Foto von Rana Ali Amjad

M.Sc. Rana Ali Amjad

Technische Universität München

Lehrstuhl für Nachrichtentechnik (Prof. Kramer)

Postadresse

Postal:
Theresienstr. 90
80333 München

Research Interests

  • Artificial Intelligence/Machine Learning.
  • Information Theory.
  • Physical Layer Security.
  • Channel and Source Coding Theory.
  • Algorithms and Discrete Mathematics.

Education

  • PhD Student at LNT, TUM since January 2014
  • MSCE 2011-2013, TUM
  • BSc Electrical Engineering and Computer Science, University of Engineering and Technology, Lahore, Pakistan

Awards

  • Walter Gademann Prize for best Master thesis in Department of Electrical, Electronic and Computer Engineering at Technical University of Munich, Germany.
  • Achievement Award for outstanding performance in Master in Communication Engineering degree at Technical University of Munich,  Germany.
  • Naeem Shafi Gold Medal for best performance(Communications Major) in Bachelors of Electrical Engineering and Computer Science degree at University of Engineering and Technology, Lahore Pakistan.
  • Academic Color Holder in High School for best performance over the span of 4 years.
  • Gold Medal for best performance in High School State Examinations.

Abschlussarbeiten

Angebotene Abschlussarbeiten

Masterarbeiten

Analysis of Deep Neural Networks using Information Theory

Analysis of Deep Neural Networks using Information Theory

Beschreibung

The aim of this thesis is to take the recently introduced methods for explaining individual predictions of DNNs and adapt them to build statistical methods using information theoretic quantities that can help in understanding the internal functionality of the DNN. This can later be used to improve the performance of the DNN or to reduce the inference complexity by pruning the parts which do not play a significant role in the operation of DNN. 

The work will consist of both theory and experimentation. 

 

Voraussetzungen

- Basic knowledge information theory

- Basic knowledge of DNNs and their operation. 

Betreuer:

Forschungspraxis oder MSCE Forschungspraxis

Analysis of Deep Neural Networks using Information Theory

Analysis of Deep Neural Networks using Information Theory

Beschreibung

The aim of this thesis is to take the recently introduced methods for explaining individual predictions of DNNs and adapt them to build statistical methods using information theoretic quantities that can help in understanding the internal functionality of the DNN. This can later be used to improve the performance of the DNN or to reduce the inference complexity by pruning the parts which do not play a significant role in the operation of DNN. 

The work will consist of both theory and experimentation. 

 

Voraussetzungen

- Basic knowledge information theory

- Basic knowledge of DNNs and their operation. 

Betreuer:

Laufende Abschlussarbeiten

Publikationen

2018

  • Amjad, R. A.: Information Rates and Error Exponents for Probabilistic Amplitude Shaping. Information Theory Workshop (ITW), Nov 2018 mehr…
  • Blöchl, C; Amjad, R.A; Geiger, B.C.: Co-Clustering via information- theoretic Markov aggregation. IEEE Trans. Knowledge and Data Engineering, Jun 2018 mehr… Volltext ( DOI )

2017

  • Geiger, B. C; Amjad, R. A.: Community detection in Shakespeare’s plays. Classic and information-theoretic approaches. Graphen, Karten, Listen, Diagramme etc. pp. - Modellierungen & Visualisierungen von Literatur in der digitalen Analyse, Dec 2017 mehr…
  • Amjad, R. A., Geiger, B. C., Blöchl, C.: Information Theoretic Cost Functions for Markov Aggregation and Clustering. Seminar at Eurecom, France, Oct 2017 mehr…
  • Geiger, B. C. and Amjad, R. A.: Generalized Kullback-Leibler Aggregation of Markov Chains. Workshop on Information and Communication Theory in Control Systems, May 2017 mehr…
  • Amjad, R.A.; Geiger, B.: Mutual Information Based Clustering: Hard or Soft? 18th Joint Workshop on Communications and Coding (JWCC), Mar 2017 mehr…
  • Geiger, B. C.; Amjad, R. A.: Mutual Information-Based Clustering: Hard or Soft? Proc. of 11th ITG Conf. on Systems, Communication and Coding (SCC) (ITG-Fachbericht 268), VDE, Feb 2017, 1-6 mehr…

2016

  • Amjad, Rana Ali: Variable-to-Fixed Length Resolution Codes for Approximate Random Number Generation. XV International Symposium on Problems of Redundancy in Information and Control Systems, Sep 2016 mehr…
  • Amjad, Rana Ali: Error Exponent and Achievable Rates for Probabilistic Amplitude Shaping. LNT Tagung, Aug 2016 mehr…
  • Geiger, B. C. and Amjad, R. A.: Hard Clusters Maximize Mutual Information – Some Results and an Open Problem. Internal LNT Workshop, Aug 2016 mehr…
  • Amjad, Rana Ali: Information Theoretic Clustering. European School of Information Theory (ESIT), Apr 2016 mehr…

2015

  • Amjad, R.A.; Kramer, G.: Channel resolvability codes based on concatenation and sparse linear coding. IEEE Int. Symp. Inf. Theory (ISIT), Jun 2015 mehr…
  • Amjad, Rana Ali: Sparse and Concatenated Codes for Channel Resolvability. Summer School on Information Processing for Large Networks, Jun 2015 mehr…
  • Amjad, Rana Ali: Low Complexity Codes for Channel Resolvability. Euopean School of Information Theory (ESIT), Apr 2015 mehr…
  • Amjad, Rana Ali: Coding for Channel Intrinsic Random Extraction. 17th Joint Conference on Communications and Coding (JCCC), Mar 2015 mehr…
  • Amjad, Rana Ali: Algorithms for Distribution Matching and Resolution Coding. SP Coding and Information School, Jan 2015 mehr…

2014

  • Amjad, Rana Ali: A Learning Perspective of Context Tree Weighting. Machine Learning in Communication, Sep 2014 mehr…
  • Böcherer, G.; Amjad, R. A.: Informational Divergence and Entropy Rate on Rooted Trees with Probabilities. IEEE Int. Symp. Inf. Theory (ISIT), Jun 2014 mehr…
  • Amjad, Rana Ali: Coding Theorems and Algorithms for Simulation of Discrete Memoryless Sources. Euopean School of Information Theory (ESIT), Apr 2014 mehr…

2013

  • Böcherer, G.; Amjad, R. A.: Fixed-to-Variable Length Resolution Coding for Target Distributions. IEEE Information Theory Workshop (ITW), Sep 2013 mehr…
  • Amjad, R. A.; Böcherer, G.: Fixed-to-Variable Length Distribution Matching. IEEE Int. Symp. Inf. Theory(ISIT), Jul 2013 mehr…
  • Bai, Q; Amjad, R. A.; Nossek, J.A.: Average Throughput Maximization for Energy Harvesting Transmitters with Causal Energy Arrival Information. IEEE Wireless Communications and Networking Conference, IEEE, 2013Shanghai, P.R. China, 4232-4237 mehr…

2012

  • Khalid, Farhan Bin; Amjad, Rana Ali; Chohan, M.A.; Khizar, Muhammad M.: FPGA based real- time signal processor for Pulse Doppler Radar. Conference on Informatics, Electronics and Vision (ICIEV), May 2012 mehr…

Master Thesis