82,549 research outputs found
Human-centered compression for efficient text input
Traditional methods for efficient text entry are based on prediction. Prediction requires a constant context-shift between entering text and selecting or verifying the predictions. Previous research has shown that the advantages offered by prediction are usually eliminated by the cognitive load associated with such context-switching. We present a novel approach that relies on compression. Users are required to compress text using a very simple abbreviation technique that yields an average keystrok reduction of 26.4%. Input text is automatically decoded using weighted finite-state transducers, incorporating both word-based and letter-based n-gram language models. Decoding yields a residual error rate of 3.3%. User experiments show that this approach yields improved text input speeds
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Abbreviated text input using language modeling.
We address the problem of improving the efficiency of natural language text input under degraded conditions (for instance, on mobile computing devices or by disabled users), by taking advantage of the informational redundancy in natural language. Previous approaches to this problem have been based on the idea of prediction of the text, but these require the user to take overt action to verify or select the system’s predictions. We propose taking advantage of the duality between prediction and compression. We allow the
user to enter text in compressed form, in particular, using a simple stipulated abbreviation method that reduces characters by 26.4%, yet is simple enough that it can be learned
easily and generated relatively fluently. We decode the abbreviated text using a statistical generative model of abbreviation, with a residual word error rate of 3.3%. The chief
component of this model is an n-gram language model. Because the system’s operation is
completely independent from the user’s, the overhead from cognitive task switching and
attending to the system’s actions online is eliminated, opening up the possibility that
the compression-based method can achieve text input efficiency improvements where the
prediction-based methods have not. We report the results of a user study evaluating this
method.Engineering and Applied Science
LLMZip: Lossless Text Compression using Large Language Models
We provide new estimates of an asymptotic upper bound on the entropy of
English using the large language model LLaMA-7B as a predictor for the next
token given a window of past tokens. This estimate is significantly smaller
than currently available estimates in \cite{cover1978convergent},
\cite{lutati2023focus}. A natural byproduct is an algorithm for lossless
compression of English text which combines the prediction from the large
language model with a lossless compression scheme. Preliminary results from
limited experiments suggest that our scheme outperforms state-of-the-art text
compression schemes such as BSC, ZPAQ, and paq8h.Comment: 7 pages, 4 figures, 4 tables, preprint, added results on using LLMs
with arithmetic codin
Empirical analysis of BWT-based lossless image compression
The Burrows-Wheeler Transformation (BWT) is a text transformation algorithm originally designed to improve the coherence in text data. This coherence can be exploited by compression algorithms such as run-length encoding or arithmetic coding. However, there is still a debate on its performance on images. Motivated by a theoretical analysis of the performance of BWT and MTF, we perform a detailed empirical study on the role of MTF in compressing images with the BWT. This research studies the compression performance of BWT on digital images using different predictors and context partitions. The major interest of the research is in finding efficient ways to make BWT suitable for lossless image compression.;This research studied three different approaches to improve the compression of image data by BWT. First, the idea of preprocessing the image data before sending it to the BWT compression scheme is studied by using different mapping and prediction schemes. Second, different variations of MTF were investigated to see which one works best for Image compression with BWT. Third, the concept of context partitioning for BWT output before it is forwarded to the next stage in the compression scheme.;For lossless image compression, this thesis proposes the removal of the MTF stage from the BWT compression pipeline and the usage of context partitioning method. The compression performance is further improved by using MED predictor on the image data along with the 8-bit mapping of the prediction residuals before it is processed by BWT.;This thesis proposes two schemes for BWT-based image coding, namely BLIC and BLICx, the later being based on the context-ordering property of the BWT. Our methods outperformed other text compression algorithms such as PPM, GZIP, direct BWT, and WinZip in compressing images. Final results showed that our methods performed better than the state of the art lossless image compression algorithms, such as JPEG-LS, JPEG2000, CALIC, EDP and PPAM on the natural images
On Prediction Using Variable Order Markov Models
This paper is concerned with algorithms for prediction of discrete sequences
over a finite alphabet, using variable order Markov models. The class of such
algorithms is large and in principle includes any lossless compression
algorithm. We focus on six prominent prediction algorithms, including Context
Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic
Suffix Trees (PSTs). We discuss the properties of these algorithms and compare
their performance using real life sequences from three domains: proteins,
English text and music pieces. The comparison is made with respect to
prediction quality as measured by the average log-loss. We also compare
classification algorithms based on these predictors with respect to a number of
large protein classification tasks. Our results indicate that a "decomposed"
CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in
sequence prediction tasks. Somewhat surprisingly, a different algorithm, which
is a modification of the Lempel-Ziv compression algorithm, significantly
outperforms all algorithms on the protein classification problems
From Imitation to Prediction, Data Compression vs Recurrent Neural Networks for Natural Language Processing
In recent studies [1][13][12] Recurrent Neural Networks were used for
generative processes and their surprising performance can be explained by their
ability to create good predictions. In addition, data compression is also based
on predictions. What the problem comes down to is whether a data compressor
could be used to perform as well as recurrent neural networks in natural
language processing tasks. If this is possible,then the problem comes down to
determining if a compression algorithm is even more intelligent than a neural
network in specific tasks related to human language. In our journey we
discovered what we think is the fundamental difference between a Data
Compression Algorithm and a Recurrent Neural Network
Caveats for information bottleneck in deterministic scenarios
Information bottleneck (IB) is a method for extracting information from one
random variable that is relevant for predicting another random variable
. To do so, IB identifies an intermediate "bottleneck" variable that has
low mutual information and high mutual information . The "IB
curve" characterizes the set of bottleneck variables that achieve maximal
for a given , and is typically explored by maximizing the "IB
Lagrangian", . In some cases, is a deterministic
function of , including many classification problems in supervised learning
where the output class is a deterministic function of the input . We
demonstrate three caveats when using IB in any situation where is a
deterministic function of : (1) the IB curve cannot be recovered by
maximizing the IB Lagrangian for different values of ; (2) there are
"uninteresting" trivial solutions at all points of the IB curve; and (3) for
multi-layer classifiers that achieve low prediction error, different layers
cannot exhibit a strict trade-off between compression and prediction, contrary
to a recent proposal. We also show that when is a small perturbation away
from being a deterministic function of , these three caveats arise in an
approximate way. To address problem (1), we propose a functional that, unlike
the IB Lagrangian, can recover the IB curve in all cases. We demonstrate the
three caveats on the MNIST dataset
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