Alpha-Beta Bidirectional Associative Memories Based Translator

IEEE Transactions on Neural Networks, 1994

An iterative learning algorithm called PRLAB is described for the discrete Bidirectional Associative Memory (BAM). Guaranteed recall of all training pairs is ensured by PRLAB. The proposed algorithm is significant in many ways. Unlike many existing iterative learning algorithms, PRLAB is not based on the gradient descent technique. It is a novel adaptation from the well-known relaxation method for solving a system of linear inequalities. The algorithm is very fast. Learning 200 random patterns in a 200-200 BAM takes only 20 epochs on the average. PRLAB is highly insensitive to learning parameters and the initial configuration of a BAM. It also offers high scalability for large applications by providing the same high performance when the number of training patterns are increased in proportion to the size of the BAM. An extensive performance analysis of the new learning algorithm is included. P M = c X (k) T y (k) k=l to store a set of vector pairs { (X (k) , Y (k)) } k = l ,. .. , p. The use of such a correlation matrix for storing information in a neural network model is often called the first-order correlation Manuscript

IEEE Transactions on Neural Networks, 2000

In contrast to conventional feedback bidirectional associative Memory (BAM) network models, a feedforward BAM network is developed based on a one-shot design algorithm of (2 (+)) computational complexity, where is the number of prototype pairs and are the dimensions of the input/output bipolar vectors. The feedforward BAM is an-three-layer network of McCulloch-Pitts neurons with storage capacity 2 min and guaranteed perfect bidirectional recall. The overall network design procedure is fully scalable in the sense that any number 2 min of bidirectional associations can be implemented. The prototype patterns may be arbitrarily correlated. With respect to inference performance, it is shown that the Hamming attractive radius of each prototype reaches the maximum possible value. Simulation studies and comparisons illustrate and support these theoretical developments.

1998

Similarity based fault tolerant retrieval in neural associative memories (N AM) has not lead to wiedespread applications. A drawback of the efficient Willshaw model for sparse patterns [Ste61, WBLH69], is that the high asymptotic information capacity is of little practical use because of high cross talk noise arising in the retrieval for finite sizes. Here a new bidirectional iterative retrieval method for the Willshaw model is presented, called crosswise bidirectional (CB) retrieval, providing enhanced performance. We discuss its asymptotic capacity limit, analyze the first step, and compare it in experiments with the Willshaw model. Applying the very efficient CB memory model either in information retrieval systems or as a functional model for reciprocal cortico-cortical pathways requires more than robustness against random noise in the input: Our experiments show also the segmentation ability of CB-retrieval with addresses containing the superposition of pattens, provided even at high memory load.

Babel. Revue internationale de la traduction / International Journal of Translation, 2008

Most of the current Translation Memory systems are based on segments determined by marks that in most cases correspond to a complete sentence. The problem of complete sentence matching is that examples are often excluded from the matching candidates even though they probably contain one or more useful sub-segments that could be helpful to the translation. In view of these limitations, some proposals have been made in the literature regarding the possibility of building Translation Memory systems that operate “below” the sentence level, that is to say, at a sub-sentential level. Existing work demonstrates that sub-sentential segmentation of Translation Memories clearly shows a significantly best recall with respect to sentential.segmentation. Accepting the benefits of sub-sentential segmentation of Translation Memories, in this paper we consider different possibilities of sub sentential segmentation and attempt an evaluation of the recall (covering sequences of chunks) and the precis...

Cognitive Computation and Systems, 2021

Machine-based language translation has been certainly picking up. Still, machines lag behind the cognitive powers of human beings. Neural Machine Translation (NMT) methods require huge datasets and computational power for high-quality translation. A novel Sequential Adaptive Memory (SAM) cognitive model-based machine translation system for English to Hindi translation, was proposed. This model is an augmented version of the Cortical Learning Algorithm (CLA). The SAM is based on the architecture of the neocortex region of the brain, where speech and language comprehension and production take place. The proposed model is capable of learning with smaller datasets. This model employs the sequence to sequence learning approach, which provides better quality translation. It enables the creation of word pairs, dictionaries, and rules for translation. The results of the proposed approach are compared with the traditional phrase-based SMT approach as well as with the state-of-the-art NMT approach. The results are comparable with the results of the conventional approaches. We illustrate that the limitations of the approaches are won over by the proposed SAM approach. It is observed that SAM is capable of exhibiting satisfactory quality translation for low resource languages as well.

Memorization is a fundamental ability of transformer-based Large Language Models, achieved through learning. In this paper, we propose a paradigm shift by designing an architecture to memorize text directly, bearing in mind the principle that memorization precedes learning. We introduce MeMo, a novel architecture for language modeling that explicitly memorizes sequences of tokens in layered associative memories. By design, MeMo offers transparency and the possibility of model editing, including forgetting texts. We experimented with the MeMo architecture, showing the memorization power of the one-layer and the multi-layer configurations.

2019

In this paper we present an algorithm for rule based systems that uses Bidirectional Associative Memories (BAMs) to memorize the inputs and their corresponding outputs, and outputs and their corresponding inputs of the system. Adaptive rule based system are becoming very important because rule based systems are now used in many applications. One drawback of current adaptive rule based systems is that these systems care only about forward chaining mechanism which diminish their performance. Because most of the applications that use rule based systems follow the forward chaining, adaption attempts which is concerned with predicting the inputs given the output is almost null, but this does not eliminate the importance of backward chaining since it is used in too many applications. To tackle this problem we propose a new algorithm that utilizes the good theoretical ground of BAMs to memorize and adapt rules in rule based systems. The main difference between the proposed algorithm and ot...

In this paper we introduce a technique for creating searchable translation memories. Linear B's searchable translation memories allow a translator to type in a phrase and retrieve a ranked list of possible translations for that phrase, which is ordered based on the likelihood of the translations. The searchable translation memories use translation models similar to those used in statistical machine translation. In this paper we first describe the technical details of how the TMs are indexed and how translations are assigned probabilities, and then evaluate a searchable TM using precision and recall metrics.

In this paper a present study focused on analysis of the women getting stress in industries using Bidirectional Associative Memories (BAM) Model. For this research we have collected the data from expert opinion. The three major domains of the problems are Causes of Physical problem, Psychological problem,Behavioral problem for women getting stress in industries is discussed.Section one, gives the introduction of the problem. Basic definitions of Bidirectional Associative Memories (BAM) are given the section two. The third section demonstrates analysis of the problem using BAM Model. Based on present study conclusion and suggestion were given in final section.

Neural Processing Letters, 2017