Publications

Stochastic Multiple Stream Decoding of Cortex Codes
Matthieu Arzel
Cyril Lahuec
Christophe Jego
Warren J. Gross
Yvain Bruned
Being one of the most efficient solutions to implement forward error correction (FEC) decoders based on belief propagation, stochastic proce… (voir plus)ssing is thus a method worthy of consideration when addressing the decoding of emerging codes such as Cortex codes. This code family offers short block codes with large Hamming distances. Unfortunately, their construction introduces many hidden variables making them difficult to be efficiently decoded with digital circuits implementing the Sum-Product algorithm. With the introduction of multiple stochastic streams, the proposed solution alleviates the hidden variables problem thus yielding decoding performances close to optimal. Morevover, this new stochastic architecture is more efficient in terms of complexity-throughput ratio compared to recently published stochastic decoders using either edge or tracking forecast memories.
2011-06-30
IEEE Transactions on Signal Processing (publié)
doi.org
Assessing Generalization in TD methods for Deep Reinforcement Learning
Emmanuel Bengio
Doina Precup
Joelle Pineau
2010-12-31
International Conference on Computational Intelligence, Modelling and Simulation (publié)
openreview.net
openreview.net
A Case Study of Instruction Tuning with Mixture of Parameter-Efficient Experts
O. Ostapenko
Lucas Caccia
Zhan Su
Nicolas Le Roux
Laurent Charlin
Alessandro Sordoni
2010-12-31
International Conference on Computational Intelligence, Modelling and Simulation (publié)
openreview.net
openreview.net
COVI White Paper-Version 1.1
H. Alsdurf
Yoshua Bengio
T. Deleu
Prateek Gupta
Daphne Ippolito
R. Janda
Max Jarvie
Tyler Kolody
S. Krastev
Tegan Maharaj
Robert Obryk
D. Pilat
Valérie Pisano
Benjamin Prud'homme
Meng Qu
Nasim Rahaman
I. Rish
J. Rousseau
Abhinav Sharma
B. Struck … (voir 3 de plus)
Jian Tang
Martin Weiss
Yun William Yu
2010-12-31
International Conference on Computational Intelligence, Modelling and Simulation (publié)
www.semanticscholar.org
Preventing Dimensional Collapse in Contrastive Local Learning with Subsampling
Louis Fournier
Adeetya Patel
Michael Eickenberg
Edouard Oyallon
Eugene Belilovsky
2010-12-31
International Conference on Computational Intelligence, Modelling and Simulation (publié)
openreview.net
openreview.net
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces
Bogdan Mazoure
Thang Doan
Tianyu Li
Vladimir Makarenkov
Joelle Pineau
Doina Precup
Guillaume Rabusseau
We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional… (voir plus) embedding of the policy on a reproducing kernel Hilbert space (RKHS). The usage of RKHS based methods allows us to derive strong theoretical guarantees on the expected return of the reconstructed policy. Such guarantees are typically lacking in black-box models, but are very desirable in tasks requiring stability. We conduct several experiments on classic RL domains. The results confirm that the policies can be robustly embedded in a low-dimensional space while the embedded policy incurs almost no decrease in return.
2010-12-31
International Conference on Computational Intelligence, Modelling and Simulation (publié)
doi.org
arxiv.org
Stochastic Decoding of Turbo Codes
Quang Trung Dong
Matthieu Arzel
Christophe Jego
Warren J. Gross
Stochastic computation is a technique in which operations on probabilities are performed on random bit streams. Stochastic decoding of forwa… (voir plus)rd error-correction (FEC) codes is inspired by this technique. This paper extends the application of the stochastic decoding approach to the families of convolutional codes and turbo codes. It demonstrates that stochastic computation is a promising solution to improve the data throughput of turbo decoders with very simple implementations. Stochastic fully-parallel turbo decoders are shown to achieve the error correction performance of conventional a posteriori probability (APP) decoders. To our knowledge, this is the first stochastic turbo decoder which decodes a state-of-the-art turbo code. Additionally, an innovative systematic technique is proposed to cope with stochastic additions, responsible for the throughput bottleneck.
2010-11-30
IEEE Transactions on Signal Processing (publié)
doi.org
Relaxation Dynamics in Stochastic Iterative Decoders
Saeed Sharifi Tehrani
Chris Winstead
Warren J. Gross
Shie Mannor
Sheryl L. Howard
Vincent C. Gaudet
Stochastic decoding is a recently proposed approach for graph-based iterative error control decoding. We present and investigate three hyste… (voir plus)resis methods for stochastic decoding on graphs with cycles and show their close relationship with the successive relaxation method. Implementation results demonstrate the tradeoff in bit error rate performance with circuit complexity.
2010-10-31
IEEE Transactions on Signal Processing (publié)
doi.org
Greedy Gossip With Eavesdropping
Deniz Ustebay
Boris Oreshkin
Mark J. Coates
Michael G. Rabbat
This paper presents greedy gossip with eavesdropping (GGE), a novel randomized gossip algorithm for distributed computation of the average c… (voir plus)onsensus problem. In gossip algorithms, nodes in the network randomly communicate with their neighbors and exchange information iteratively. The algorithms are simple and decentralized, making them attractive for wireless network applications. In general, gossip algorithms are robust to unreliable wireless conditions and time varying network topologies. In this paper, we introduce GGE and demonstrate that greedy updates lead to rapid convergence. We do not require nodes to have any location information. Instead, greedy updates are made possible by exploiting the broadcast nature of wireless communications. During the operation of GGE, when a node decides to gossip, instead of choosing one of its neighbors at random, it makes a greedy selection, choosing the node which has the value most different from its own. In order to make this selection, nodes need to know their neighbors' values. Therefore, we assume that all transmissions are wireless broadcasts and nodes keep track of their neighbors' values by eavesdropping on their communications. We show that the convergence of GGE is guaranteed for connected network topologies. We also study the rates of convergence and illustrate, through theoretical bounds and numerical simulations, that GGE consistently outperforms randomized gossip and performs comparably to geographic gossip on moderate-sized random geometric graph topologies.
2010-06-30
IEEE Transactions on Signal Processing (publié)
doi.org
arxiv.org
Optimization and Analysis of Distributed Averaging With Short Node Memory
Boris Oreshkin
Mark J. Coates
Michael G. Rabbat
Distributed averaging describes a class of network algorithms for the decentralized computation of aggregate statistics. Initially, each nod… (voir plus)e has a scalar data value, and the goal is to compute the average of these values at every node (the so-called average consensus problem). Nodes iteratively exchange information with their neighbors and perform local updates until the value at every node converges to the initial network average. Much previous work has focused on algorithms where each node maintains and updates a single value; every time an update is performed, the previous value is forgotten. Convergence to the average consensus is achieved asymptotically. The convergence rate is fundamentally limited by network connectivity, and it can be prohibitively slow on topologies such as grids and random geometric graphs, even if the update rules are optimized. In this paper, we provide the first theoretical demonstration that adding a local prediction component to the update rule can significantly improve the convergence rate of distributed averaging algorithms. We focus on the case where the local predictor is a linear combination of the node's current and previous values (i.e., two memory taps), and our update rule computes a combination of the predictor and the usual weighted linear combination of values received from neighboring nodes. We derive the optimal mixing parameter for combining the predictor with the neighbors' values, and conduct a theoretical analysis of the improvement in convergence rate that can be achieved using this acceleration methodology. For a chain topology on N nodes, this leads to a factor of N improvement over standard consensus, and for a two-dimensional grid, our approach achieves a factor of ¿N improvement.
2010-04-30
IEEE Transactions on Signal Processing (publié)
doi.org
arxiv.org
Majority-Based Tracking Forecast Memories for Stochastic LDPC Decoding
Saeed Sharifi Tehrani
Ali Naderi
Guy-Armand Kamendje
Saied Hemati
Shie Mannor
Warren J Gross
2009-12-31
IEEE Transactions on Signal Processing (publié)
doi.org
Theano: A CPU and GPU Math Compiler in Python
James Bergstra
Olivier Breuleux
Frédéric Bastien
Pascal Lamblin
Razvan Pascanu
Guillaume Desjardins
Joseph Turian
David Warde-Farley
Yoshua Bengio
Theano is a compiler for mathematical expressions in Python that combines the convenience of NumPy's syntax with the speed of optimized nati… (voir plus)ve machine language. The user composes mathematical expressions in a high-level description that mimics NumPy's syntax and semantics, while being statically typed and functional (as opposed to imperative). These expressions allow Theano to provide symbolic differentiation. Before performing computation, Theano optimizes the choice of expressions, translates them into C++ (or CUDA for GPU), compiles them into dynamically loaded Python modules, all automatically. Common machine learn- ing algorithms implemented with Theano are from 1:6 to 7:5 faster than competitive alternatives (including those implemented with C/C++, NumPy/SciPy and MATLAB) when compiled for the CPU and between 6:5 and 44 faster when compiled for the GPU. This paper illustrates how to use Theano, outlines the scope of the compiler, provides benchmarks on both CPU and GPU processors, and explains its overall design.
2009-12-31
Proceedings of the Python in Science Conference (publié)
doi.org