Connectionists: Fwd: Post-doc position: Pattern mining for Neural Networks debugging: application to speech recognition (INRIA, France)

[Irina Illina]

> De: "Irina Illina" <irina.illina at loria.fr>
> À: "connectionists" <connectionists at mailman.srv.cs.cmu.edu>, "connectionists"
> <connectionists at cs.cmu.edu>, "parole" <parole at listes.afcp-parole.org>
> Envoyé: Mercredi 28 Août 2019 19:26:44
> Objet: Post-doc position: Pattern mining for Neural Networks debugging:
> application to speech recognition (INRIA, France)

> Post-doc position: Pattern mining for Neural Networks debugging: application to
> speech recognition
> Advisors : Elisa Fromont & Alexandre Termier, IRISA/INRIA RBA – Lacodam team
> (Rennes)

> Irina Illina & Emmanuel Vincent, LORIA/INRIA – Multispeech team (Nancy)
> firstname.lastname at inria.fr

> Location : INRIA RBA, team Lacodam (Rennes)

> Keywords: discriminative pattern mining , neural networks analysis,
> explainability of black
> box models, speech recognition.

> Deadline to apply : September 30th, 2019

> Context:

> Understanding the inner working of deep neural networks (DNN) has attracted a
> lot of attention in the past years [1, 2] and most problems were detected and
> analyzed using visualization techniques [3, 4]. Those techniques help to
> understand what an individual neuron or a layer of neurons are computing. We
> would like to go beyond this by focusing on groups of neurons which are
> commonly highly activated when a network is making wrong predictions on a set
> of examples. In the same line as [1], where the authors theoretically link how
> a training example affects the predictions for a test example using the so
> called “influence functions”, we would like to design a tool to “debug” neural
> networks by identifying, using symbolic data mining methods, (connected) parts
> of the neural network architecture associated with erroneous or uncertain
> outputs.

> In the context of speech recognition, this is especially important. A speech
> recognition system contains two main parts: an acoustic model and a language
> model. Nowadays models are trained with deep neural networks-based algorithms
> (DNN) and use very large learning corpora to train an important number of DNN
> hyperparameters. There are many works to automatically tune these
> hyperparameters. However, this induces a huge computational cost, and does not
> empower the human designers. It would be much more efficient to provide human
> designers with understandable clues about the reasons for the bad performance
> of the system, in order to benefit from their creativity to quickly reach more
> promising regions of the hyperparameter search space.

> Description of the position:

> This position is funded in the context of the HyAIAI “Hybrid Approaches for
> Interpretable AI” INRIA project lab ( [
> https://www.inria.fr/en/research/researchteams/inria-project-labs |
> https://www.inria.fr/en/research/researchteams/inria-project-labs ] ). With
> this position, we would like to go beyond the current common visualization
> techniques that help to understand what an individual neuron or a layer of
> neurons is computing, by focusing on groups of neurons that are commonly highly
> activated when a network is making wrong predictions on a set of examples.
> Tools such as activation maximization [8] can be used to identify such neurons.
> We propose to use discriminative pattern mining , and, to begin with, the
> DiffNorm algorithm [6] in conjunction with the LCM one [7] to identify the
> discriminative activation patterns among the identified neurons.

> The data will be provided by the MULTISPEECH team and will consist of two deep
> architectures as representatives of acoustic and language models [9, 10].
> Furthermore, the training data will be provided, where the model parameters
> ultimately derive from. We will also extend our results by performing
> experiments with supervised and unsupervised learning to compare the features
> learned by these networks and to perform qualitative comparisons of the
> solutions learned by various deep architectures. Identifying “faulty” groups of
> neurons could lead to the decomposition of the DL network into “blocks”
> encompassing several layers. “Faulty” blocks may be the first to be modified in
> the search for a better design.

> The recruited person will benefit from the expertise of the LACODAM team in
> pattern mining and deep learning ( [ https://team.inria.fr/lacodam/ |
> https://team.inria.fr/lacodam/ ] ) and of the expertise of the MULTISPEECH team
> ( [ https://team.inria.fr/multispeech/ | https://team.inria.fr/multispeech/ ] )
> in speech analysis, language processing and deep learning. We would ideally
> like to recruit a 1 year (with possibly one additional year) post-doc with the
> following preferred skills:
> • Some knowledge (interest) about speech recognition
> • Knowledgeable in pattern mining (discriminative pattern mining is a plus)
> • Knowledgeable in machine learning in general and deep learning particular
> • Good programming skills in Python (for Keras and/or Tensor Flow)
> • Very good English (understanding and writing)

> See the INRIA web site for the post-doc page.

> The candidates should send a CV, 2 names of referees and a cover letter to the
> four researchers ( firstname.lastname at inria.fr ) mentioned above. Please
> indicate if you are applying for the post-doc or the PhD position. The selected
> candidates will be interviewed in September for an expected start in
> October-November 2019.

> Bibliography:

> [1] Pang Wei Koh, Percy Liang: Understanding Black-box Predictions via Influence
> Functions. ICML 2017: pp 1885-1894 (best paper).

> [2] Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals:
> Understanding deep learning requires rethinking generalization. ICLR 2017.

> [3] Anh Mai Nguyen, Jason Yosinski, Jeff Clune: Deep neural networks are easily
> fooled: High confidence predictions for unrecognizable images. CVPR 2015: pp
> 427-436.

> [4] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru
> Erhan, Ian Goodfellow, Rob Fergus: Intriguing properties of neural networks.
> ICLR 2014.

> [5] Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, Wenchang Shi:
> Deep Text Classification Can be Fooled. IJCAI 2018: pp 4208-4215.

> [6] Kailash Budhathoki and Jilles Vreeken. The difference and the
> norm—characterising similarities and differences between databases. In Joint
> European Conference on Machine Learning and Knowledge Discovery in Databases,
> pages 206–223. Springer, 2015.

> [7] Takeaki Uno, Tatsuya Asai, Yuzo Uchida, and Hiroki Arimura. Lcm: An
> efficient algorithm for enumerating frequent closed item sets. In Fimi, volume
> 90. Citeseer, 2003.

> --
> Irina Illina

> Associate Professor
> Lorraine University
> LORIA-INRIA
> Multispeech Team
> office C147
> Building C
> 615 rue du Jardin Botanique
> 54600 Villers-les-Nancy Cedex
> Tel:+ 33 3 54 95 84 90

-- 
Irina Illina 

Associate Professor 
Lorraine University 
LORIA-INRIA 
Multispeech Team 
office C147 
Building C 
615 rue du Jardin Botanique 
54600 Villers-les-Nancy Cedex 
Tel:+ 33 3 54 95 84 90 
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