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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... | {
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} | 0.333333 | -1.24491 | 1.578243 | 0.333333 | 0.053333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.333333 | {
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} | 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... | {
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} | 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 | {
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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... | {
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} | 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 | {
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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... | {
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"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 | {
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"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... | {
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} | 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 | {
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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... | {
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} | 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,
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} | 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... | {
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} | 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,
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} | 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... | {
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"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 | {
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"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... | {
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} | 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 | {
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} | 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 ... | {
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} | 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 | {
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