-
1
-
-
84867605836
-
Applying convolutional neural networks concepts to hybrid nn-hmm model for speech recognition
-
3
-
O. Abdel Hamid, A. R. Mohamed, H. Jiang, and G. Penn. Applying convolutional neural networks concepts to hybrid nn-hmm model for speech recognition. ICASSP, 2012. 3
-
(2012)
ICASSP
-
-
Abdel Hamid, O.1
Mohamed, A.R.2
Jiang, H.3
Penn, G.4
-
2
-
-
0000782329
-
Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping
-
6
-
R. Caruana, S. Lawrence, and C. L. Giles. Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping. NIPS, 2000. 6
-
(2000)
NIPS
-
-
Caruana, R.1
Lawrence, S.2
Giles, C.L.3
-
4
-
-
84890527827
-
Improving deep neural networks for lvcsr using rectified linear units and dropout
-
2
-
G. E. Dahl, T. N. Sainath, and G. E. Hinton. Improving deep neural networks for lvcsr using rectified linear units and dropout. ICASSP, 2013. 2
-
(2013)
ICASSP
-
-
Dahl, G.E.1
Sainath, T.N.2
Hinton, G.E.3
-
5
-
-
84944046597
-
-
arXiv 1411 4389. 2, 7
-
J. Donahue, L. A. Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darreell. Long-term recurrent convolutional networks for visual recognition and description. arXiv: 1411. 4389, 2014. 2, 7
-
(2014)
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
-
-
Donahue, J.1
Hendricks, L.A.2
Guadarrama, S.3
Rohrbach, M.4
Venugopalan, S.5
Saenko, K.6
Darreell, T.7
-
6
-
-
84898958665
-
Devise: A deep visual-semantic embedding model
-
1, 2, 6, 7, 8
-
A. Frame, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, M. A. Ranzato, and T. Mikolov. Devise: A deep visual-semantic embedding model. NIPS, 2013. 1, 2, 6, 7, 8
-
(2013)
NIPS
-
-
Frame, A.1
Corrado, G.S.2
Shlens, J.3
Bengio, S.4
Dean, J.5
Ranzato, M.A.6
Mikolov, T.7
-
7
-
-
84911400494
-
Rich feature hierachies for accurate object detection and semantic segmentation
-
8
-
R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierachies for accurate object detection and semantic segmentation. CVPR, 2014. 8
-
(2014)
CVPR
-
-
Girshick, R.1
Donahue, J.2
Darrell, T.3
Malik, J.4
-
8
-
-
84959243872
-
Improving image-sentence embeddings using large weakly annotated photo collections
-
1
-
Y. Gong, L. Wang, M. Hodosh, J. Hockenmaier, and S. lazebnik. Improving image-sentence embeddings using large weakly annotated photo collections. ECCV, 2014. 1
-
(2014)
ECCV
-
-
Gong, Y.1
Wang, L.2
Hodosh, M.3
Hockenmaier, J.4
Lazebnik, S.5
-
9
-
-
34447620428
-
A neural network to retrieve images from text queries
-
2
-
D. Grangier and S. Bengio. A neural network to retrieve images from text queries. ICANN, 2006. 2
-
(2006)
ICANN
-
-
Grangier, D.1
Bengio, S.2
-
10
-
-
84928278589
-
Spatial pyramid pooling in deep convolutional networks for visual recognition
-
1
-
K. He, X. Zhang, S. Ren, and J. Sun. Spatial pyramid pooling in deep convolutional networks for visual recognition. ECCV, 2014. 1
-
(2014)
ECCV
-
-
He, K.1
Zhang, X.2
Ren, S.3
Sun, J.4
-
12
-
-
84867720412
-
-
arXiv 1207 0580. 6
-
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Improving neural networks by proventing co-adaptation of feature detecters. arXiv: 1207. 0580, 2012. 6
-
(2012)
Improving Neural Networks by Proventing Co-adaptation of Feature Detecters
-
-
Hinton, G.E.1
Srivastava, N.2
Krizhevsky, A.3
Sutskever, I.4
Salakhutdinov, R.5
-
13
-
-
84883394520
-
Framing image description as a ranking task: Data, models and evaluation metrics
-
1, 2, 6
-
M. Hodosh, P. Young, and J. Hockenmaier. Framing image description as a ranking task: Data, models and evaluation metrics. Journal of Artificial Intelligence Research, 47: 853-899, 2013. 1, 2, 6
-
(2013)
Journal of Artificial Intelligence Research
, vol.47
, pp. 853-899
-
-
Hodosh, M.1
Young, P.2
Hockenmaier, J.3
-
14
-
-
84937936034
-
Convolutional neural network architectures for matching natural language sentences
-
1, 3
-
B. Hu, Z. Lu, H. Li, and Q. Chen. Convolutional neural network architectures for matching natural language sentences. NIPS, 2014. 1, 3
-
(2014)
NIPS
-
-
Hu, B.1
Lu, Z.2
Li, H.3
Chen, Q.4
-
15
-
-
84906922163
-
A convolutional neural network for modelling sentences
-
1
-
N. Kalchbrenner, E. Grefenstette, and P. Blunsom. A convolutional neural network for modelling sentences. ACL, 2014. 1
-
(2014)
ACL
-
-
Kalchbrenner, N.1
Grefenstette, E.2
Blunsom, P.3
-
16
-
-
84937843643
-
Deep fragment embeddings for bidirectional image sentence mapping
-
1, 2, 6, 7, 8
-
A. Karpathy, A. Joulin, and F.-F. Li. Deep fragment embeddings for bidirectional image sentence mapping. NIPS, 2014. 1, 2, 6, 7, 8
-
(2014)
NIPS
-
-
Karpathy, A.1
Joulin, A.2
Li, F.-F.3
-
18
-
-
84961376850
-
Convolutional neural network for sentence classification
-
1
-
Y. Kim. Convolutional neural network for sentence classification. EMNLP, 2014. 1
-
(2014)
EMNLP
-
-
Kim, Y.1
-
20
-
-
84944113729
-
-
arXiv 1411 2539. 2, 6, 7
-
R. Kiros, R. Salakhutdinov, and R. S. Zemel. Unifying visual-semantic embeddings with multimodal neural language models. arXiv: 1411. 2539, 2014. 2, 6, 7
-
(2014)
Unifying Visual-semantic Embeddings with Multimodal Neural Language Models
-
-
Kiros, R.1
Salakhutdinov, R.2
Zemel, R.S.3
-
21
-
-
84877777478
-
Deep representations and codes for image auto-annotation
-
1
-
R. Kiros and C. Szepesvári. Deep representations and codes for image auto-annotation. NIPS, 2012. 1
-
(2012)
NIPS
-
-
Kiros, R.1
Szepesvári, C.2
-
23
-
-
84939821073
-
-
arXiv 1412 6632. 2, 7
-
J. Mao, W. Xu, Y. Yang, J. Wang, and A. L. Yuille. Deep captioning with multimodal recurrent neural networks (mrnn). arXiv: 1412. 6632, 2014. 2, 7
-
(2014)
Deep Captioning with Multimodal Recurrent Neural Networks (Mrnn)
-
-
Mao, J.1
Xu, W.2
Yang, Y.3
Wang, J.4
Yuille, A.L.5
-
24
-
-
84951072975
-
-
arXiv 1410 1090. 2, 6, 7
-
J. Mao, W. Xu, Y. Yang, J. Wang, and A. L. Yuille. Explain images with multimodal recurrent neural networks. arXiv: 1410. 1090, 2014. 2, 6, 7
-
(2014)
Explain Images with Multimodal Recurrent Neural Networks
-
-
Mao, J.1
Xu, W.2
Yang, Y.3
Wang, J.4
Yuille, A.L.5
-
26
-
-
85162522202
-
Im2txt: Describing images using 1 million captioned photogrphs
-
1
-
V. Ordonez, G. Kulkarni, and T. L. Berg. Im2txt: Describing images using 1 million captioned photogrphs. NIPS, 2011. 1
-
(2011)
NIPS
-
-
Ordonez, V.1
Kulkarni, G.2
Berg, T.L.3
-
27
-
-
80052889458
-
Recognition using visual phrases
-
1, 2
-
M. A. Sadeghi and A. Farhadi. Recognition using visual phrases. CVPR, 2011. 1, 2
-
(2011)
CVPR
-
-
Sadeghi, M.A.1
Farhadi, A.2
-
28
-
-
85083951635
-
-
arXiv 1312 6229, 2, 5, 6, 7
-
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. Overfeat: Intergrated recognition, localization and detection using convolutional networks. arXiv: 1312. 6229, 2014. 2, 5, 6, 7
-
(2014)
Overfeat: Intergrated Recognition, Localization and Detection Using Convolutional Networks
-
-
Sermanet, P.1
Eigen, D.2
Zhang, X.3
Mathieu, M.4
Fergus, R.5
LeCun, Y.6
-
29
-
-
84925410541
-
-
arXiv 1409 1556. 1, 2, 5, 6, 7
-
K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv: 1409. 1556, 2014. 1, 2, 5, 6, 7
-
(2014)
Very Deep Convolutional Networks for Large-scale Image Recognition
-
-
Simonyan, K.1
Zisserman, A.2
-
30
-
-
84964474107
-
Grounded compositional semantics for finding and describing images with sentences
-
1, 2, 6, 7, 8
-
R. Socher, Q. V. L. A. Karpathy, C. D. Manning, and A. Y. Ng. Grounded compositional semantics for finding and describing images with sentences. Transactions of the Association for Computational Linguistics, 2: 207-218, 2014. 1, 2, 6, 7, 8
-
(2014)
Transactions of the Association for Computational Linguistics
, vol.2
, pp. 207-218
-
-
Socher, R.1
Karpathy, Q.V.L.A.2
Manning, C.D.3
Ng, A.Y.4
-
32
-
-
84877724347
-
Multimodal learning with deep boltzmann machines
-
1, 2
-
N. Srivastava and R. Salakhutdinov. Multimodal learning with deep boltzmann machines. NIPS, 2012. 1, 2
-
(2012)
NIPS
-
-
Srivastava, N.1
Salakhutdinov, R.2
-
33
-
-
84964983441
-
-
arXiv 1409 4842. 1, 7
-
C. Szegedy, W. Liu, Y. Jia, P. Sermannet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. arXiv: 1409. 4842, 2014. 1, 7
-
(2014)
Going Deeper with Convolutions
-
-
Szegedy, C.1
Liu, W.2
Jia, Y.3
Sermannet, P.4
Reed, S.5
Anguelov, D.6
Erhan, D.7
Vanhoucke, V.8
Rabinovich, A.9
-
34
-
-
84939821075
-
-
arXiv 1411 4556. 2, 6, 7
-
O. Vinyals, A. Toshev, S. Bengio, and D. Erhan. Show and tell: A neural image caption generator. arXiv: 1411. 4556, 2014. 2, 6, 7
-
(2014)
Show and Tell: A Neural Image Caption Generator
-
-
Vinyals, O.1
Toshev, A.2
Bengio, S.3
Erhan, D.4
-
35
-
-
84867117593
-
Wsabie: Scaling up to large vocabulary image annotation
-
2
-
J. Weston, S. Bengio, and N. Usunier. Wsabie: Scaling up to large vocabulary image annotation. IJCAI, 2011. 2
-
(2011)
IJCAI
-
-
Weston, J.1
Bengio, S.2
Usunier, N.3
-
37
-
-
84898772194
-
Learning the visual interpretation of sentences
-
1, 2
-
C. L. Zitnick, D. Parikh, and L. Vanderwende. Learning the visual interpretation of sentences. ICCV, 2013. 1, 2
-
(2013)
ICCV
-
-
Zitnick, C.L.1
Parikh, D.2
Vanderwende, L.3
|