325,099 research outputs found
Information Networks with in-Block Memory
A class of channels is introduced for which there is memory inside blocks of
a specified length and no memory across the blocks. The multi-user model is
called an information network with in-block memory (NiBM). It is shown that
block-fading channels, channels with state known causally at the encoder, and
relay networks with delays are NiBMs. A cut-set bound is developed for NiBMs
that unifies, strengthens, and generalizes existing cut bounds for discrete
memoryless networks. The bound gives new finite-letter capacity expressions for
several classes of networks including point-to-point channels, and certain
multiaccess, broadcast, and relay channels. Cardinality bounds on the random
coding alphabets are developed that improve on existing bounds for channels
with action-dependent state available causally at the encoder and for relays
without delay. Finally, quantize-forward network coding is shown to achieve
rates within an additive gap of the new cut-set bound for linear, additive,
Gaussian noise channels, symmetric power constraints, and a multicast session.Comment: Paper to appear in the IEEE Transactions on Information Theor
Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition
Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term
Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved
promising performance in sequential data modeling. The hidden layers in RNNs
can be regarded as the memory units, which are helpful in storing information
in sequential contexts. However, when dealing with high dimensional input data,
such as video and text, the input-to-hidden linear transformation in RNNs
brings high memory usage and huge computational cost. This makes the training
of RNNs unscalable and difficult. To address this challenge, we propose a novel
compact LSTM model, named as TR-LSTM, by utilizing the low-rank tensor ring
decomposition (TRD) to reformulate the input-to-hidden transformation. Compared
with other tensor decomposition methods, TR-LSTM is more stable. In addition,
TR-LSTM can complete an end-to-end training and also provide a fundamental
building block for RNNs in handling large input data. Experiments on real-world
action recognition datasets have demonstrated the promising performance of the
proposed TR-LSTM compared with the tensor train LSTM and other state-of-the-art
competitors.Comment: 9 page
Survey and Benchmark of Block Ciphers for Wireless Sensor Networks
Cryptographic algorithms play an important role in the security architecture of wireless sensor networks (WSNs). Choosing the most storage- and energy-efficient block cipher is essential, due to the facts that these networks are meant to operate without human intervention for a long period of time with little energy supply, and that available storage is scarce on these sensor nodes. However, to our knowledge, no systematic work has been done in this area so far.We construct an evaluation framework in which we first identify the candidates of block ciphers suitable for WSNs, based on existing literature and authoritative recommendations. For evaluating and assessing these candidates, we not only consider the security properties but also the storage- and energy-efficiency of the candidates. Finally, based on the evaluation results, we select the most suitable ciphers for WSNs, namely Skipjack, MISTY1, and Rijndael, depending on the combination of available memory and required security (energy efficiency being implicit). In terms of operation mode, we recommend Output Feedback Mode for pairwise links but Cipher Block Chaining for group communications
Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks
Deep networks consume a large amount of memory by their nature. A natural
question arises can we reduce that memory requirement whilst maintaining
performance. In particular, in this work we address the problem of memory
efficient learning for multiple tasks. To this end, we propose a novel network
architecture producing multiple networks of different configurations, termed
deep virtual networks (DVNs), for different tasks. Each DVN is specialized for
a single task and structured hierarchically. The hierarchical structure, which
contains multiple levels of hierarchy corresponding to different numbers of
parameters, enables multiple inference for different memory budgets. The
building block of a deep virtual network is based on a disjoint collection of
parameters of a network, which we call a unit. The lowest level of hierarchy in
a deep virtual network is a unit, and higher levels of hierarchy contain lower
levels' units and other additional units. Given a budget on the number of
parameters, a different level of a deep virtual network can be chosen to
perform the task. A unit can be shared by different DVNs, allowing multiple
DVNs in a single network. In addition, shared units provide assistance to the
target task with additional knowledge learned from another tasks. This
cooperative configuration of DVNs makes it possible to handle different tasks
in a memory-aware manner. Our experiments show that the proposed method
outperforms existing approaches for multiple tasks. Notably, ours is more
efficient than others as it allows memory-aware inference for all tasks.Comment: CVPR 201
Bi-directional block self-attention for fast and memory-efficient sequence modeling
© Learning Representations, ICLR 2018 - Conference Track Proceedings.All right reserved. Recurrent neural networks (RNN), convolutional neural networks (CNN) and self-attention networks (SAN) are commonly used to produce context-aware representations. RNN can capture long-range dependency but is hard to parallelize and not time-efficient. CNN focuses on local dependency but does not perform well on some tasks. SAN can model both such dependencies via highly parallelizable computation, but memory requirement grows rapidly in line with sequence length. In this paper, we propose a model, called “bi-directional block self-attention network (Bi-BloSAN)”, for RNN/CNN-free sequence encoding. It requires as little memory as RNN but with all the merits of SAN. Bi-BloSAN splits the entire sequence into blocks, and applies an intra-block SAN to each block for modeling local context, then applies an inter-block SAN to the outputs for all blocks to capture long-range dependency. Thus, each SAN only needs to process a short sequence, and only a small amount of memory is required. Additionally, we use feature-level attention to handle the variation of contexts around the same word, and use forward/backward masks to encode temporal order information. On nine benchmark datasets for different NLP tasks, Bi-BloSAN achieves or improves upon state-of-the-art accuracy, and shows better efficiency-memory trade-off than existing RNN/CNN/SAN
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