0.4090909090909091,
"results_and_discussion": 0.18181818181818182,
"suggestion_and_solution": 0... | 2.227273 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
idpCdOWtqXd60 | Efficient Estimation of Word Representations in Vector Space | We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. ... | Efficient Estimation of Word Representations in
Vector Space
Tomas Mikolov
Google Inc., Mountain View, CA
tmikolov@google.com
Kai Chen
Google Inc., Mountain View, CA
kaichen@google.com
Greg Corrado
Google Inc., Mountain View, CA
gcorrado@google.com
Jeffrey Dean
Google Inc., Mountain View, CA
jeff@google.com
Abstract
We ... | Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean | Unknown | 2,013 | {"id": "idpCdOWtqXd60", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358403300000, "tmdate": 1358403300000, "ddate": null, "number": 58, "content": {"title": "Efficient Estimation of Word Representations in Vector Space", "decision": "conferencePoster-iclr2013-workshop", "abs... | [Review]:
In response to the request for references made by the first author for the statement regarding semantic similarity being intransitive, I think the reference should be to 'Features of similarity' by Tversky (1977). Please find what I believe to be the relevant portion below.
`We say 'the portrait resemb... | Pontus Stenetorp | null | null | {"id": "3Ms_MCOhFG34r", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1368188160000, "tmdate": 1368188160000, "ddate": null, "number": 1, "content": {"title": "", "review": "In response to the request for references made by the first author for the statement regarding semantic ... | {
"criticism": 0,
"example": 3,
"importance_and_relevance": 0,
"materials_and_methods": 1,
"praise": 0,
"presentation_and_reporting": 5,
"results_and_discussion": 1,
"suggestion_and_solution": 3,
"total": 7
} | 1.857143 | 1.288975 | 0.568167 | 1.867758 | 0.174887 | 0.010615 | 0 | 0.428571 | 0 | 0.142857 | 0 | 0.714286 | 0.142857 | 0.428571 | {
"criticism": 0,
"example": 0.42857142857142855,
"importance_and_relevance": 0,
"materials_and_methods": 0.14285714285714285,
"praise": 0,
"presentation_and_reporting": 0.7142857142857143,
"results_and_discussion": 0.14285714285714285,
"suggestion_and_solution": 0.42857142857142855
} | 1.857143 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
iKeAKFLmxoim3 | Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity
Estimation from Facial Images | We propose a novel method for automatic pain intensity estimation from facial images based on the framework of kernel Conditional Ordinal Random Fields (KCORF). We extend this framework to account for heteroscedasticity on the output labels(i.e., pain intensity scores) and introduce a novel dynamic features, dynamic ra... | Ognjen Rudovic, Maja Pantic, Vladimir Pavlovic | Unknown | 2,013 | {"id": "iKeAKFLmxoim3", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358924400000, "tmdate": 1358924400000, "ddate": null, "number": 25, "content": {"decision": "reject", "title": "Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity\r\n Estimation from Fac... | [Review]:
This paper seeks to estimate ordinal labels of pain intensity from
videos of faces. The paper discusses a new variation of a conditional
random field in which the produced labels are ordinal values. The
paper's main claim to novelty is the idea of 'dynamic ranks', but it
is unclear what these are.
Th... | anonymous reviewer 0342 | null | null | {"id": "lBM7_cfUaYlP1", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362186300000, "tmdate": 1362186300000, "ddate": null, "number": 1, "content": {"title": "review of Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity\r\n Estimation from Facial Images", ... | {
"criticism": 10,
"example": 1,
"importance_and_relevance": 2,
"materials_and_methods": 4,
"praise": 1,
"presentation_and_reporting": 3,
"results_and_discussion": 1,
"suggestion_and_solution": 1,
"total": 17
} | 1.352941 | 1.100422 | 0.252519 | 1.352941 | 0.034118 | 0 | 0.588235 | 0.058824 | 0.117647 | 0.235294 | 0.058824 | 0.176471 | 0.058824 | 0.058824 | {
"criticism": 0.5882352941176471,
"example": 0.058823529411764705,
"importance_and_relevance": 0.11764705882352941,
"materials_and_methods": 0.23529411764705882,
"praise": 0.058823529411764705,
"presentation_and_reporting": 0.17647058823529413,
"results_and_discussion": 0.058823529411764705,
"suggestio... | 1.352941 | iclr2013 | openreview | 0 | 0 | 0 | null | |||
iKeAKFLmxoim3 | Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity
Estimation from Facial Images | We propose a novel method for automatic pain intensity estimation from facial images based on the framework of kernel Conditional Ordinal Random Fields (KCORF). We extend this framework to account for heteroscedasticity on the output labels(i.e., pain intensity scores) and introduce a novel dynamic features, dynamic ra... | Ognjen Rudovic, Maja Pantic, Vladimir Pavlovic | Unknown | 2,013 | {"id": "iKeAKFLmxoim3", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358924400000, "tmdate": 1358924400000, "ddate": null, "number": 25, "content": {"decision": "reject", "title": "Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity\r\n Estimation from Fac... | [Review]:
This extended abstract discusses a modification to an existing ordinal conditional random field model (CORF) so as to treat non-stationary data. This is done by making the variance in a probit model depend on the observations (x) and appealing to results on kernels methods for CRFs by Lafferty et al. The auth... | anonymous reviewer 9402 | null | null | {"id": "VTEO8hp3ad83Q", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362297780000, "tmdate": 1362297780000, "ddate": null, "number": 2, "content": {"title": "review of Heteroscedastic Conditional Ordinal Random Fields for Pain Intensity\r\n Estimation from Facial Images", ... | {
"criticism": 3,
"example": 0,
"importance_and_relevance": 1,
"materials_and_methods": 6,
"praise": 2,
"presentation_and_reporting": 11,
"results_and_discussion": 3,
"suggestion_and_solution": 10,
"total": 20
} | 1.8 | 1.026661 | 0.773339 | 1.8 | 0.098 | 0 | 0.15 | 0 | 0.05 | 0.3 | 0.1 | 0.55 | 0.15 | 0.5 | {
"criticism": 0.15,
"example": 0,
"importance_and_relevance": 0.05,
"materials_and_methods": 0.3,
"praise": 0.1,
"presentation_and_reporting": 0.55,
"results_and_discussion": 0.15,
"suggestion_and_solution": 0.5
} | 1.8 | iclr2013 | openreview | 0 | 0 | 0 | null | |||
i87JIQTAnB8AQ | The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization | Non-negative matrix factorization (NMF) has become a popular machine learning approach to many problems in text mining, speech and image processing, bio-informatics and seismic data analysis to name a few. In NMF, a matrix of non-negative data is approximated by the low-rank product of two matrices with non-negative en... |
The Diagonalized Newton Algorithm for Non-
negative Matrix Factorization
Hugo Van hamme 1
University of Leuven, dept. ESAT 2
Kasteelpark Arenberg 10 – bus 2441, 3001 Leuven, B elgium 3
hugo.vanhamme@esat.kuleuven.be 4
Abstract 5
Non-negative matrix factorization (NMF) has become a popular machine 6
learn... | Hugo Van hamme | Unknown | 2,013 | {"id": "i87JIQTAnB8AQ", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358343900000, "tmdate": 1358343900000, "ddate": null, "number": 60, "content": {"title": "The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization", "decision": "conferencePoster-iclr2013-confe... | [Review]:
This paper develops a new iterative optimization algorithm for performing non-negative matrix factorization, assuming a standard 'KL-divergence' objective function. The method proposed combines the use of a traditional updating scheme ('multiplicative updates' from [1]) in the initial phase of optimization, ... | anonymous reviewer 57f3 | null | null | {"id": "oo1KoBhzu3CGs", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1362192540000, "tmdate": 1362192540000, "ddate": null, "number": 6, "content": {"title": "review of The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization", "review": "This paper develops a ne... | {
"criticism": 12,
"example": 3,
"importance_and_relevance": 2,
"materials_and_methods": 17,
"praise": 3,
"presentation_and_reporting": 18,
"results_and_discussion": 8,
"suggestion_and_solution": 10,
"total": 43
} | 1.697674 | -12.506512 | 14.204187 | 1.773556 | 0.714406 | 0.075882 | 0.27907 | 0.069767 | 0.046512 | 0.395349 | 0.069767 | 0.418605 | 0.186047 | 0.232558 | {
"criticism": 0.27906976744186046,
"example": 0.06976744186046512,
"importance_and_relevance": 0.046511627906976744,
"materials_and_methods": 0.3953488372093023,
"praise": 0.06976744186046512,
"presentation_and_reporting": 0.4186046511627907,
"results_and_discussion": 0.18604651162790697,
"suggestion_a... | 1.697674 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
i87JIQTAnB8AQ | The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization | Non-negative matrix factorization (NMF) has become a popular machine learning approach to many problems in text mining, speech and image processing, bio-informatics and seismic data analysis to name a few. In NMF, a matrix of non-negative data is approximated by the low-rank product of two matrices with non-negative en... |
The Diagonalized Newton Algorithm for Non-
negative Matrix Factorization
Hugo Van hamme 1
University of Leuven, dept. ESAT 2
Kasteelpark Arenberg 10 – bus 2441, 3001 Leuven, B elgium 3
hugo.vanhamme@esat.kuleuven.be 4
Abstract 5
Non-negative matrix factorization (NMF) has become a popular machine 6
learn... | Hugo Van hamme | Unknown | 2,013 | {"id": "i87JIQTAnB8AQ", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358343900000, "tmdate": 1358343900000, "ddate": null, "number": 60, "content": {"title": "The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization", "decision": "conferencePoster-iclr2013-confe... | [Review]:
About the comparison with Cyclic Coordinate Descent (as described in C.-J. Hsieh and I. S. Dhillon, “Fast Coordinate Descent Methods with Variable Selection for Non-negative Matrix Factorization,” in proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), San Di... | Hugo Van hamme | null | null | {"id": "aplzZcXNokptc", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363615980000, "tmdate": 1363615980000, "ddate": null, "number": 2, "content": {"title": "", "review": "About the comparison with Cyclic Coordinate Descent (as described in C.-J. Hsieh and I. S. Dhillon, \u20... | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 3,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0,
"total": 3
} | 1 | -0.578243 | 1.578243 | 1.015833 | 0.147275 | 0.015833 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | {
"criticism": 0,
"example": 0,
"importance_and_relevance": 0,
"materials_and_methods": 1,
"praise": 0,
"presentation_and_reporting": 0,
"results_and_discussion": 0,
"suggestion_and_solution": 0
} | 1 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
i87JIQTAnB8AQ | The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization | Non-negative matrix factorization (NMF) has become a popular machine learning approach to many problems in text mining, speech and image processing, bio-informatics and seismic data analysis to name a few. In NMF, a matrix of non-negative data is approximated by the low-rank product of two matrices with non-negative en... |
The Diagonalized Newton Algorithm for Non-
negative Matrix Factorization
Hugo Van hamme 1
University of Leuven, dept. ESAT 2
Kasteelpark Arenberg 10 – bus 2441, 3001 Leuven, B elgium 3
hugo.vanhamme@esat.kuleuven.be 4
Abstract 5
Non-negative matrix factorization (NMF) has become a popular machine 6
learn... | Hugo Van hamme | Unknown | 2,013 | {"id": "i87JIQTAnB8AQ", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358343900000, "tmdate": 1358343900000, "ddate": null, "number": 60, "content": {"title": "The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization", "decision": "conferencePoster-iclr2013-confe... | [Review]:
I would like to thank the reviewers for their investment of time and effort to formulate their valued comments. The paper was updated according to your comments. Below I address your concerns:
A common remark is the lack of comparison with state-of-the-art NMF solvers for Kullback-Leibler divergence (KLD).... | Hugo Van hamme | null | null | {"id": "RzSh7m1KhlzKg", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363574460000, "tmdate": 1363574460000, "ddate": null, "number": 5, "content": {"title": "", "review": "I would like to thank the reviewers for their investment of time and effort to formulate their valued co... | {
"criticism": 12,
"example": 6,
"importance_and_relevance": 2,
"materials_and_methods": 30,
"praise": 5,
"presentation_and_reporting": 20,
"results_and_discussion": 7,
"suggestion_and_solution": 18,
"total": 84
} | 1.190476 | -78.368752 | 79.559228 | 1.31617 | 1.136194 | 0.125694 | 0.142857 | 0.071429 | 0.02381 | 0.357143 | 0.059524 | 0.238095 | 0.083333 | 0.214286 | {
"criticism": 0.14285714285714285,
"example": 0.07142857142857142,
"importance_and_relevance": 0.023809523809523808,
"materials_and_methods": 0.35714285714285715,
"praise": 0.05952380952380952,
"presentation_and_reporting": 0.23809523809523808,
"results_and_discussion": 0.08333333333333333,
"suggestion... | 1.190476 | iclr2013 | openreview | 0 | 0 | 0 | null | ||
i87JIQTAnB8AQ | The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization | Non-negative matrix factorization (NMF) has become a popular machine learning approach to many problems in text mining, speech and image processing, bio-informatics and seismic data analysis to name a few. In NMF, a matrix of non-negative data is approximated by the low-rank product of two matrices with non-negative en... |
The Diagonalized Newton Algorithm for Non-
negative Matrix Factorization
Hugo Van hamme 1
University of Leuven, dept. ESAT 2
Kasteelpark Arenberg 10 – bus 2441, 3001 Leuven, B elgium 3
hugo.vanhamme@esat.kuleuven.be 4
Abstract 5
Non-negative matrix factorization (NMF) has become a popular machine 6
learn... | Hugo Van hamme | Unknown | 2,013 | {"id": "i87JIQTAnB8AQ", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1358343900000, "tmdate": 1358343900000, "ddate": null, "number": 60, "content": {"title": "The Diagonalized Newton Algorithm for Nonnegative Matrix Factorization", "decision": "conferencePoster-iclr2013-confe... | [Review]:
First: sorry for the multiple postings. Browser acting weird. Can't remove them ...
Update: I was able to get the sbcd code to work. Two mods required (refer to Algorithm 1 in the Li, Lebanon & Park paper - ref [18] in v2 paper on arxiv):
1) you have to be careful with initialization. If the estimates for... | Hugo Van hamme | null | null | {"id": "MqwZf2jPZCJ-n", "original": null, "cdate": null, "pdate": null, "odate": null, "mdate": null, "tcdate": 1363744920000, "tmdate": 1363744920000, "ddate": null, "number": 1, "content": {"title": "", "review": "First: sorry for the multiple postings. Browser acting weird. Can't remove them ...\r\n\r\nUpdate: I was... | {
"criticism": 5,
"example": 2,
"importance_and_relevance": 0,
"materials_and_methods": 10,
"praise": 1,
"presentation_and_reporting": 4,
"results_and_discussion": 3,
"suggestion_and_solution": 1,
"total": 16
} | 1.625 | 1.482958 | 0.142042 | 1.65988 | 0.328035 | 0.03488 | 0.3125 | 0.125 | 0 | 0.625 | 0.0625 | 0.25 | 0.1875 | 0.0625 | {
"criticism": 0.3125,
"example": 0.125,
"importance_and_relevance": 0,
"materials_and_methods": 0.625,
"praise": 0.0625,
"presentation_and_reporting": 0.25,
"results_and_discussion": 0.1875,
"suggestion_and_solution": 0.0625
} | 1.625 | iclr2013 | openreview | 0 | 0 | 0 | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.