229,697 research outputs found
Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment
Word translation without parallel corpora has become feasible, rivaling the
performance of supervised methods. Recent findings have shown that the accuracy
and robustness of unsupervised word translation (UWT) can be improved by making
use of visual observations, which are universal representations across
languages. In this work, we investigate the potential of using not only visual
observations but also pretrained language-image models for enabling a more
efficient and robust UWT. Specifically, we develop a novel UWT method dubbed
Word Alignment using Language-Image Pretraining (WALIP), which leverages visual
observations via the shared embedding space of images and texts provided by
CLIP models (Radford et al., 2021). WALIP has a two-step procedure. First, we
retrieve word pairs with high confidences of similarity, computed using our
proposed image-based fingerprints, which define the initial pivot for the word
alignment. Second, we apply our robust Procrustes algorithm to estimate the
linear mapping between two embedding spaces, which iteratively corrects and
refines the estimated alignment. Our extensive experiments show that WALIP
improves upon the state-of-the-art performance of bilingual word alignment for
a few language pairs across different word embeddings and displays great
robustness to the dissimilarity of language pairs or training corpora for two
word embeddings.Comment: In Proceedings of the 2022 Conference on Empirical Methods in Natural
Language Processing (EMNLP Findings
Formal Representation of the SS-DB Benchmark and Experimental Evaluation in EXTASCID
Evaluating the performance of scientific data processing systems is a
difficult task considering the plethora of application-specific solutions
available in this landscape and the lack of a generally-accepted benchmark. The
dual structure of scientific data coupled with the complex nature of processing
complicate the evaluation procedure further. SS-DB is the first attempt to
define a general benchmark for complex scientific processing over raw and
derived data. It fails to draw sufficient attention though because of the
ambiguous plain language specification and the extraordinary SciDB results. In
this paper, we remedy the shortcomings of the original SS-DB specification by
providing a formal representation in terms of ArrayQL algebra operators and
ArrayQL/SciQL constructs. These are the first formal representations of the
SS-DB benchmark. Starting from the formal representation, we give a reference
implementation and present benchmark results in EXTASCID, a novel system for
scientific data processing. EXTASCID is complete in providing native support
both for array and relational data and extensible in executing any user code
inside the system by the means of a configurable metaoperator. These features
result in an order of magnitude improvement over SciDB at data loading,
extracting derived data, and operations over derived data.Comment: 32 pages, 3 figure
DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation
In previous works, only parameter weights of ASR models are optimized under
fixed-topology architecture. However, the design of successful model
architecture has always relied on human experience and intuition. Besides, many
hyperparameters related to model architecture need to be manually tuned.
Therefore in this paper, we propose an ASR approach with efficient
gradient-based architecture search, DARTS-ASR. In order to examine the
generalizability of DARTS-ASR, we apply our approach not only on many languages
to perform monolingual ASR, but also on a multilingual ASR setting. Following
previous works, we conducted experiments on a multilingual dataset, IARPA
BABEL. The experiment results show that our approach outperformed the baseline
fixed-topology architecture by 10.2% and 10.0% relative reduction on character
error rates under monolingual and multilingual ASR settings respectively.
Furthermore, we perform some analysis on the searched architectures by
DARTS-ASR.Comment: Accepted at INTERSPEECH 202
Technology assessment of advanced automation for space missions
Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology
A Survey on Array Storage, Query Languages, and Systems
Since scientific investigation is one of the most important providers of
massive amounts of ordered data, there is a renewed interest in array data
processing in the context of Big Data. To the best of our knowledge, a unified
resource that summarizes and analyzes array processing research over its long
existence is currently missing. In this survey, we provide a guide for past,
present, and future research in array processing. The survey is organized along
three main topics. Array storage discusses all the aspects related to array
partitioning into chunks. The identification of a reduced set of array
operators to form the foundation for an array query language is analyzed across
multiple such proposals. Lastly, we survey real systems for array processing.
The result is a thorough survey on array data storage and processing that
should be consulted by anyone interested in this research topic, independent of
experience level. The survey is not complete though. We greatly appreciate
pointers towards any work we might have forgotten to mention.Comment: 44 page
Tiramisu: A Polyhedral Compiler for Expressing Fast and Portable Code
This paper introduces Tiramisu, a polyhedral framework designed to generate
high performance code for multiple platforms including multicores, GPUs, and
distributed machines. Tiramisu introduces a scheduling language with novel
extensions to explicitly manage the complexities that arise when targeting
these systems. The framework is designed for the areas of image processing,
stencils, linear algebra and deep learning. Tiramisu has two main features: it
relies on a flexible representation based on the polyhedral model and it has a
rich scheduling language allowing fine-grained control of optimizations.
Tiramisu uses a four-level intermediate representation that allows full
separation between the algorithms, loop transformations, data layouts, and
communication. This separation simplifies targeting multiple hardware
architectures with the same algorithm. We evaluate Tiramisu by writing a set of
image processing, deep learning, and linear algebra benchmarks and compare them
with state-of-the-art compilers and hand-tuned libraries. We show that Tiramisu
matches or outperforms existing compilers and libraries on different hardware
architectures, including multicore CPUs, GPUs, and distributed machines.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0041
Neural Networks Compression for Language Modeling
In this paper, we consider several compression techniques for the language
modeling problem based on recurrent neural networks (RNNs). It is known that
conventional RNNs, e.g, LSTM-based networks in language modeling, are
characterized with either high space complexity or substantial inference time.
This problem is especially crucial for mobile applications, in which the
constant interaction with the remote server is inappropriate. By using the Penn
Treebank (PTB) dataset we compare pruning, quantization, low-rank
factorization, tensor train decomposition for LSTM networks in terms of model
size and suitability for fast inference.Comment: Keywords: LSTM, RNN, language modeling, low-rank factorization,
pruning, quantization. Published by Springer in the LNCS series, 7th
International Conference on Pattern Recognition and Machine Intelligence,
201
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