3,303 research outputs found
Simulating Noisy Channel Interaction
We show that rounds of interaction over the binary symmetric channel
with feedback can be simulated with
rounds of interaction over a noiseless channel. We also introduce a more
general "energy cost" model of interaction over a noisy channel. We show energy
cost to be equivalent to external information complexity, which implies that
our simulation results are unlikely to carry over to energy complexity. Our
main technical innovation is a self-reduction from simulating a noisy channel
to simulating a slightly-less-noisy channel, which may have other applications
in the area of interactive compression
Exponential Separation of Quantum Communication and Classical Information
We exhibit a Boolean function for which the quantum communication complexity
is exponentially larger than the classical information complexity. An
exponential separation in the other direction was already known from the work
of Kerenidis et. al. [SICOMP 44, pp. 1550-1572], hence our work implies that
these two complexity measures are incomparable. As classical information
complexity is an upper bound on quantum information complexity, which in turn
is equal to amortized quantum communication complexity, our work implies that a
tight direct sum result for distributional quantum communication complexity
cannot hold. The function we use to present such a separation is the Symmetric
k-ary Pointer Jumping function introduced by Rao and Sinha [ECCC TR15-057],
whose classical communication complexity is exponentially larger than its
classical information complexity. In this paper, we show that the quantum
communication complexity of this function is polynomially equivalent to its
classical communication complexity. The high-level idea behind our proof is
arguably the simplest so far for such an exponential separation between
information and communication, driven by a sequence of round-elimination
arguments, allowing us to simplify further the approach of Rao and Sinha.
As another application of the techniques that we develop, we give a simple
proof for an optimal trade-off between Alice's and Bob's communication while
computing the related Greater-Than function on n bits: say Bob communicates at
most b bits, then Alice must send n/exp(O(b)) bits to Bob. This holds even when
allowing pre-shared entanglement. We also present a classical protocol
achieving this bound.Comment: v1, 36 pages, 3 figure
Balanced Quantization: An Effective and Efficient Approach to Quantized Neural Networks
Quantized Neural Networks (QNNs), which use low bitwidth numbers for
representing parameters and performing computations, have been proposed to
reduce the computation complexity, storage size and memory usage. In QNNs,
parameters and activations are uniformly quantized, such that the
multiplications and additions can be accelerated by bitwise operations.
However, distributions of parameters in Neural Networks are often imbalanced,
such that the uniform quantization determined from extremal values may under
utilize available bitwidth. In this paper, we propose a novel quantization
method that can ensure the balance of distributions of quantized values. Our
method first recursively partitions the parameters by percentiles into balanced
bins, and then applies uniform quantization. We also introduce computationally
cheaper approximations of percentiles to reduce the computation overhead
introduced. Overall, our method improves the prediction accuracies of QNNs
without introducing extra computation during inference, has negligible impact
on training speed, and is applicable to both Convolutional Neural Networks and
Recurrent Neural Networks. Experiments on standard datasets including ImageNet
and Penn Treebank confirm the effectiveness of our method. On ImageNet, the
top-5 error rate of our 4-bit quantized GoogLeNet model is 12.7\%, which is
superior to the state-of-the-arts of QNNs
Privacy-Preserving Shortest Path Computation
Navigation is one of the most popular cloud computing services. But in
virtually all cloud-based navigation systems, the client must reveal her
location and destination to the cloud service provider in order to learn the
fastest route. In this work, we present a cryptographic protocol for navigation
on city streets that provides privacy for both the client's location and the
service provider's routing data. Our key ingredient is a novel method for
compressing the next-hop routing matrices in networks such as city street maps.
Applying our compression method to the map of Los Angeles, for example, we
achieve over tenfold reduction in the representation size. In conjunction with
other cryptographic techniques, this compressed representation results in an
efficient protocol suitable for fully-private real-time navigation on city
streets. We demonstrate the practicality of our protocol by benchmarking it on
real street map data for major cities such as San Francisco and Washington,
D.C.Comment: Extended version of NDSS 2016 pape
Interactive Compression for Multi-Party Protocol
The field of compression studies the question of how many bits of communication are necessary to convey a given piece of data. For one-way communication between a sender and a receiver, the seminal work of Shannon and Huffman showed that the communication required is characterized by the entropy of the data; in recent years, there has been a great amount of interest in extending this line of research to interactive communication, where instead of a sender and a receiver we have two parties communication back-and-forth. In this paper we initiate the study of interactive compression for distributed multi-player protocols. We consider the classical shared blackboard model, where players take turns speaking, and each player\u27s message is immediately seen by all the other players. We show that in the shared blackboard model with k players, one can compress protocols down to ~O(Ik), where I is the information content of the protocol and k is the number of players. We complement this result with an almost matching lower bound of ~Omega(Ik), which shows that a nearly-linear dependence on the number of players cannot be avoided
Construction and Verification of Performance and Reliability Models
Over the last two decades formal methods have been extended towards performance and reliability evaluation. This paper tries to provide a rather intuitive explanation of the basic concepts and features in this area.
Instead of striving for mathematical rigour, the intention is to give an illustrative introduction to the basics of stochastic models, to stochastic modelling using process algebra, and to model checking as a technique to analyse stochastic models
A Candidate for a Strong Separation of Information and Communication
The weak interactive compression conjecture asserts that any two-party communication protocol with communication complexity C and information complexity I can be compressed to a protocol with communication complexity poly(I)polylog(C).
We describe a communication problem that is a candidate for refuting that conjecture. Specifically, while we show that the problem can be solved by a protocol with communication complexity C and information complexity I=polylog(C), the problem seems to be hard for protocols with communication complexity poly(I)polylog(C)=polylog(C)
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